table_white $15.000.000$

table_white Output Nodes
table_white $M$
table_white Number of Validation Set
table_white $5.000.000$

table_white Hidden Layer Activation
table_white ReLU
table_white Epoch
table_white $50$

table_white Output Layer Activation
table_white Softmax
table_white BPSK Hidden Nodes
table_white $128$-$64$-$32$

table_white Loss Function
table_white Cross-Entropy
table_white QPSK Hidden Nodes
table_white $256$-$128$-$64$

table_white Optimizer
table_white SGD
table_white $16$-QAM Hidden Nodes
table_white $512$-$256$-$128$

As shown in Fig. <ref>, the block-DNN detector consists of two stages: FVG and feed-forward DNN. In the first stage, the FVG generates the input feature vector $\mathbf{d}_{i}$ of the feed-forward DNN by processing the raw data composed of the received signal vector $\mathbf{y}$ and the channel matrix $\mathbf{H}_{I}$ corresponding to the $i^{\text{th}}$ legitimate TAC. As described in [130], the selected FVG type significantly impacts model performance, and the joint FVG results in the best performance among the other FVG types for the model suggested in [130]. Hence, the authors of [49] explore the influence of FVG type on detection reliability and evaluate the BER performance of the block-DNN detector for three FVG kinds described in [130]: separate FVG, joint FVG, and conventional FVG. Combining the feature vectors of all legitimate TACs, the FVG creates the feature matrix $\mathbf{D}$ and feeds it to the feed-forward DNN. In the second stage, the DNN yields a $\hat{\mathbf{s}}_{I}$ for each $\mathbf{d}_{i}$. The feed-forward DNN contains $N_{p}$ sub-DNNs to estimate the QAM symbol corresponding to each active transmit antenna. Finally, the block-DNN detector employs (<ref>) to obtain the selected TAC and QAM symbols. We reproduce some of the figures in [49] using the network and training parameters given in Table <ref> and share some portion of example codes. The training procedure of any block-DNN detector ignores the GSM scheme at the transmitter, assuming all transmit antennas are active, which makes sense since DNN is only responsible for detecting QAM symbols given a TAC. In addition, the received signal does not experience AWGN during the training. Here is an example model creation of a DNN with separate FVG for QPSK transmission, which is another version of model definition in Keras different than what we provided in Section II-B:

from tensorflow.keras import Input, Model
from tensorflow.keras.layers import  Dense, BatchNormalization
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.regularizers import l2
y_lst, l_lst, m_lst = [], [], []
M, Nt, Np, Nr = 4, 2, 2, 2
n_x, n_y = 2 * Nr + 2 * Nr * Np, M
x = Input(shape=(n_x,))
for i in range(Np):
h1 = Dense(256, kernel_regularizer=l2(l=0.001), activation="relu", name="D" + str(i) + "1")(x)
b1 = BatchNormalization(name="BN" + str(i) + "1")(h1)
h2 = Dense(128, kernel_regularizer=l2(l=0.001), activation="relu", name="D" + str(i) + "2")(b1)
b2 = BatchNormalization(name="BN" + str(i) + "2")(h2)
h3 = Dense(64, kernel_regularizer=l2(l=0.001), activation="relu", name="D" + str(i) + "3")(b2)
b3 = BatchNormalization(name="BN" + str(i) + "3")(h3)
y = Dense(n_y, kernel_regularizer=l2(l=0.001), activation="softmax", name="O" + str(i))(b3)
model = Model(inputs=x, outputs=y_lst)
SGD_opt = SGD(lr=0.005, nesterov=True)
model.compile(optimizer=SGD_opt, loss=l_lst, metrics=m_lst)

BER comparisons for the B-DNN detector (a) Conventional detectors vs B-DNN detector, (b) B-DNN detector with various FVG types, (c) B-DNN detector under various modulation levels.

Here, we create a chain of layers instead of using the Sequential module for each active transmit antenna by a for loop, and combine them to a single Model. The BatchNormalization and l2 modules represent the batch normalization layer and l2 regularizer, respectively. The next step is defining PHY parameters prior to constructing the training data:

import WirelessCommLib as wcl
N_tot = wcl.Combination(Nt, Np)
ns = int(np.floor(np.log2(N_tot)))
m = int(np.log2(M))
ni = Np * m
n_tot = ns + ni
N = 2 ** ns
is_normalized = True
ss = wcl.Constellation(M, mod_type, is_normalized)
TAC_set = wcl.OptimalTAC_Set(Nt, Np, N)

The WirelessCommLib is our package for PHY-related tasks, in which Combination(a, b) function corresponds to the number of picking unordered b items out of a items, Constellation returns a symbol set given a modulation level $M$ and modulation type, like PSK or QAM. OptimalTAC_Set creates the set of legitimate TACs from all possible TACs. Optimality indicates that the TAC set is not created by randomly picking some of the TACs, instead TAC set is created so that the number of each transmit antenna in the set is either equal or close to each other. Now, we can construct the input training data:

Ns = 15000000
FVG_type = "SFVG"
bit_matrix = np.random.randint(2, size=(n_tot, Ns))
for j in range(Ns):
bit_array = bit_matrix[:, j]
x = wcl.EncodeBits(bit_array, ss, TAC_set, ns, m, Nt, Np)
H = wcl.Channel([Nr, Nt])
y = np.matmul(H, x)
train_input_data[j, :] = np.concatenate((wcl.FVG(y, FVG_type), wcl.FVG(H, FVG_type)))[:, 0]

The number of training instances is equal to $N_{s}$. For input of each training data, we first randomly generate a bit array and encode these bits into a transmit vector $\mathbf{x}$ by using EncodeBits function. Subsequently, we randomly generate a i.i.d. channel matrix $\mathbf{H}$ whose elements follow the complex Gaussian distribution $\mathcal{CN}(0, 1)$ via Channel function, and multiply it with $\mathbf{x}$. The input data is the concatenation of the feature vectors of the received signal vector $\mathbf{y}$ and the channel matrix $\mathbf{H}$. The labels for this training data are the correct symbols transmitted from all antennas:

train_output_data = []
for i in range(Np):
labels = np.zeros((Ns, M))
for j in range(Ns):
start = ns + i * m
stop = ns + (i + 1) * m
bits = bit_matrix[start : stop, j]
labels[j, wcl.Bin2Dec(bits)] = 1

Here, Bin2Dec function converts the given bit array to the corresponding symbol. Once the training data and the model is ready, we can train the model:

model.fit(train_input_data, train_output_data, validation_split=0.25, batch_size=512, epochs=50, shuffle=True)

The codes, trained models, simulations for testing the B-DNN model and comparisons with the conventional linear detectors and MLD, as well as the generated figures are available in our online database[https://github.com/burakozpoyraz/Block-DNN]. Fig. <ref>(a) illustrates that the proposed block-DNN detector outperforms the conventional block-linear detectors and performs similar to the optimal MLD detection assuming $N_{t} = 4$, $N_{r} = 2$, $N_{p} = 2$, and QPSK modulation. It should be noted that, compared to MLD, the proposed B-DNN model can achieve similar results with less time complexity. Fig <ref>(b) shows the impact of FVG type on the BER performance of B-DNN detector under the same setting with the former except BPSK modulation. In contrast to the model of [130], where joint FVG produces the best performance, the block-DNN detector that employs separate FVG delivers the best BER performance, while the other FVG types cause unreliable communication with a BER value of almost 0.5 across all SNR levels. Thus, the appropriate FVG type might vary depending on the application, necessitating thorough examination during the validation step. Finally, Fig. <ref>(c) provides the BER performance of the block-DNN detector under various modulation levels, assuming the same antenna settings as before. The block-DNN with BPSK obtains the best BER performance, while 16-QAM performs the worst, as expected. As a trade-off, increasing the modulation order provides higher bit rates.

With this striking implementation, we may now end this part and go on to the following section's examination of DL methods for MC waveforms.

§ DL FOR MULTICARRIER WAVEFORM DESIGN

Waveform, which characterizes the physical shape of information-carrying signals, is one of the key components of wireless communication systems [20]. Existing waveforms can be categorized into two groups as single-carrier (SC) and MC waveforms. In order to increase the data rate in a SC or MC system, symbol duration needs to be decreased or equivalently bandwidth occupied for data transmission should be increased.  However, with the adoption of wider bandwidth, SC waveforms suffer from frequency-selective fading that causes inter-symbol interference (ISI) since symbol duration is less than the delay spread of the wireless channel.  Since complex equalization techniques should be applied to reduce ISI, it is very challenging to provide high data rates with the existing SC waveforms. In an MC system, the frequency band is divided into many sub-bands which are also known as subcarriers [142]. These subcarriers are employed in parallel to convey information bits simultaneously. The spacing between two consecutive subcarriers is selected such that each of them undergoes flat fading in the frequency domain. Hence, a simple single-tap equalizer may be utilized to remove the channel effect in MC systems. Consequently, MC techniques enable wideband transmission to convey information; therefore, they yield higher data rates than SC techniques.

OFDM appears as the most popular MC waveform and has been used in numerous standards such as LTE and the IEEE 802.11 family due to its simple and effective structure. Owing to the overlapped orthogonal subcarriers, OFDM is capable of using the spectrum efficiently. Moreover, the time frequency grid of OFDM allows the flexible use of resource elements. In conventional OFDM systems, modulated data symbols for each data subcarrier are determined in the frequency domain by mapping information bits to the PSK/QAM constellation. A certain number of subcarriers are allocated for the transmission of pilot symbols in order to perform channel estimation at the receiver side. These pilot symbols can be inserted into the OFDM symbol with block or comb type methods [143]. Channel coefficients can be estimated in the time-domain or frequency-domain and numerous estimation techniques, which provide different performance and complexity, have been proposed for these two domains [144, 145]. In the frequency domain, the channel frequency response is estimated by exploiting pilot symbols and is interpolated to obtain the channel frequency response over data symbols. For example, the least squares (LS) and the linear minimum mean error square (lmmse) are two well-known frequency domain channel estimation methods. After inserting pilot symbols, the time domain OFDM signal is obtained by employing the inverse fast Fourier transform (ifft). Furthermore, a CP is embedded in the beginning or end of the OFDM symbol to eliminate ISI. Additionally, the CP allows us to model the frequency selective channel as circular convolution. After adding a CP, the OFDM signal is transmitted through the wireless communication channel. At the receiver side, the CP is removed and the frequency domain OFDM signal is acquired by taking FFT. Channel estimation is performed and equalization is applied to remove the effect of the wireless channel. Finally, the signal is demodulated to obtain information.

Although OFDM has several advantages as discussed above, it also has many drawbacks such as high PAPR, sensitivity to frequency and timing errors, high out-of-band emissions (oobe), and CP&pilot overhead. After IFFT operation, the subcarriers are randomly summed up in the time domain and this may cause high peaks due to the overlapping of the peak amplitudes of different signals. The power amplifier at the transmitter works in the nonlinear region due to these high peaks, generating distortion and spectral dispersion. These peaks give rise to high PAPR that reduces the efficiency of the power amplifier, analog-to-digital, and digital-to-analog converters. Therefore, researchers have designed various PAPR reduction techniques for OFDM transmission systems [146]. Another drawback of OFDM is sensitivity to intercarrier interference (ici) caused by high mobility, phase noise, timing offset, and carrier frequency offset (CFO). The channel in a mobile wireless environment rapidly changes with respect to time. This places a strain on the receiver since it must precisely estimate the channel prior to coherent detection. The most critical consequence of this time variation is the distortion of the orthogonality between subcarriers resulting in ICI, whose intensity is determined by the amount of channel time variation. Furthermore, ICI may also occur due to the frequency difference between the local oscillators of the transmitter and receiver. Since ICI disrupts OFDM subcarriers, data detection becomes less accurate, and a single tap equalizer can not provide satisfactory performance. Thus, more complicated methods are required to decode OFDM symbols. To combat this difficult interference mitigation problem under high mobility environments or in the presence of CFO, several decoding algorithms have been proposed [147]. Another important concern with OFDM systems is their excessive out-of-band emissions (OOBE) that should be diminished to prevent adjacent channel interference. The OFDM signal has a rectangular pulse in the time domain, which generates a sinc signal in the frequency domain. The sidelobes of the sinc signals at the edge carriers bring tremendous interference; therefore, they should be minimized. OOBE decreases through various windowing/filtering methods together with guard band allocation to fulfill the spectrum mask criteria of the different standards. Nonetheless, spectral efficiency degrades due to the fixed guard band allocation. Lastly, in OFDM systems, inserting CP and pilot symbols is highly indispensable. However, the spectral efficiency reduces because of reserving a considerable number of resources for CP and pilot overhead.

In recent years, many different and attractive OFDM-based MC waveforms have been designed to alleviate the drawbacks of OFDM. Windowed-OFDM (w-ofdm) has been proposed to reduce OOBE by applying windowing operation to classical OFDM. Filter bank MC (fbmc) [148], which implements subcarrier-wise filtering operation, has been introduced. FBMC can use the spectrum more efficiently and is more resilient under high mobility compared to OFDM in the expense of additional signal processing. Generalized frequency division multiplexing (gfdm) [149], which also performs filtering at the subcarrier level like FBMC, appears as a remarkably flexible waveform since it does not have to meet the orthogonality requirement. Another engaging waveform is universal filtered MC (ufmc) that applies filtering to subbands instead of subcarriers [150]. UFMC needs less redundancy in comparison to FBMC; however, it is not applicable for very high data rates. As mentioned previously, since the orthogonality of its subcarriers is disrupted, OFDM is highly susceptible to high Doppler which leads to ICI. Orthogonal time frequency space (otfs) modulation appears as a promising waveform that brings a clever solution to this disadvantage of OFDM [151]. Even in high-Doppler channels, OTFS assures that each transmitted symbol has a near-constant channel gain by converting the time-varying multipath channel into a two-dimensional delay-Doppler channel. Finally, OFDM with IM (ofdm-im) emerges as a promising OFDM-based waveform that changes only the modulation/demodulation processes of plain OFDM system and offers an additional degree of freedom for waveform design thanks to the flexibility of IM [152]. It is clear that all waveforms mentioned above have both some advantages and disadvantages over OFDM. Because of its simple structure, OFDM was chosen for also 5G networks, and we believe that it will maintain its importance for future wireless systems.

Against this background, DL emerges as an appealing tool to solve challenging problems in designing MC transmission systems. For instance, DL can be used to improve the subblocks of MC systems such as channel estimation and symbol detection or to optimize the whole transmission and reception pipeline jointly. In literature, a vast number of interesting DL-based methods are developed to enhance the performance of existing MC schemes. Under the subsection of transceiver design, as seen from Fig. <ref>, we present both DL-based receiver architectures and also joint designs of transmitter and receiver. Then, in the second subsection, DL-based techniques, which address the shortcomings of OFDM, are investigated. These techniques provide significant improvements that encourage us to believe in the feasibility of DL-based solutions for future MC systems.

DL applications in MC systems.

§.§ Transceiver Design

In this subsection, an overview of the studies in the literature review on DL-based receiver and transceiver designs, that is the studies that focus on the joint optimization of both transmitter and receiver structures, is provided. For the receiver design, most studies mainly focus on the implementation of DL-based techniques for channel estimation and symbol detection. For the transceiver design, the processing blocks at the transmitter and receiver are devised together by using AE-based approaches to optimize the system as a whole.

DL has been implemented for channel estimation and symbol detection in an OFDM system for the first time in [153]. In this study, DNNs have been shown to be capable of learning and evaluating wireless channel properties. The proposed DNN architecture, which is trained with the simulated OFDM symbols, has been designed as data-driven and fully-connected. When the number of pilots is reduced or CP is completely removed, the proposed DNN-based receiver outperforms the traditional algorithms such as LS and MMSE in terms of error performance. Moreover, the DNN-based receiver provides better error performance than LS and MMSE in the presence of clipping noise. In [154], a low complexity DNN called SimNet with simple architecture and shorter training has been designed to perform channel estimation. A meta-learning-based NN called robust channel estimation with meta NNs, RoemNet, is also introduced [155].  Since it leverages meta-learner, RoemNet can tackle new channel learning problems with a minimal number of pilots. DeepRx, which improves the channel estimation and symbol detection performance significantly by adjusting the inputs of NN in a clever way, is another DL-based data-driven approach [156]. Since it supports 5G frame structure including different modulation schemes or pilot configurations, DeepRx is compatible with 5G communication systems. The output of DeepRx has been determined such that various QAM schemes can be supported by training only a single NN. The authors investigated both coded and uncoded error performance of DeepRx and compared it to the well-known LMMSE receiver. It is indicated that DeepRx outperforms LMMSE for different pilot configurations. Additionally, DeepRx has superiority over LMMSE in the presence of Doppler shift and inter-cell interference. The aforementioned NNs work with real-valued tensors and this can cause DNN to have more complexity and a decrease in their performance. In [157], a deep complex-valued convolutional network, that works in complex fields and decodes bits from time domain OFDM signals without requiring any IFFT/FFT operation, has been proposed.

A model-driven approach called ComNet [158], which designs two different DNNs for channel estimation and symbol detection blocks, has been proposed instead of performing channel estimation and symbol detection jointly in [153]. These two DNNs are initialized by conventional wireless communication solutions. Thanks to the benefit of expert knowledge, it becomes possible to reduce the need for training data and converge faster with the model-driven method compared to data driven one [153]. Additionally, simulation results have shown that ComNet outperforms rival channel estimation and symbol detection algorithms such as LMMSE channel estimation, fully-connected DNN [153], MMSE symbol detection. The mismatch between the channel model for offline training and the real environment, which causes a performance gap between the simulation and the over-the-air test, is detected in [159]. To overcome this problem, a novel online training system called SwitchNet receiver is designed to catch channel characteristics that were neglected during offline training. SwitchNet pretrains multiple channel estimation RefineNet of ComNet with diverse channel conditions and selects the network by online learning. Another model-driven approach is DeepWiPHY [160], a DL-based OFDM receiver that is completely compatible with the newest Wi-Fi standard IEEE 802.11ax. In contrast to above mentioned designs, DeepWiPHY receiver is trained with not only a synthetic data set but also a real-world data set created by using universal software radio peripheral (usrp) modules. Finally, a model-driven technique, that divides channel estimation into three CNN-based networks, is also proposed [161].

Representation of the time-frequency grid as a 2D image [162].

Authors of [163, 162, 164, 165, 166, 167, 168, 169, 170] have considered the time frequency response of wireless channel as a 2D image as in Fig. <ref> where LR and HR stand for low resolution and high resolution, respectively. In [163], the channel response at the pilot positions is regarded as low resolution image and the estimated channel is a high resolution image. A DL-based receiver called ChannelNet including two different CNNs, super-resolution CNN [171] and denoising CNN [172], which improves the resolution of low resolution image and eliminates the effect of noise, respectively, is designed. In [162], inspired from the image super
resolution technique [173] and conditioned
image synthesis technology [174], two cascaded NNs, channel estimation network and channel conditional
recovery network, have been used to estimate the channel and detect the signal. In [164], a DL-based channel estimation method, that does not need for training, has been introduced for multidimensional OFDM signals. Another channel estimation technique, that combines the conventional LS estimator with fast super resolution CNN, FSRCNN, [175] to increase the estimation performance, has been presented [166]. A deep
residual channel estimation network, ReEsNet, whose architecture is optimized by taking advantage of residual learning, [165] is introduced to outperform ChannelNet [163]. A CNN-based NN architecture called FreqTimeNet [168, 169], that performs learning in both time and frequency domain, is introduced for channel estimation. FreqTimeNet decreases the complexity by facilitating the orthogonality between two domains. Comprehensive simulation results have indicated that FreqTimeNet outperforms ChannelNet and ReEsNet. Furthermore, another NN design called AttenFreqTimeNet, that includes a widely used DL technique called attention mechanism, has been presented. Since it employs SNR information, AttenFreqTimeNet outperforms FreqTimeNet. A CNN-based DL structure called dual CNN, that takes advantage of both angle-delay domain and spatial-frequency domain, is introduced. Moreover, another novel network called HyperNet is proposed to further enhance the robustness. In [170], a GAN-based NN called SRGAN is exploited for super resolution to obtain the whole channel response. Due to the employment of discriminator, the proposed channel estimator has superiority over ReEsNet and traditional techniques such as LMMSE.

It is also possible to implement DL-based techniques at both transmitter and receiver sides. Thus, the whole OFDM transceiver pipeline can be optimized jointly by utilizing the AE-based structure. In [176], authors have shown that it is possible to integrate the E2E system into an OFDM system. Furthermore, for wireless picture transmission across multipath fading channels with nonlinear disstortion, a DL-based joint source-channel coding technique is given [177]. Lastly, in [178], a DL-based MC system called MC-AE, that optimizes modulation and demodulation operations jointly to maximize coding and diversity gain, is presented. This MC-AE system is explained in the following subsection in detail.

On the other hand, various NNs have been designed for channel estimation and symbol detection in OFDM-IM-based schemes. In [179], a fully-connected DNN, called DeepIM, is used for symbol detection of OFDM-IM. The efficiency of the DNN in signal detection of OFDM-IM is demonstrated in terms of both error performance and decoding complexity. A preprocessing based on domain knowledge is applied to the received signal before inputting it to DeepIM. CNN-based receiver has also been proposed where the received symbols are converted to polar coordinates to ease the decoding of information bits transmitted by active subcarriers [180]. To enhance the performance of channel estimation and symbol detection for pilot-assisted OFDM-IM, complex DNN and complex CNN have been exploited [181]. Additionally, a detector called DeepDM that includes both a DNN and a CNN is introduced for an OFDM-IM-based scheme, dual-mode OFDM-IM (DM-OFDM-IM) [182]. In DeepDM, CNN and DNN are used to detect index bits and QAM/PSK modulated bits, respectively. Another receiver called IMNet is also proposed to detect IM-aided MIMO-OFDM, IM-MIMO-OFDM, signals [183]. Finally, the performance of DNN-based receiver is investigated for sparse vector coding OFDM which can also be considered as a member of OFDM-IM family [184].

DNN-based receivers have been designed for also other waveforms such as UFMC, FBMC, GFDM, and OTFS. In [185], for two-stage index modulated UFMC system, a DL-based signal detector called TSIMNet, is proposed over multipath underwater acoustic channels. DNN-based and ResNet-DNN based receivers have been presented for channel estimation and symbol detection of FBMC systems in [186, 187], respectively. Moreover, in [188], a damped generalized approximation message passing method is suggested to minimize receiver complexity in OTFS systems, with the damping factors tuned using DL approaches. A data-driven DNN-based method [189], another low-complexity DNN-based technique [190] that equalizes received signal at the symbol level, and 2D CNN-based design [191] have been proposed for the equalization task at the OTFS receiver. Additionally, in [192], authors have presented a novel DNN-based framework for the equalization of both SISO and MIMO-OTFS systems. DNN-based transceiver architectures have also been introduced for the detection of OTFS signals under IQ imbalance [193] and PAPR reduction [194]. Lastly, DL-aided receivers are presented for GFDM and GFDM-IM systems [195, 196].

In Table <ref> and <ref>, we demonstrate DL-based channel estimation and symbol detection methods with their respective NN techniques for OFDM and OFDM-based alternative waveforms such as FBMC and GFDM. Here, we note that SD and CE represent symbol detection and channel estimation, respectively. It can be seen from Table <ref> that CNN is more frequently exploited particularly for channel estimation due to its ability to learn the correlation in a time-frequency grid.

An overview of DL-based transceiver designs for MC systems.

table_blue Reference      table_blue Name                                                               table_blue CE      table_blue SD      table_blue NN Type      table_blue Waveform

table_white [153]
table_white FC-DNN
table_white DNN
table_white OFDM

table_white [156]
table_white DeepRx
table_white CNN
table_white OFDM

table_white [157]
table_white DCCN
table_white CNN
table_white OFDM

table_white [162]
table_white CENet
table_white SAN, GAN
table_white OFDM

table_white [158]
table_white ComNet
table_white DNN, LSTM
table_white OFDM

table_white [160]
table_white DeepWiPHY
table_white DNN, CNN
table_white OFDM

table_white [159]
table_white SwitchNet
table_white DNN
table_white OFDM

table_white [176]
table_white N/A
table_white AE, DNN
table_white OFDM

table_white [177]
table_white N/A
table_white AE, CNN
table_white OFDM

table_white [176]
table_white MC-AE
table_white AE, DNN
table_white OFDM

table_white [154]
table_white SimNet
table_white DNN
table_white OFDM

table_white [166]
table_white FSRCE
table_white CNN
table_white OFDM

table_white [165]
table_white ReEsNet
table_white CNN
table_white OFDM

table_white [168]
table_white FreqTimeNet
table_white DNN
table_white OFDM

table_white [161]
table_white N/A
table_white CNN
table_white OFDM

table_white [170]
table_white SRGAN
table_white GAN
table_white OFDM

table_white [155]
table_white RoemNet
table_white DNN
table_white OFDM

table_white [163]
table_white ChannelNet
table_white CNN
table_white OFDM

table_white [167]
table_white DualCNN
table_white CNN, RNN
table_white OFDM

table_white [164]
table_white DCE
table_white DNN
table_white OFDM

table_white [169]
table_white AttenFreqTimeNet
table_white DNN
table_white OFDM

table_white [179]
table_white DeepIM
table_white DNN
table_white OFDM-IM

table_white [182]
table_white DeepDM
table_white DNN, CNN             table_white OFDM-IM

table_white [180]
table_white CNN-IM
table_white CNN
table_white OFDM-IM

table_white [183]
table_white IMNet
table_white CNN
table_white OFDM-IM

table_white [184]
table_white Deep SVC
table_white CNN
table_white OFDM-IM

table_white [181]
table_white N/A
table_white DNN, CNN
table_white OFDM-IM

table_white [185]
table_white TSIMNet
table_white DNN
table_white UFMC-IM

table_white [186]
table_white DL-CE
table_white DNN
table_white FBMC

table_white [187]
table_white Res-DNN
table_white DNN
table_white FBMC

table_white [195]
table_white N/A
table_white DNN, CNN
table_white GFDM

table_white [196]
table_white DeepConvIM
table_white DNN, CNN
table_white GFDM-IM

An overview of DL-based transceiver designs for OTFS systems (Red hues indicate no CE or no SD and green hues indicate CE or SD).

table_blue Reference      table_blue Name                                                               table_blue CE      table_blue SD      table_blue NN Type      table_blue Waveform

table_white [188]
table_white DL-Based GAMP
table_white Unfolding
table_white OTFS

table_white [189]
table_white N/A
table_white DNN
table_white OTFS

table_white [190]
table_white Symbol-DNN
table_white DNN
table_white OTFS

table_white [191]
table_white N/A
table_white CNN
table_white OTFS

table_white [192]
table_white N/A
table_white RNN
table_white OTFS

table_white [193]
table_white N/A
table_white DNN
table_white OTFS

table_white [194]
table_white N/A
table_white DNN
table_white OTFS

§.§ DL-Based OFDM 2.0: Overcoming Drawbacks of Classical OFDM

In this subsection, an overview of the literature on DL-based techniques to deal with the main disadvantages of OFDM is given. By enjoying particularly deep unfolding approach, numerous DL-based schemes are proposed to improve the performance of classical OFDM systems.

In [197], a novel PAPR reduction network, PRNet, is proposed where PAPR and BER performance are jointly optimized thanks to its AE-based architecture. Furthermore, a low-complex real-valued NN is presented to reduce PAPR and minimize BER at the same time [198]. Since the proposed method in [198] is implemented in the time domain unlike PRNet, it can provide a reduction in complexity. Nonetheless, optimizing different metrics increases the computational complexity considerably. Motivated from model-driven approach, A DL-based tone reservation network, namely TRNet, is proposed to improve only the PAPR performance to increase the training speed [199]. TRNet improves the performance of the classical tone reservation method and in contrast to PRNet, it is applied only at the transmitter side. Another tone reservation-based DL method called DL-TR is introduced in [200] where conventional tone reservation method is unfolded to design a DNN. Moreover, in [201], a model-driven DL-based tone reservation technique, which yields low-complexity due to its clever design, is proposed by unfolding an iterative tone reservation scheme as a layer of DNN. In [202], the usage of residual NNs with soft-clipping is presented as a novel PAPR reduction approaches. Authors of [203] have proposed to employ a DNN at the transmitter to develop a high-dimensional modulation method that enables regulation of both the PAPR and adjacent channel leakage ratio. Another NN is implemented at the receiver side to decode information bits. Finally, an AE-based PAPR reduction method is introduced for pre-coded OFDM signals without reducing the OOBE performance [204].

In order to remove the CP in OFDM systems, a model-driven DL technique based on orthogonal approximate message passing (DL-OAMP) is presented [205, 206]. The channel estimation module of ComNet [158] and the OAMP detection NN, OAMP-Net, that merges the OAMP method and DL by incorporating a few trainable parameters, are included in the DL-OAMP receiver. Authors have shown that the complexity of DL-OAMP is lower than ComNet and DL-OAMP is capable of adapting time-varying channels. In [206], the performance of OAMP-NET is investigated not only by extensive simulation and also by employing a real communication system. Another model-driven DL method based on OAMP algorithm has also been proposed for CP removal in MIMO-OFDM systems [207]. Although CP is inserted to time-domain OFDM signal to avoid ICI, its length may not be long enough in some practical scenarios. In case of insufficient CP, model-driven DL-based receivers are designed for the detection of SISO-OFDM [208] and MIMO-OFDM [209] signals. In addition to CP, pilot overhead needs to be considered in OFDM systems to increase spectral efficiency. In [210], firstly, the number of pilot symbols is removed partially without a decrease in error performance. Secondly, by using an AE-based NN, pilots are completely removed along with a learned constellation or superimposed pilots. In order to increase the throughput considerably, authors of [211] addressed both CP and pilot reduction issues and demonstrated that it is possible to eliminate CP and pilot entirely by exploiting E2E learning. In [212], for frequency division duplex massive MIMO-OFDM systems, an NN-based combined downlink pilot design and channel estimation approach is presented. An efficient pilot reduction approach is also suggested for reducing pilot overhead and saving time-frequency resources for data transmission by progressively pruning less important neurons from dense layers. Lastly, to solve the channel estimation problem, a pilotless AE-based E2E learning technique is presented in [213] where a CNN and two DNN are implemented in the transmitter and receiver side, respectively.

As mentioned earlier, high mobility causes ICI in OFDM signals and this ICI disrupts the orthogonality between subcarriers and makes the detection process considerably challenging. DL emerges as an interesting tool to be able to detect OFDM signals in fast time varying environment. In [214], a channel estimation network, ChanEstNet, which exploits a CNN and a RNN, is introduced for channel estimation in the high speed mobile scenarios. Moreover, this idea is extended to MIMO systems [215]. DL-based cascaded structures called ICINet [216] and Cascade-Net [217] are also proposed for channel estimation and symbol detection for rapidly time varying channels. Lastly, AE-based DL technique is also developed to cope with complex and fast varying environments in marine communications [218].

An overview of DL-based designs focusing on OFDM drawbacks. (The colors are used to cluster similar problem types.)

table_blue Reference      table_blue Name      table_blue Problem      table_blue NN Type
table_white [197]      table_white PRNet
table_red High PAPR           table_white AE, DNN

table_white [198]     table_white N/A
table_red High PAPR       table_white AE, DNN

table_white [199]     table_white TRNet
table_red High PAPR              table_white  AE, DNN

table_white [200]     table_white DL-TR
table_red High PAPR               table_white  Unfolding

table_white [201]     table_white N/A
table_red High PAPR                table_white  Unfolding

table_white [202]     table_white RDNN
table_red High PAPR                   table_white Residual DNN

table_white [203]     table_white N/A
table_red High PAPR       table_white AE-Residual CNN

table_white [204]     table_white N/A
table_red High PAPR&OOBE         table_white    AE, DNN

table_white [205]     table_white DL-OAMP
table_green CP Overhead         table_white   Unfolding

table_white [206]     table_white DL-OAMP
table_green CP Overhead          table_white  Unfolding

table_white [207]     table_white CG-OAMP-NET
table_green CP Overhead
table_white Unfolding

table_white [208]     table_white DetNet-IG
table_green CP Overhead         table_white    Unfolding

table_white [209]     table_white N/A
table_green CP Overhead
table_white Unfolding

table_white [210]     table_white N/A
table_green Pilot Overhead      table_white    AE, Residual CNN

table_white [211]     table_white N/A
table_green CP&Pilot Overhead      table_white AE, Residual CNN

table_white [212]     table_white N/A
table_green Pilot Overhead       table_white   CNN

table_white [213]     table_white N/A
table_green Pilot Overhead       table_white   AE, CNN, DNN

table_white [214]     table_white ChanEstNet
table_banana High Mobility     table_white   CNN, BiLSTM

table_white [215]     table_white N/A
table_banana High Mobility       table_white    CNN, BiLSTM

table_white [216]     table_white ICINet
table_banana High Mobility      table_white     DNN, Residual CNN

table_white [217]     table_white Cascade-Net
table_banana High Mobility       table_white    Unfolding

table_white [218]     table_white N/A
table_banana High Mobility      table_white     AE, CNN, LSTM

table_white [219]     table_white HybridDeepRx
table_x Nonlinear Distortion
table_white Residual CNN

table_white [220]     table_white N/A
table_x Nonlinear Distortion     table_white DNN

table_white [221]     table_white N/A
table_x Phase Noise Compensation
table_white DNN, CNN

table_white [222]     table_white N/A
table_x Interference Suppression
table_white LSTM, DNN

table_white [223]     table_white N/A
table_x Synchronization Error    table_white DNN

table_white [224]     table_white N/A
table_x One-bit Quantization      table_white AE, DNN

In OFDM transmission systems, DL can be also used to combat other impairments such as phase noise and synchronization errors. For example, a DL-based receiver called HybridDeepRx is introduced to detect nonlinearly distorted OFDM signals [219]. Additionally, in order to alleviate nonlinear distortion in a MIMO-OFDM system, authors of [220] have designed both model and data-driven DL-based receivers. Furthermore, to mitigate the phase noise in channel estimation and symbol detection in both GFDM and OFDM systems, a DL-based method including DNN and CNN, has been devised [221]. In [222], LSTM and DNN-based technique has been utilized to diminish multi-tone interference in a polar coded OFDM system. In [223], an enhanced version of FC-DNN [153] is presented to perform channel estimation and symbol detection in the presence of timing synchronization error. Finally, under the restriction of one-bit complex quantization, authors of [224] explore innovative DL-based techniques for an OFDM receiver.

In Table <ref>, we provide a summary of the reviewed studies that focus on DL-based designs to overcome some important drawbacks of classical OFDM, such as high PAPR and CP overhead. Particularly, deep unfolding comes into view as a significant concept to deal with the challenging impairments of OFDM by unfolding conventional algorithms. For PAPR reduction, AE-based NNs are advantageous since they can still improve the error performance while reducing PAPR.

Although the DL-based methods show great promise, there are still limitations and drawbacks related to the application of DL-based solutions for MC waveforms. One of the most important limitations is the fact that most studies focus on optimizing only one performance metric such as BER and PAPR. Unfortunately, the performance of MC systems relies on multiple key performance indicators, which should be considered in the design process. Implementation of these indicators to DL model design is a challenging task and has not been addressed in the literature. Also, the design process requires the involvement of various 6G applications such as eMBB, URLLC, mMTC, and their possible combinations. These applications require specific challenges that need to be considered in the DL model design.

§.§ An Example AE-Based OFDM System

The block diagram of MC-AE system  [178].

In this subsection, we introduce an AE-based MC (MC-AE) system proposed in [178]. The block diagram of MC-AE is given in Fig. <ref>. In MC-AE, an OFDM system with $N_c$ number of subcarriers, is considered. These $N_c$ subcarriers are separated into $G$ blocks, each containing $N$ subcarriers, where $N=N_c/G$. For each subblock, AE method is performed independently. As seen in Fig. <ref>, the modulator and demodulator blocks of the overall OFDM system are modeled as encoder and decoder DNNs of an AE. At the transmitter side, for the $g$-th subblock, $g=1,\cdots,G$, the incoming message $s_g$  is mapped into a one-hot vector $\mathbf{s} \in \mathbb{R}^{M \times 1}$. Note that the incoming message $s_g$ can be a bit stream with length $m$. Then, $\mathbf{s}$ is passed through linear and normalization layers and converted into a complex-valued vector $\mathbf{x}_g$. After that, the overall OFDM symbol is obtained by concatenating all subblocks as $\Bar{\mathbf{x}}=[\mathbf{x}^{\mathrm{T}}_1,\mathbf{x}^{\mathrm{T}}_2,\cdots,\mathbf{x}^{\mathrm{T}}_G]^{\mathrm{T}}$. After performing OFDM transmission procedures, $\Bar{\mathbf{x}}$ is transmitted through the Rayleigh channel and the received signal is obtained. For the $g$th subblock, the frequency domain input-output relationship can be given as

\begin{equation}
\mathbf{y}_g = \mathbf{h}_g \odot \mathbf{x}_g + \mathbf{n}_g,
\end{equation}

where $\mathbf{y}_g$, $\mathbf{h}_g$, and $\mathbf{n}_g$ are the received signal, channel coefficient and AWGN vectors corresponding to the $g$th subblock, respectively. The elements of $\mathbf{h}_g$ and $\mathbf{n}_g$ follow the distributions $\mathcal{CN}(0,1)$ and $\mathcal{CN}(0,\sigma^2)$, respectively. The average received SNR is defined as $\Bar{\gamma} = E_s/\sigma^2$, where $E_s$ represents the average transmit power.

At the receiver side, the perfect CSI $\mathbf{h}$ is assumed to be known. The real and imaginary parts of $\mathbf{y}_g$ and $\mathbf{h}_g$ are concatenated and the resulting vector is given as an input to the decoder. As seen in Fig. <ref>, the inputted vector is passed through two different FC layers with activation functions ReLU and softmax. Finally, the estimated message $s_g$ is obtained.

For the training of this AE-based structure, a set of random incoming messages and randomly generated channel and noise samples are used. The MSE loss function and SGD optimization method are exploited for training.

The learned constellation for MC-AE system can be seen in Fig. <ref>, where each marker represents the complex constellation point of a subcarrier. In [178], it is demonstrated that diversity and coding gains of MC-AE system are higher than benchmark schemes thanks to the learned constellation by the capability of AE structure. Therefore, the MC-AE system can provide outstanding error performance in fading channels.

The learned constellation of MC-AE for $N=2$ and $M=8$.

§ DL-AIDED COMMUNICATION SYSTEMS THROUGH RIS

Communication through riss, emerges as a new and promising technology for next-generation communication systems due to its numerous advantages such as enabling smart radio environments with low cost and high efficiency. The RIS technology, which provides environmentally friendly solutions while increasing wireless communication performance, offers attractive advantages at low cost and complexity due to its passive reflective architecture. Thus, the RIS technology has started to be seen as a strong candidate to be an indispensable part of next-generation systems, as an alternative to massive MIMO systems [6].

An RIS interacts intelligently with incoming signals, aiming to expand energy efficiency and coverage in radio communication systems. The mentioned controlled interaction is realized with the adaptive meta elements on intelligent surfaces. It is aimed to obtain the optimum phase configuration and to optimize the wireless communication performance by manipulating the incoming signals thanks to these meta elements. Here, the incoming signals from the BS are controlled over-the-air in real-time and reflected to the receivers. Intelligent surfaces aim to increase the signal level at the receiver side as well as extend the signal coverage as illustrated in Fig. <ref>. Here, we consider the scenario of coverage expansion for multiple users having no line-of-sight path to the BS thanks to intelligent reflection phase configuration by an RIS coated on a building. To put it another way, RIS technologies enable intelligent wireless communication environments using software control methodologies. Thanks to their flexible mechanism, it is possible to work in different frequency bands such as terahertz communication with RISs [225] where it is crucial to establish mass communication and reduce interference between users.

An RIS, along with offering energy-efficient signal modulation at low cost in intelligent radio environments, takes an active role in secure and MU communication [226]. RIS-assisted beamforming techniques can ensure the secrecy of communication in the presence of a potential eavesdropper. The fact that it can be used for wireless energy transfer in low-energy applications such as wireless sensors, where continuous energy supply is required, induces us to see RISs frequently in areas  such as the IoT. Smart surfaces, which are capable of directing electromagnetic (em) waves thanks to their unique structure and free-working nature, might be influential players in energy transfer [227]. Intelligent surfaces that act as reflective relays in the communication environment affected by poor environmental conditions are also used to improve the qos.

All these advantages of RIS architectures and the success of passive  beamforming techniques show a solid commitment to channel knowledge [228]. The acquisition of multi-hop communication channel information in RIS-assisted communication schemes is more complicated than in traditional massive MIMO systems and can be addressed as an issue that needs to be studied
There beside, phase optimization algorithms show a strong commitment to the presence of the perfect channel information at the BS. Yet, acquiring this channel information can also become challenging due to hardware limitations, especially in RIS scenarios with passive reflective elements [229].

RIS-assisted wireless communication for multiple users.

New scenarios brought by RISs for next-generation communication technologies will create new requirements that existing architectures will not be able to meet. Apart from the aforementioned challenges, the optimization of energy consumption of next-generation systems and providing storage options to meet the high training load of complex systems are some of the issues that need to be addressed by researchers. Adaptation performance of RISs to EM signals in radio environments  is one of the major issues that are open to improvements
For instance, in RIS-assisted iot applications, the number of wireless devices connected to the intelligent surface can be vast. In such cases, the number of parameters required for system optimization will increase to the same extent [230].

Advanced information processing techniques are essential in the response performance of smart surfaces to EM signals. This will require additional resources in terms of computation time and storage, as well as additional energy and bandwidth [231]. The biggest obstacle in adapting existing systems to new communication technologies is that analytical models limit the working flexibility. Analytical modeling applies not only to network planning in the first stage in scenarios using RISs but also to resource management and network control where the adaptive approach is used. The complexity of RIS systems raises the possibility that existing traditional analytical models may become dysfunctional for them

Considering the situations mentioned above, uncertainties arising from RIS system configurations and channel dynamics complicate the system design [232]. Although signal processing techniques utilized by conventional RIS models provide solutions to many communication problems, their dependence on hypothetical mathematical models causes certain drawbacks. Model-independent solutions such as dl stand out as a convenient solution to combat the aforementioned uncertainties. In RIS scenarios, the rapid change of the radio scattering and the hardware impairments result in certain mismatches, such that signal processing performance deteriorates. Thanks to its ability to process raw data and make it meaningful, DL can make a model-independent mapping by training with learnable parameters [228]. DL approaches yield more efficient and flexible results compared to traditional analytical model-dependent methods that are capable of working under only certain conditions. In other words, it would not be right to talk about the actual concept of "smart surfaces" in a communication environment where DL or ai approaches are not implemented [17].

DL applications for RIS-assisted MIMO systems.

In pursuit of the aforementioned advantages of DL-based approaches, many novel studies have been carried out in recent years using DL techniques in the field of RIS-assisted communication. This section is fictionalized on channel estimation and signal detection solutions, passive beamforming design scenarios, and resource allocation in NOMA techniques for RIS-aided DL-based systems. In Fig. <ref>, the major applications of  DL  solutions in RIS-assisted systems are categorized from the perspective of receiver optimizations and RIS-side phase configurations. Finally, we discuss the actions that need to be taken to compensate for potential drawbacks of current applications.

§.§ Channel Estimation and Signal Detection for RIS-assisted DL-based Systems

Since the number of reflecting meta-elements increases in RIS-assisted massive MIMO systems, the channel acquisition process becomes more challenging due to the increased number of channels. In other respects, reliable and accurate channel acquisition carries critical importance for 6G and beyond technologies. The main inspiration behind DL-based solutions is balancing the trade-off between system complexity and achievable rate performance. Therefore, as can be seen in Table <ref>, several studies are present in the literature on channel estimation and signal detection.

The biggest motivation for combining DL techniques with RIS-assisted wireless communication is the acquisition of mapping methods that can be unveiled without linear mathematical models. In this regard, [233] performed a non-linear mapping between the sent and the received signals and optimized signal detection with a DL approach. A DL solution for signal detection proffered by [233], which contains individual learned models for each user. The foremost advantage of this scheme is that no channel estimation algorithm is required. Nonetheless, this system needs either beamforming optimization on the BS side or phase shift optimization directly by the RIS to improve the BER performance of users.

On the side of channel acquisition, [228] and [234] combined channel estimation scenarios with the DL approach and performed channel estimation for both reflected and direct paths. The proposed channel estimation technique, which employs the cnn architecture, is highly dataset dependent. The diversity of data sets influences performance efficiency, which can sometimes limit the performance to local dimensions. This causes high training overhead during channel estimation learning. To overcome this problem,
[235] created the architecture of deep denoising NNs, that reduces the training load. In this model, the intelligent surface contains both active and passive elements concurrently. This architecture combines compressed sensing and DL approaches to achieve greater efficiency than either approach alone. However, the presence of active elements increases hardware complexity. Assuming that one of the main purposes is to reduce complexity in traditional mmWave massive MIMO architecture, this can be regarded as an issue that must be addressed.

An overview of dl-based channel estimation and signal detection studies for RIS systems.

table_blue Reference
table_blue Type of Algorithm
table_blue Application Scnearios

table_white [228]

table_white DL-CNN
table_white A novel CNN-based intelligent channel estimation framework without need of re-training for relocated users up to 4 degrees

[234]

table_white Federated learning-CNN
table_white Channel   estimation for direct and cascaded paths via a novel DL approach in RIS-assisted mmWave MIMO systems

table_white [235]

table_white DL-Deep Denoising NN
table_white Combining the CS and DL to reduce training overhead by Deep Denoising NN architecture

table_white [236]

table_white DNN-Deep Unfolding
table_white  Deep unfolding with enhanced estimation performance at lower computational complexity and training overhead compared to the conventional least square estimator

table_white [237]
table_white DL-CNN
table_white  CSI acquisition with the sparse channel sensors and optimization of the RIS phase configuration with reduced training overhead

table_white [238]
table_white MLP-DNN
table_white  Strengthen interaction between channel information and passive beamforming of conventional DL-based systems by practicing MLP architecture

table_white [239]
table_white Unsupervised Learning-CNN
table_white  Two-stage novel channel estimation method developed to reduce the training load in mmWave communication scheme

table_white [240]
table_white DL-CNN
table_white A novel data-driven channel estimator in MISO architecture to achieve reduced system complexity

table_white [241]
table_white DL-DNN
table_white Sequential trained three-stage synthetic DNNs to estimate cascaded channel by using fully passive elements

table_white [242]
table_white CENet-CNN
table_white Enhanced element-grouping method by investigating and eliminating channel interference to reduce pilot overhead with reliable channel estimation processes

Integration of DL-based techniques in RIS-aided systems has been challenging due to complicated training processes since the number of channels to be exploited is proportional with the number of reflecting elements. Therefore, the literature frequently focuses on this subject. DL-based channel estimation techniques reducing the training overhead are also presented in [237], [238], and [239] by different learning and network architectures. [238] aimed to strengthen the interaction between channel information and passive beamforming of conventional DL-based systems by practicing the multi-layer perceptron (MLP) architecture. [239] presented a two-stage channel estimation method to reduce the training load in mmWave communication schemes. The considered DNN is trained with a reduced number of users in the first stage to learn effective channel parameters over active users. Then, the authors present a spatial-temporal–spectral framework that estimates deficient channel information in the second phase. [237] used a similar approach, in which the channel information obtained with only a few active elements was also used to estimate the remaining channels, resulting in almost no training overhead. In the first part of the offered two-tier solution, the authors performed CSI acquisition with the presented sparse channel sensors structure and then used the obtained information to optimize the RIS phase configuration. However, the presence of these controlled active elements used in channel reconstruction cannot practically meet the standalone operation principle and the passive nature of RISs. The presence of those active elements also increases the system.  [236] has been inspired by the high learning and prediction capability of DNNs and aimed low system complexity for mmWave communication. The proposed deep unfolding method gives higher estimation performance at lower computational complexity and learning load compared to the conventional LS estimator. The model-driven unfolding method, which is a sub-type of DNN and has the ability to learn from extensive synthetic data with lower iteration, follows an algorithm similar to traditional gradient descent optimizations. [240] proposes a novel data-driven channel estimator in MISO architecture to achieve reduced system complexity for a similar purpose of [236]. This study benefits from the fact that the CNN structure involves fewer parameters compared to prior architectures such as DNN. The proposed CNN-based channel estimator has superiority over traditional linear estimators in terms of implementation complexity. Nonetheless, extended scenarios, including mobility and plurality of users as well as multiple antennas assumption, will be vital to realize this architecture in practical applications.

One of the fundamental motivations for exercising DL methods in channel estimation is to employ fewer pilots. [241] and [242] presents DL-based approaches achieving reliability in estimation accuracy while reducing the pilot overhead. Both studies perform entire channel extrapolation from sampled channels. [241] proposes sequential trained three synthetic DNNs. These DNNs first estimate the direct channel and then the cascaded channel. The last DNN predicts the cascaded channel by using fully passive elements at the final stage.
[242] also used multiple DNNs, which are cascaded and sequentially trained. The proposed study enhances the conventional element-grouping method by investigating and eliminating channel interference. Sampled channels refined by the first DNN as a result of interference elimination are used in the prediction of entire channels in the second stage. Both studies gain significant improvements in reducing the pilot overhead with reliable channel estimation processes.

Considering the current approaches in the literature, simpler wireless design schemes have been the focus of ongoing investigations on RIS-assisted systems. In order to measure the performance boundaries in wireless connections utilizing RISs, more accurate modeling approaches are necessary for the transmission of signals scattered by metasurfaces. Another challenge is that no amplifiers are present in an entirely passive RIS architecture. At that point, RISs might need a low-powered unit to be informed by current channel conditions and other communications blocks such as receivers or transmitters for the purpose of phase optimization. Benefiting these channel monitoring units embedded with RISs powered by novel energy harvesting methods might be necessary for energy-efficient approaches.

As concluded from the majority of recent studies, the presence of perfect CSI is crucial at the transmitter. Nevertheless, it is not a straightforward process to retrieve full channel knowledge in practice because of the passive nature of RIS-aided schemes. For this reason, the quick and accurate channel acquisition required for next-generation technologies causes a high training burden.
For the present, supervised learning techniques are commonly employed to tackle channel issues in order to increase efficiency, and DL approaches investigate csi structures of current designs.
However, due to the limitations mentioned above, the research efforts can be expanded with different deployment scenarios. For instance, using RISs in a near-field setting might offer several exciting applications and benefits.

An overview of DL-based passive beamforming design studies.

table_blue Reference
table_blue Preliminary Summary
table_blue Type of   Algorithm
table_blue Application Scenarios

table_white [243]
table_white Location-based Training
table_white DNN
table_white The fingerprinting database to unveil mapping between measured user location and   RIS unit cells to maximize received signal strength

table_white [232]
table_white Phase Shifts Design
table_white Deep Reinforcement Learning-DDPG
table_white DDPG   algorithm that can quickly adapt the model for dynamic channel state and environmental   conditions  thanks to the continuous action space

table_white [244]
table_white DRL for Joint Beamforming
table_white Deep Reinforcement Learning-DDPG
table_white An   optimization where the DDPG algorithm is responsible to search for the   optimum action at each decision during the network learning phase

table_white [245]
table_white Enhanced Data Rate and Protected Privacy
table_white Federated Learning-MLP
table_white The federated architecture to reduce transfer overhead offering beamforming optimization on the RIS   side

table_white [246]
table_white Power Allocation
table_white Unsupervised Learning
table_white A novel usupervised learning architecture for beamforming design optimization

table_white [229]
table_white Standalone RIS Architecture
table_white Deep Reinforcement Learning
table_white The architecture created an active learning scheme that quickly adapts to changing environmental conditions with high performance

table_white [247]
table_white Supervised Learning for Phase Shift Design
table_white Feed-forward NN
table_white The feed-forward NN fed with reflected symbols performing phase matrix optimization

table_white [248]
table_white  Historical Channel Knowledge
table_white  Deep Reinforcement Learning-DNN
table_white  DNN architecture evaluating historical line-of-sight path channels to interpret channel behaviour

table_white [249]
table_white Secrecy Rate Optimization
table_white Unsupervised Learning-DNN
table_white Supervised Learning approach for the secrecy rate optimization at  receiver and phase configuration optimization on the RIS side with reduced computational complexity

table_white [250]
table_white Mobile User Scenario
table_white Deep Reinforcement Learning
table_white Deep reinforcement learning approach achieving higher SNR for noisy channels and mobile scenarios comparing to conventional techniques

table_white [251]
table_white Learning from Environment
table_white Deep Reinforcement Learning-DNN
table_white  The relay selection optimization to reduce propagation loss over distance by the proposed Deep Reinforcement Learning model that can learn from the environment

table_white [252]
table_white Terahertz Communication
table_white Deep Reinforcement Learning-DNN
table_white  A method that addresses the challenge in path loss optimization for RIS-aided terahertz communication having high molecular absorption and attenuation

table_white [253]
table_white Resource Allocation
table_white Deep Reinforcement Learning-NN
table_white  Resource allocation  for D2D networks and phase shift configuration optimization in terms of achievable rate and computational time performance

table_white [231]
table_white Energy-efficient Policy
table_white Deep Reinforcement Learning-DNN
table_white Deep Reinforcement Learning approach presenting a fully energy-efficient method by optimizing the ON/OFF state of RIS elements besides transmit power

table_white [254]
table_white Dynamic Positioning of RIS
table_white MLP-Position Based
table_white The MLP-based NN addressing dynamic positioning of multiple RISs to overcome the storage and computational performance limitations

table_white [255]
table_white Optimized Energy Expenditure
table_white  Unsupervised Learning-NN
table_white A NN trained by the approach of deep unsupervised learning optimizing both energy expenditure and phase configuration

table_white [256]
table_white Mapping without CSI
table_white  ML/DL-DNN
table_white  The ML-based approach leveraged by DL techniques has no need for CSI for direct mapping

table_white [230]
table_white Bypassing Channel Prediction
table_white  Unsupervised Learning-DNN
table_white  Proposed algorithm bypassing channel prediction process, requires fewer pilots compared to prior studies with the channel estimation

table_white [257]
table_white Reduced Hardware Complexity
table_white Unsupervised Learning-DNN
table_white  Proposed deep-transfer learning-based algorithm requesting less sampled data for training process resulting in reduced hardware complexity and training load

table_white [258]
table_white Learning Channel Behaviour
table_white Deep Reinforcement Learning
table_white Deep Reinforcement Learning-based novel architecture capable of learning channel behaviour

§.§ Passive Beamforming Design with DL for System Optimization

Passive beamforming design is among the most critical problems for the RIS technology to be included in the next generation of communication systems. The success of phase reconfiguration not only increases the quality of signal transformation but also increases the adaptability of RISs to different communication environments such as indoor/outdoor applications. Especially in highly dynamic channels, such as application scenarios with high mobility, the inadequacy of existing control systems has revealed the necessity of a new perspective. In preserving the fully passive nature of RISs with reduced costs and system complexity, truly intelligent approaches have a bright future for next-generation communications. Therefore, DL-based passive beamforming techniques have extensively been addressed by the literature as presented in Table <ref>.

Current DL-based studies on RIS-aided communication systems are composed of various NN structures depending on their advantages for target applications. mlp, a member of the feed-forward artificial-NN and one of the most basic types of DNN, can be given as an instance. [237] offers an mlp architecture for beamforming design on the RIS side. The considered RIS scheme, which has a hybrid structure in which active and passive elements are present simultaneously, performs pilot training with randomly distributed active components. The trained data set, in which the beamforming on the RIS side is established as an input-output pair, is built with the supervised learning scheme. The use of active elements can be considered as a drawback of this study. The possible advantages of the supervised learning-based MLP algorithm with fully passive RIS have been inspected in [254]  to overcome the storage and computational performance limitations in the dynamic positioning of multiple RISs. The MLP-based NN has fed by dynamic channel information as well as RIS positioning information. The proposed position-trained NN results are superior to benchmark results retrieved by an exhaustive study.

[245] offers beamforming optimization on the RIS side using the federated learning  approach. After the training phase of the mlp  architecture, model updates are calculated for each user using local data sets. Model updates are received from the parameter server to which the RIS is connected. The results of the study indicate that the transfer overhead is reduced thanks to the federated architecture. However, because of the passive nature of RISs, the scenario in which it is constantly connected to the parameter server, makes this architecture inefficient. At that point, [259] sought a solution to cure unit modulus constraints of passive reflecting surfaces from previous studies and conducted an approach that also reduces model complexity during the training phase. This study proposes an unsupervised learning architecture, in which the direct and reflected channels are the inputs, and the output is the phase values of the response beamforming of the RIS while the mapping is unveiled. This architecture is capable of making online predictions thanks to the trained DNN in the offline phase. However, the output parameters are unique for each learning phase. This contradiction will act as a layer. Therefore, each learning data will instantly output different values. However, different methods have been proposed in the literature to prevent this stratification and can be treated as further study on enhancement of this study.

Deep reinforcement learning, appears as a promising method among the DL-based techniques with its high learning capability and other recent advantages in the decision-making process. As a result, it also appears as a strong candidate for various fields in RIS-assisted next-generation communication systems. The importance of deep reinforcement learning comes from its remarkable ability to make inferences from limited knowledge. The literature covers a wide range of topics, such as providing energy-efficient and robust processes or extending coverage, but it can primarily be evaluated at a higher level as passive beamforming applications.

[232] proposes the deep deterministic policy gradient (ddpg) technique for a DL-assisted RIS scheme, which combines the deep-Q network and policy gradient (PG), where the continuous action space is in use to accelerate the training phase. The presented model can adapt quickly to changing channel data and environmental conditions thanks to the continuous action space of DDPG algorithm. Yet, using multiple NNs increases the number of parameters in the learning phase. Therefore, the training overhead will cause high system load and storage requirements as well as hardware costs. To overcome the computational complexity and hardware limitations introduced by multiple NNs, [257] offers a model combining deep-transfer learning algorithm and unsupervised learning which requests less sampled data for the training process. Therefore, results demonstrated that hardware complexity and training load problems in previous studies are addressed by this scheme. It is also inspected that a less transmit power request at the BS side is achieved to tackle hardware cost and limitations as well as process time.

Here, the DDPG algorithm has also been used by [244] to optimize and accelerate the learning process. The DDPG algorithm is responsible for searching the optimum action for each decision during the network learning phase. Thus, the learning phase will be accelerated by optimizing the search space. However, this somewhat contradicts the model-free nature, which is the main philosophy of the DL approach, as the learning process will require an optimization toolbox and signal models for the learning process. At this point, instead of optimization-based techniques, it would be more appropriate to follow methods using completely deep model-independent learning approaches. To compensate model-dependent structure of current deep reinforcement learning-DDPG applications, [250], [229], and [248] proposed their advanced deep reinforcement learning techniques making the systems robust in changing environmental conditions. By these advanced deep reinforcement learning techniques, not only the channel information but also channel behavior can be learned similar to [258]. [250] has achieved a higher SNR for noisy channels and mobile scenarios compared to conventional deep reinforcement learning methods. The authors of [248] demonstrated that their algorithm could enhance the BER performance in every cycle thanks to the evaluation of previous rewards for actions, so that enhance optimization of the phase matrix. The channel behavior can be interpreted by evaluating historical line-of-sight path channels to determine optimal phase configuration for further actions.

[229] developed an active learning scheme that adapts rapidly to changing environmental conditions while maintaining high performance. However, since reinforcement learning works with a reward mechanism, it has a longer operation time and lower performance compared to supervised learning. The fact that the considered scheme does not have a layered structure causes it to lag behind supervised learning performance. However, [255] has demonstrated that a design outperforming supervised learning in terms of both energy and time consumption can be created without the use of a layered structure. The proposed algorithm with trained NNs by the approach of deep unsupervised learning optimizes both energy expenditure and phase configuration to outperform even the Genetic algorithm, which is considered a groundbreaking development in optimization. That being said, this improvement comes with a slight loss in throughput performance compared to the genetic algorithm. However, that loss can be compensated with an increased number of antennas.

Unsupervised learning has also been used to optimize computational complexity. In [260], efficient relay selection optimization is performed with low system complexity by the proposed algorithm. On the other hand,[251] outperforms previous deep reinforcement learning-based techniques in terms of computational complexity for complicated phase shift optimization scenarios by creating direct relation between optimization parameters and throughput. The relay selection method is also conducted to reduce propagation loss over increasing distance by the proposed deep reinforcement learning model that can learn from the environment.

Propagation loss is also the subject of terahertz communication which has a bright future for next-generation communication systems. [252] seeks a method that addresses the challenge in path loss optimization for RIS-aided terahertz communication which has high molecular absorption and attenuation. The proposed method implementing hybrid beamforming provides 50% coverage extension compared to ZF and alternating beamforming.

In addition to prior applications, joint optimization of transmission power with phase shift configuration by deep reinforcement learning in RIS-assisted systems provides major advantages for optimizing achievable rates and achieving an energy-efficient system. [253] performs resource allocation optimization for device-to-device (d2d) networks. The proposed scheme conducts a proximal policy optimization algorithm to define the policy gradient of deep reinforcement learning that seeks the maximized reward. The presented model outperforms the random phase shift matrix selection algorithm with random relay selection and maximum power transmission algorithm that also performs phase-shift configuration optimization in terms of achievable rate and computational time performance. The great reward achieved by [253] in which the deep reinforcement learning was involved, was enhanced by [231] in an energy-efficiency manner. [231] has aimed to present a fully energy-efficient method by optimizing the ON/OFF state of RIS elements besides transmit power. In this scheme, the access point receives available energy information from CSI in order to make the best decision for the current state via a backhaul link that connects the RIS to the BS. [229] terminates the necessity of controlling RIS from any infrastructure in order to introduce standalone operation. The proposed study shows that RIS is capable of learning and adjusting optimal relay selection using the proposed deep reinforcement learning approach with almost no training overhead, thanks to the online learning phase.

Advanced DL applications also promise intelligent environments in the case of mobile user scenarios for RIS-assisted communication systems. [243] used the DL approach in phase configuration on the RIS side and developed a process that yields user location-dependent results. An NN using the location information as input is trained for many reference points to give optimized phase outputs. In other words, this study offers an efficient method for configuring an RIS in the indoor communication environment. The fingerprinting database in the training of weights and biases forms the concept of location-based learning, which unveils a mapping between measured user location and RIS unit cells to maximize the received signal strength. Nevertheless, both the inefficiency of RIS positioning and the inefficient result in the throughput must be investigated.

[247] used the DL approach to optimize the phase configuration on the RIS side, trained feedforward NN with reflected symbol signals and aimed to obtain the optimum phase matrix. The facts that the system cannot superior the least square estimation methods for high symbol powers and causes high signaling overhead are the points that need to be addressed. Also, the system should be analyzed under the assumption of imperfect channel estimation. However, channel acquisition is a significant challenge in RIS-assisted schemes due to complicated channel information caused by a large number of reflective elements. Therefore, [256] and [230] proposed approaches unveiling a direct mapping between optimal phase configuration and achievable rate.[230] uses the pilot signals in the DNN trained by an unsupervised learning approach to determine the optimal phase configuration instead of using it in the channel acquisition. The proposed algorithm bypassing the channel prediction process requires fewer pilots compared to prior studies with channel estimation. In [256], the considered ML-based approach leveraged by DL techniques has no need for initial CSI for direct mapping. The proposed algorithm is capable of learning the relation between RIS configuration and achievable rate correlated with dynamic receiver positioning. Since [256] is one of the studies that give the closest results to the benchmarks with the perfect channel assumption, the proposed method gleams as a bright future direction so that RIS shift optimization can be executed without the need for perfect CSI from the infrastructure.

Following a different perspective from prior studies, [249] concentrates on the advantages of the RIS architecture for the field of PHY security. This study intends to optimize the secrecy rate at the receiver by tuning meta elements on the RIS side. Therebeside, real-time tuned reflecting elements allow phase configuration optimization on the RIS side using the DL approach and this proposed a technique that yields similar outcomes to traditional methods but at the same time reduces the computational complexity considerably.

Considering these studies and the rest of the literature, we conclude that the results obtained in combining RIS architectures with DL approaches do not adequately meet the flexibility and required performance for 6G and beyond wireless communication systems. In particular, the limitations are presented by both the transmitter and the user perspectives, such as the high computational complexity, the high training overhead, and the perfect csi assumption in the transmitter. Besides, the use of active elements on smart surfaces in optimizing the phase configuration does not comply with the fully passive nature of RISs. The urgent need to achieve high energy efficiencies at the lowest possible cost should be considered. It is clear that the literature still contains many gaps that require work in light of these problems. Examination of different RIS positioning scenarios, optimization of system performances over computer simulation, and application scenarios under different environmental conditions, in which the DL approach enhances channel estimation and phase configuration optimization, is vital in designing promising perspectives on related research problems for 6G and beyond.

Another technical limitation that makes current system designs less viable is the assumption of a static environment. Though, in dynamic scenarios, the mobility of users includes some additional parameters such as speed, acceleration, and position that need to be considered in the process. Therefore, the learning process for the data traffic of a mobile user becomes more complicated due to dynamic channels. Moreover, the system parameters cannot be specified as constants because of the infinite amount of mobility. This situation makes it challenging to employ supervised techniques in which input-output pairs are labeled and learned from properly arranged data. Considering the benefits that ML techniques provide in the face of complex data in various fields, methods such as computer vision or neural language processing can also be regarded from the perspective of communication engineering. At this point, structures such as convolutional neural networks may be preferred instead of more superficial DNN structures.

RISs are comprised of complex formed subwavelength elements. As a consequence, a fundamental restriction in continuing RIS research is the lack of accurate and controllable models that define reconfigurable metasurfaces as a function of their EM features. As the majority of studies assume, the passive reflection behavior of RISs is not applicable in practice. Components such as material composition, polarization, and angles influence the RISs response to radio waves. In other words, RISs do not just reflect the waves but also re-design them. For RIS topologies, practical EM-based circuit designs should be used, which take mutual linking and unit cell configurations into account, similar to [261].

§.§ DL Techniques for Resource Allocation in RIS-aided NOMA Systems

NOMA stands out as another promising technique for increasing the efficiency of future massive MIMO systems. Therefore, NOMA-MIMO techniques have been studied extensively in the literature to examine how to improve the spectrum and energy efficiency required by 6G and beyond technologies and to increase the total capacity of multiple users [262]. Thus, NOMA sparkles as a promising solution for challenging fields of 6G communication systems such as massive IoT that require high data capacity and lower latency with high spectral efficiency. Fig. <ref> illustrates an RIS-assisted downlink-NOMA scenario where the users having correlated channels are clustered together. Effective clustering schemes with individuals having minimum correlation with other clusters are provided to conduct efficient resource allocation and enhanced throughput while decreasing interference.

RIS-assisted downlink-NOMA with multiple clusters.

Besides all the aforementioned advantages of NOMA, DL approaches  will be critical in improving existing systems to meet the high-performance system requirements of next-generation perspectives. In the most general sense, the studies aimed at improving the performance of existing NOMA systems using the DL approach can be categorized under the detection of channel characteristics and SIC. Power optimization, user clustering, signal detection, decoding techniques, and phase configuration can be cited as the main areas where the DL approaches are employed to improve existing NOMA schemes. Since DL approaches are vital in increasing the performance of traditional RIS-assisted NOMA systems, numerous studies have been carried out in relevant areas as presented in Table <ref>.

In the current NOMA literature, the intelligent system approach with DL has been mainly seen in user clustering scenarios that have already been studied extensively due to their critical role, as mentioned earlier. [263] provided a new idea about possible contributions of intelligent approaches in next-generation communication systems by presenting the effect of the DL approach integrated into a conventional RIS-assisted NOMA architecture. The proposed study presents a user clustering algorithm that reduces computational complexity and creates the optimum power allocation with sequential decoding. The study, which designed a K-means-based ML algorithm for user clustering, benefited from the correlation in the user channels of mmWave systems. So the proposed K-means-based algorithm also has the ability to do online user clustering. This characteristic allows it to outperform traditional K-means-based algorithms in terms of performance in lower system complexity. Therebeside, a conventional K-means-based Gaussian mixture model is conducted as a clustering technique used to interference alignment to cope with growing network traffic by [264]. The proposed method aims to perform passive beamforming optimization on the RIS side with the ML algorithm. By this approach, they sought an optimized solution for signal decoding, power allocation, and user clustering under QoS constraints while the MISO-NOMA downlink scheme is considered for the proposed network. A novel Deep Q-network algorithm is adopted in the regulation of the power allocation process and the phase configuration on the RIS side.

The advantages of handling clustering techniques with intelligent approaches are also studied and discussed in RIS-assisted scenarios by [246], [265], and [266]. In an RIS-assisted NOMA communication scheme, [246] optimized the phase configuration using the deep reinforcement learning approach, thus aiming to increase the spectrum efficiency by developing a deep reinforcement learning approach solution for resource allocation. The authors of this work also propose multiple clustering schemes providing by the BS under the dynamic number of users assumption for miso scheme. A novel Deep-Q network-based algorithm is introduced for the phase matrix configuration and power allocation. The proposed algorithm is superior to the benchmarks such as traditional RIS-assisted orthogonal multiple access (oma) systems.

An overview of DL-based applications in RIS-aided NOMA studies.

table_blue Reference
table_blue Type of   Algorithm
table_blue Application Scnearios

table_white [246]
table_white Deep Reinforcement Learning-Deep Q
table_white A novel Deep-Q network-based algorithm for the phase matrix configuration and power allocation for a dynamic number of multiple users in RIS assisted downlink NOMA scheme

table_white [265]
table_white Federated Learning
table_white Over-the-air federated learning algorithm for resource allocation with RIS-assisted hybrid network

table_white [266]
table_white Deep Reinforcement Learning-DDPG
table_white DDPG-based optimization algorithm capable to learn long-term policy for configuring phase shifting as well as user clustering under dynamic states

table_white [264]
table_white Deep Reinforcement Learning- Deep Q
table_white A novel deep Q-network algorithm adopted to regulate the power allocation process and K-means-based Gaussian mixture model for user clustering

table_white [267]
table_white Deep Reinforcement Learning
table_white Deep reinforcement learning approach  involved in phase prediction and tuning, aiming the lowest possible training data load

Thanks to the reward mechanism in Deep reinforcement learning, [266] used a structure that can adapt to the dynamic number of users presented by [246] to learn the channel behaviour. [266] proposed a long-term stochastic optimization technique aiming to maximize the sum rate for multiple user NOMA downlink communication. They aimed at joint optimization of phase configuration on RIS side and user clustering. The proposed DDPG-based optimization is also capable of taking optimal action for dynamic states by various scenarios. This makes it possible to learn and apply long-term policy for configuring phase shifting as well as user clustering. The presented algorithm has optimized the sum data rate of mobile users, outperforming the results of traditional OMA technologies.

However, compared to conventional OMA solutions, the high system complexity and high training load created by NOMA schemes are an issue that needs to be addressed. [265] proposed the relaxation-then-quantization method that addresses the problem of optimizing the balance between system complexity and performance. This study combined the over-the-air federated learning algorithm with a RIS-assisted hybrid network and developed a flexible method that can adapt to different channel conditions. The presented study performs resource allocation by optimizing the transmitter power and phase configuration on the RIS side while meeting different QoS requirements. It is aimed to maximize the potential hybrid rate by performing these joint optimizations.

High training overhead created by NOMA schemes is also addressed by [267] aiming to optimize total system capacity for the absence of CSI, using an approach similar to other downstream NOMA schemes. It is intended to learn the optimum phase configuration of the RIS, where the Deep reinforcement learning approach is involved in phase prediction and tuning, with the lowest possible training data load. In the same breath, as mentioned before in this survey, the performance degradation of imperfect SIC on the user data rate was studied. As a result of the parametric study, it was determined that the SIC imperfection rate grew ten times causing the decline of the system performance by 10

When the current RIS-aided NOMA literature is considered, it is evident that the existing studies are insufficient in terms of system complexity, energy efficiency and adaptation capability to different channel conditions required by 6G and beyond communication technologies. In particular, the high complexity of the systems and optimization problems has pushed the current practical studies to single antenna system assumption. Therefore, performance improvement of the RIS-assisted NOMA in MIMO systems emerges as a subject that needs to be addressed. In RIS-NOMA scenarios, imperfect SIC adoption is the main factor that degrades system performance and limits the number of users. Considering the current literature, it is evident that SIC techniques using conventional optimization algorithms are insufficient to obtain adequate performance. For this reason, novel grouping techniques have been developed and applied such as [242]. However, the development of advanced interference cancellation processes seems as a bright future for next-generation NOMA architecture. Advanced DL approaches for SIC optimization will undoubtedly increase the performance of RIS-assisted NOMA systems.

§ PHY SECURITY MEETS DL

In this section, we present an overview of dl-based PHY security studies. For this purpose, firstly, the PHY security concept will be briefly introduced and dl-based PHY security methods will be categorized for their attack type: spoofing, jamming, and eavesdropping. In each subsection, our investigation results will be presented with explanations and comparisons.

Wireless communication is highly vulnerable to malicious attacks at phy as a result of the open nature of the wireless channel. Since the channel is accessible by any party, an adversary can easily interfere with legitimate communication at phy.
Traditionally, a countermeasure for PHY attacks is designed at upper layers, e.g., network or transport layers. However, traditional methods are often resource-inefficient or might have limited protection in 6G networks or emerging iot applications. Therefore, providing effective countermeasures against PHY attacks is an open problem.

In the past years, many researchers focused on PHY security methods to tackle this problem. These methods are essentially based on exploiting phy attributes for security purposes.
Although PHY security shows great promise, it also holds significant challenges for practical implementations. With the emergence of dl networks, researchers sought dl-based PHY solutions to overcome existing challenges or further improve security performance. Readers are referred to Table <ref> for a list of related survey papers in this field.

Three different PHY attack types: (a) Spoofing: Eve aims to impersonate Alice. (b) Jamming: Eve aims to block the communication between Alice and Bob. (c) Eavesdropping: Eve aims to obtain the messages that are sent from Alice.

Existing survey papers related to dl-based security or phy security.

table_blue Survey      table_blue Year      table_blue Title
table_white OShea et al. [25]      table_white 2017
table_white An introduction to deep learning for the physical layer

table_white Wu et al. [18]      table_white 2018
table_white A survey of physical layer security techniques for 5G wireless networks and challenges ahead

table_white Suomalainen et al. [268]      table_white 2020
table_white ml threatens 5G security

table_white Benzaid et al. [19]      table_white 2020
table_white ai for beyond 5G Networks: A cyber-security defense or offense enabler?

table_white Jiang et al. [269]      table_white 2020
table_white Short survey on physical layer authentication by machine-learning for 5G-based internet of things

table_white Nguyen et al. [270]      table_white 2021
table_white Enabling AI in future wireless networks: A data life cycle perspective

table_white Wang et al. [271]      table_white 2020
table_white Physical layer authentication for 5G communications: Opportunities and road ahead

table_white Al-garadi et al. [272]      table_white 2020
table_white A survey of machine and deep learning methods for internet of things (IoT) security

We believe that an effective categorization for dl-based security studies would be based on the attack types. The most common three attack types (spoofing, jamming, and eavesdropping) are illustrated in Fig. <ref> can be defined as follows.

* Spoofing: In spoofing attacks, the adversary (Eve) aims to imitate the identity of the legitimate transmitter (Alice) such that the receiver (Bob) thinks that the message is sent from Alice.

* Jamming: In jamming attacks, Eve aims to block legitimate communication by introducing jamming signals to the channel such that Bob obtains highly distorted signals.

* Eavesdropping: In eavesdropping attacks, Eve seeks to obtain the message sent from Alice by simply eavesdropping the channel.

In the rest of this section, dl-based PHY solutions against these three fundamental attacks are presented with comprehensive comparisons.

§.§ Anti-Spoofing Solutions

An overview of DL-based anti-spoofing literature (Red hues indicate no USRP/Real-world data and green hues indicate USRP/Real-world data).

table_blue Paper
table_blue DL Structure
table_blue USRP/ Real-world data
table_blue Model Input
table_blue Pros
table_blue Cons

table_white Liao et al. [273]
table_white cnn
table_white CSI vector
table_white MU, comprehensive analysis, good results with small dataset
table_white Require training after coherence time, CSI requirement

table_white Qiu et al. [274]
table_white cnn
table_white Channel estimation matrix
table_white MU, feature extraction
table_white CSI requirement

table_white Liao et al. [275]
table_white dnn cnn PCNN
table_white CSI matrix
table_white MU, comprehensive analysis
table_white CSI requirement

table_white Wang et al. [276]
table_white cnn rnn CRNN
table_white CSI vector
table_white Can capture spectral dependencies, comprehensive analysis
table_white Single-transmitter, CSI requirement

table_white Qiu et al. [277]
table_white cnn
table_white Augmented data-adaptive matrix (formed from CSI)
table_white Non-line-of-sight investigation
table_white Single-transmitter, CSI requirement

table_white Li et al. [278]
table_white Feed-forward NN
table_white Virtual channel vector
table_white mmWave MIMO, feature selection, channel correlation investigation
table_white Single-transmitter, CSI requirement

table_white Liao et al. [279]
table_white dnn
table_white Channel response vector
table_white Data augmentation, good results with extremely small dataset
table_white CSI requirement

In traditional communication systems, authentication algorithms are widely employed as a countermeasure against spoofing attacks. The authentication algorithm relies on the fact that Alice owns a prior information that indicates her authenticity. On the receiving side, Bob uses an algorithm (a detection/classification process) to check if Alice has this information. Although traditional methods are very effective at classifying non-authorized users, delivering prior information to Alice securely or keeping it secure is a highly vulnerable step. In PHY security methods, the role of prior information is transferred to channel or device characteristics that are unique to the location, time, or device. As a result, PHY authentication methods are able to catch attackers even if Alice's credentials are leaked. However, selecting practical features from the channel and designing the detection process is a challenging task. Recently, dl networks are implemented to overcome these challenges and improve detection accuracy. A list of the dl-based PHY authentication methods is given in Table <ref> with their pros and cons.

ml-aided PHY authentication is first introduced by Xiao et al. in [280]. The study considers a common PHY authentication scenario where the receiver applies a binary hypothesis test based on the received signal strength indicator (rssi) values. The decision mechanism first calculates the test statistics, i.e., the normalized distance between the received rssi value and a reference one. Secondly, test statistics are compared with a threshold to decide if the signal is spoofed or not. Here, the selection of the threshold is a critical step that directly affects the detection performance. As the main contribution, the authors propose a  Q-learning-based threshold selection algorithm. The algorithm basically searches the action and state planes to find the threshold that maximizes the Q-function[Q-function calculates the expected reward of an action in Q-learning algorithms. Q-learning or other reinforcement learning algorithms optimize their objective by maximizing a reward value. The reward values are calculated by trial-and-error since the system is unsupervised (i.e., labeled data does not exist). In larger problems, Q-function is often approximated with dl algorithms.]. The authors also investigate the performance of the proposed algorithm using usrp modules. Test results indicate that a dynamic threshold selection with Q-learning outperforms fixed threshold selection. In [281], the authors extend their previous work in [280] to include Dyna-Q learning method. In addition to the results of [280], experiments reveal that Dyna-Q model can improve both learning speed and authentication performance compared to Q-learning. The authors also consider the mimo scenario in [282] by extending the algorithms given in [281]. The results show that both using reinforcement learning and increasing the number of antennas improve the detection performance.

The most significant drawback of [280, 281, 282] is that the receiver is assumed to know reference rssi values for the detection mechanism. This assumption is dropped in [283] by generating a decision result with logistic regression. Specifically, the receiver uses previous RSSI information to train a logistic regression model and estimates the authenticity of the current RSSI value. As a result, the proposed method is able to detect spoofers successfully without prior RSSI information, even under dynamic and unknown channel models. On the other hand, the study considers a network where the receiver obtains RSSI information from multiple spatially distributed nodes (landmarks) with multiple antennas to improve detection performance. In [284], the authors extend their work by reducing the computational overhead of the regression model. usrp experiments verify that the logistic regression-based decision mechanism can successfully detect spoofers under unknown channel models.

dl is introduced to PHY authentication by the authors of [273, 274, 275, 276] simultaneously and independently in 2019. Also, another independent study [277] was published in 2020. In [273], a MU mobile edge network where a node aims to authenticate packets coming from multiple users is considered. The authentication system exploits past csi values that are obtained with pilot transmission to discriminate unauthorized nodes. Instead of applying a traditional hypothesis test, the decision task is given to a cnn. As a challenging task, the receiver requires that the authentication of a node should be within the coherence time of the last csi that is used in the training process. When the coherence window expires, the receiver updates its cnn parameters with a new training process. The authors present a comprehensive investigation of the proposed method where various algorithms are tested for their training speed. Simulation results show successful detection rates even with very small training data sizes. Also, simulation results are verified with usrp modules in a real-world test environment.

Similar to [273], the authors of [274] and [275] consider a PHY authentication method where the receiver node obtains packets coming from multiple nodes. These studies are mainly based on the same idea and differ from each other in terms of cnn structure and performance analysis. In [274], staying in a coherence window is not considered. Instead, the study focuses on the impact of the PHY attributes that are used to extract features. Also, the study includes snr investigation and lacks practical implementation. In [275], the authors implement a dnn, a cnn and a preprocessing added cnn model. The study gives a complete investigation of these three models in terms of computational complexity, training time, transmitter number, hidden layer number, antenna number, and authentication accuracy. Although dnn model gives the best authentication rates, the proposed preprocessing added cnn model achieves close results with less computational complexity. Also, the study presents usrp implementations to verify the practical feasibility of the proposed models.

The authors of [276] consider a three-node network that includes a transmitter, a receiver, and an eavesdropper. Similar to [273] and [274], this study exploits wireless channel features as input of a dnn in order to identify spoofers. This study distinguishes from [273] and [274] by thoroughly implementing and investigating cnn, rnn and convolutional RNN models. The rnn model is shown to be able to capture the spectral dependencies and improve detection performance. Moreover, the convolutional RNN model can further improve the detection performance by cascading cnn and rnn models. This work also proves the superiority of dl on PHY authentication with numerical comparisons to traditional Neyman-Pearson hypothesis testing.

In [277], another dl-based PHY authentication method is proposed. Similar to [276], this model consists of a single legitimate transmitter and exploits channel characteristics. Also, this study considers adding newly obtained csi data to the training dataset as used in [276]. In simulations, this adaptive approach is compared with a non-adaptive scenario (i.e., new data is not added to the training set). The results prove the superiority of the adaptive approach. This work differs from [276] by its dnn structure and its investigation of non-line-of-sight scenarios.

In [278], the authors consider a mmWave mimo scenario where channel characteristics are highly sensitive to spatial location. For this purpose, the authors propose a novel channel feature to improve the detection accuracy of mmWave mimo networks. The proposed channel feature is used in two spoofing detection mechanisms: i) traditional Neyman-Pearson hypothesis testing, ii) feed-forward NN. Hypothesis testing is considered for static channels that have constant channel correlation. The feed-forward NN is considered for dynamic channels with varying channel correlations. It has been shown that the proposed new channel feature can improve detection performance for both static and dynamic channel models. This work differs from previous dl-based approaches by its novel channel feature and its focus on the channel correlation level.

Although we can not compare the models of [273, 274, 275, 276, 277, 278] with the same system parameters, the authors of these works state that their methods can achieve $97\%$ detection accuracy rates at their peak training time.

One of the biggest drawbacks of dl-based models is the requirement of large datasets. It is a known fact that the performance of dl models can significantly drop with limited data. In order to overcome this problem, data augmentation methods have been proposed in dl literature. Data augmentation techniques are mainly based on generating artificial data from the existing dataset. The limited data problem of PHY security dl networks has been considered by Liao et al. in [279]. Liao et al. previously worked on dl-assisted PHY authentication in [275]. In [279], the authors argue that wireless networks are especially vulnerable to limited data as a result of channel coherence time. Hence, the authors propose three data augmentation methods to improve the training speed and authentication accuracy. Also, the authors perform experiments on real-world datasets to verify their results.

The majority of studies in the literature consider the application of dl at the legitimate side (i.e., to improve security). In [285], the authors take the opposite view and investigate dl-assisted spoofing attacks. In particular, this study focuses on ofdm systems that are deemed secure against spoofing attacks. Simulation results show that data-driven spoofing attacks are able to effectively disrupt ofdm systems that are traditionally considered secure.

DL-based anti-spoofing methods are proved to be highly beneficial when compared to traditional threshold-based detection mechanisms. DL models learn the best decision mechanism from data when traditional methods suffer from the problem of threshold selection. An inaccurate threshold selection in a mathematical detection model can easily lead to high miss detection or false alarm rates. Moreover, DL-based methods in the literature are able to deal with large networks where authentication of multiple nodes is required. Creating a DL model that classifies multiple nodes simultaneously is possible. On the other hand, the greatest challenge of DL-based anti-spoofing methods is to obtain information on the spoofer. DL-based methods require training data to generate a model that can distinguish between a spoofer and a legitimate user. However, including any information from the spoofer to the training data is not practical. Unfortunately, current studies include the spoofer's channel in the training process to train their models. This assumption is a critical drawback on implementing DL-based methods in real scenarios.

§.§ Anti-Jamming Solutions

An overview of DL-based anti-jamming literature.

table_blue Paper
table_blue DL Structure
Model Input
table_blue Pros
table_blue Cons

table_white Han et al. [286]             table_white cnn-based DQN
table_white State matrix
table_white Applicable to any channel model
table_white Limited jammer model, no real-world data

table_white Liu et al. [287]             table_white Recursive cnn-based dqn
table_white Spectrum sensing matrix
table_white Improved jammer model, raw data as feature, cost of frequency hops are included
table_white More training time as a result of raw data, no real-world data

table_white Bi et al. [288]             table_white CNN and LSTM-based Double DQN
table_white User ID, position, channel information
table_white More stable than DQN
table_white No real-world data

table_white Xu et al. [289]
table_white Transformer encoder-based Double DQN
table_white Spectrum sensing matrix
table_white More stable than DQN, improved throughput
table_white No real-world data

table_white Tingpeng et al. [290]
table_white cnn
table_white Signal matrix from electronic information system
table_white Can identify three jammer types
table_white Limited analysis, no real-world data

table_white Cai et al. [291]
table_white Le-Net5 cnn
table_white Spectrum sensing matrix
table_white Can identify multi-tone jammers
table_white Limited analysis, ideal jammer model, no real-world data

table_white Liu et al. [292]
table_white cnn
table_white Spectrum sensing matrix
table_white Both jammer identification and frequency hopping strategies are considered, cost of frequency hops are included, comprehensive numerical analysis
table_white No real-world data

Jamming attacks can dramatically disrupt the communication between Alice and Bob since Alice's signals easily become unrecognizable when superimposed with the jamming signal. Traditionally, spread spectrum techniques are implemented to combat jammers. Frequency hopping schemes aim to dodge jammers by continually changing the communication frequency. Direct sequence spread spectrum methods spread the communication to a larger bandwidth to reduce the impact of the jamming signal. Designing an efficient frequency hopping pattern is an open issue and is considered by many researchers. In the last decade, various efforts have been made to leave the design step to a dl network. Since continuously dodging a jammer over a spectrum is a sequential game where Alice makes its hops depending on new observations, reinforcement learning is a powerful candidate to improve performance. Table <ref> contains several dl-based anti-jamming studies that exist in the literature with their pros and cons.

One of the first examples of learning-based anti-jamming approaches can be seen in [293]. The study considers a wireless network that consists of two competing teams. Each team consists of a jammer and a receiver node, where each jammer node tries to interrupt the communication of the opposing team's receiver while each receiver node aims to avoid the jammed frequencies. The authors propose various Q-learning approaches to select the frequency hops of a receiver node and the target frequencies of a jammer node. The proposed approach assumes that each node has spectrum sensing capabilities and each team has control channels for receiver-friendly jammer communication. The authors investigate the performance of Q-learning methods against conventional methods and each other.

The accuracy of the Q-learning-based detection results can be insufficient in high-dimensional data, and the learning speed can increase dramatically. In order to capture non-linear relations in the data and increase both learning speed and accuracy, dl-based jamming detection models have been proposed in [286, 287, 288], and [289]. These studies essentially focus on dl techniques to find an efficient frequency hopping strategy. In [286], a cognitive radio network is considered where the secondary user applies a deep Q-learning network (dqn) to avoid jammed frequencies while not interfering with the primary users. The proposed model uses Q-learning to decide on the jammed frequencies and select the appropriate frequency band. The Q-function is calculated with a cnn to improve accuracy and time complexity. Q-learning states are calculated based on the received SNIR values (features extracted to avoid jammed frequencies) and the primary user occupation information. The proposed method does not require knowledge of the jammer or channel model. As a result, it is applicable to any environment. Computer simulation results show that cnn-based Q-learning yields higher learning rates than traditional Q-learning methods. Also, the receiver attains higher SNIR values.

A similar anti-jamming approach is considered in [287]. Contrary to [286], the method does not assume that the jammer follows the same transmission slot structure of the legitimate communication. Also, this method uses raw spectrum information to feed cnn instead of using extracted features. In [288], Q-function is approximated with double deep Q-learning. [288] can be distinguished from [286, 287] by their double dqn choice instead of dqn. Although double dqn is shown to be more stable than dqn in the literature, [288] only compares their method with traditional Q-learning and lacks any comparison with dqn. As a strong side, the authors implement three types of networks: fully connected network, cnn and long-short-term-memory. The authors of [289] improve the results of [288] by implementing double dqn with a more efficient network model. Specifically, the authors exploit transformer encoder to implement double dqn and obtain improved results compared to cnn-based double dqn. Unlike previous studies, in [294], dl is used for receiver design. Specifically, a dl algorithm is implemented at the receiver of a continuous phase modulation scheme in order to improve the prediction of the received signals under single or multi-tone jamming attacks. Numerical results showed that dl assisted receiver can improve ber performance of the system by $3-5$ dB under single-tone jamming attacks.

Detection of the jammer type is an important step for anti-jamming communications. Instead of focusing on avoiding the jammed frequencies (which primarily considers Q-learning), the detection of jammer type focuses on identifying jammer features. Conventional model-based methods first convert raw information into features using mathematical models and solve a classification problem to identify jammer types. Applying dl in this process can significantly overcome the inaccuracies of mathematical models. In [290], the authors proposed a cnn-based jammer identification method which imitates the image processing applications of dl. This method first converts one-dimensional data into image format in order to train the network. Although simulation results indicate $92\%$ accuracy for identifying three jammer types (single-tone jammers that use Gaussian noise with different mean and variance), this study lacks comparison with any benchmark model. An improved detection model is proposed in [291] where a simplified Le-Net5 cnn model is used. In the performance evaluation, the authors include multi-tone jammers. The method achieves $92\%$ accuracy (which is similar to [290]), yet the authors remark that the method attains a higher learning speed. As major drawbacks, this study lacks comparison with a benchmark model and assumes an ideal jamming pattern, i.e., the jammer can instantaneously shift frequencies.

The above studies mainly focus on either avoiding the jammer with frequency hopping or identifying the jammer type. In [292], the authors focus on both concepts by firstly identifying the jammer type and then proposing an efficient frequency hopping pattern. The proposed method firstly sweeps the time domain and captures multiple frames of spectral energy (i.e., two-dimensional spectral energy information in a time interval). The frames are fed into a cnn to identify the jammer type. In the next step, traditional Q-learning is used to provide a frequency hopping pattern. As an important contribution, the authors also consider the cost of frequency hops in the system model.

An overview of the dl-based anti-jamming literature is presented in Table <ref>. These studies mainly focus on improving the reinforcement learning model with dl assistance. Based on our investigations, we conclude that dl can improve the frequency hopping accuracy of anti-jamming systems and bring robustness against unbalanced environments. Although most of these studies include extensive computer analyses, the literature heavily lacks proof of concepts or testbed implementations with real-world datasets.

§.§ Anti-Eavesdropping Solutions

An overview of DL-based anti-eavesdropping literature.

table_blue Paper
table_blue DL Structure
table_blue Model Input
table_blue Pros
table_blue Cons

table_white Fritschek et al. [295]
table_white ae
table_white One-hot-encoded message vector
table_white Provides a trade-off between secrecy and ber, Eve has dl abilities
table_white Bob has a better channel than Eve, signal alphabet is infinite, no real-world data

table_white Sun et al. [296]
table_white cnn-based ae
table_white One-hot-encoded message vector
table_white Include an authentication model
table_white Limited analysis, no real-world data

table_white Besser et al. [297]
table_white ae
table_white Message vector
table_white Provides a trade-off between secrecy and ber, BPSK signals are more practical, extensive theoretical and numerical analysis
table_white No real-world data

table_white Zhang et al. [298]
table_white Fully-connected nn
table_white Channel matrices of Bob and Eve
table_white mimo network
table_white No real-world data

table_white Zhang et al. [299]
table_white nn
table_white Channel matrices and error bounds
table_white Applicable to cognitive radio networks (can be sensitive to primary users), can work without csi, thorough investigation
table_white No real-world data

table_white Li et al. [300]
table_white lstm, echo state network
table_white Position vectors and transmit antenna vectors
table_white Applicable to D2D communication, energy efficient
table_white No real-world data

Encryption/decryption methods are the conventional countermeasures against eavesdropping attacks. An encryption method requires secret information (key) shared by only Alice and Bob. The security of an encryption method is based on the assumption that Eve is unable to decrypt the message without the key. However, this assumption can be falsified with high computational power. Moreover, distributing the key to Alice and Bob without leaking it to Eve is a major drawback of the conventional encryption methods. Instead of hiding the message inside a cipher, PHY security methods aim to prevent Eve from correctly obtaining the messages at phy. For this purpose, PHY security methods focus on various techniques such as coding, BF, or artificial noise to nullify Eve's channel. However, designing an effective model for these techniques is a challenging task and still draws the attention of many researchers. In the past years, various dl-assisted models have been proposed in the literature to improve the secrecy rate. A list of the reviewed dl-based anti-eavesdropping methods is given in Table <ref> with their pros and cons.

e2e learning with aes is an emerging concept that can significantly improve communication rates under unpredictable environments. The success of aes on communication drew the attention of wireless security researchers in the past years. A direct implementation of aes for the secure communication purpose is presented in [295]. The main idea of the study is to include a security objective to the loss function of an ae. In other words, the loss function does not maximize only the legitimate channel; it also aims to minimize the eavesdropper's channel. However, this objective as the difference between Bob and Eve's channel capacity is hard to compute. For this reason, the authors implement this objective as a modified version of the difference in cross-entropy losses. The method uses clusters that contain redundant symbols to confuse Eve. The proposed loss function enables the users to choose the level of the information loss in the legitimate channel (and the leaked information to Eve as a trade-off). As a strong side, Eve is considered to have nn abilities. The main drawback of the study is that Eve is assumed to have more noise than Bob. Another secure ae model is proposed in [296]. Similar to [295], the authors consider the design of a secure loss function based on cross-entropy. As an addition, [296] also implements an authentication method into the ae.

In [297], the authors use dl to generate wiretap codes. The main idea behind the study is to include the error rates of the eavesdropper into the loss function of the ae. The study successfully analyzes the problems of implementing this idea and presents a thorough mathematical basis. Also, the design allows a trade-off between information leakage to the eavesdropper and ber of the legitimate receiver. The authors also verify their theoretical results with computer simulations by comparing their scheme with polar wiretap codes. A similar approach is also considered in [301]. Contrary to [297], this study constrains their alphabet to bpsk and uses a different loss function.

A secure communication model for MIMO networks is proposed in [298]. Instead of focusing on e2e communication, the proposed dnn aims to find the optimum covariance matrix of the input signal. The dnn is fed with a large dataset containing input channels and covariance matrices in offline training. Compared to conventional analytical solutions, dnn-based covariance matrix approximation reduces the time-complexity. Also, it is a practical solution to power and time-restricted applications since the computational load is at the offline training stage.

dl-aided secure communication for cognitive radio networks is first considered in [299]. In addition to the traditional secure communication objective function, the proposed method includes two additional constraints: i) transmit power of the secondary user should be under a threshold, ii) leaked interference to the primary user should be under a threshold. The authors exploit a dl network to approximate the best transmit power allocation scenario for secure transmission with given cognitive radio constraints. Also, the method is able to find a solution to the optimization problem even without csi. This study thoroughly compares the proposed method with conventional optimization techniques for their secrecy rate performance, leaked information to the primary user, and computation time. Their results show that dl can heavily reduce the computational time and complexity without any significant performance loss.

In [300], D2D communication networks are considered. The authors propose a dl-assisted algorithm, which selects the transmit antennas and the device pairs in order to prevent pairing with eavesdroppers.

Future directions for DL-based PHY techniques in four primary research items: massive MIMO, advanced waveform designs, RIS-empowered systems, and PHY security.

dl-based anti-eavesdropping studies are listed in Table <ref>. Most of these works are based on utilizing aes to find secure encoding or BF schemes. Since traditional methods are model-based, they are highly vulnerable to unexpected variations in the environment. On the other hand, dl-based anti-eavesdropping literature proves that the data-driven nature of dl networks can significantly improve the robustness against unexpected variations.

One of the challenges that DL literature faces is to include the eavesdropper's channel capacity in the loss function. Contrary to the spoofing or jamming scenarios, DL-based anti-eavesdropping methods do not face a classification problem. Essentially, the DL models are expected to optimize their variables for the secrecy rate metric which includes the eavesdropper's channel capacity. However, known DL models and loss functions are not fully applicable for this task. As a result, unique DL models are still needed to improve the performance of DL-based methods. Similar to anti-jamming literature, we should remark that anti-eavesdropping studies lack proof of concepts and testbed implementations with real-world datasets. Moreover, dl-assistance is applied to a limited number of communication scenarios, i.e., ris or ofdm scenarios are missing. The following section dwells on the future aspects of our four leading research directions and presents our comments.

§ CONCLUSIONS AND FUTURE DIRECTIONS

As we have often stated, we are on the verge of a potential revolution in wireless communications. The deployment process of the 5G technology has come a long way all over the world these days and the active research on the 6G technology has also gained tremendous momentum. It is an undeniable fact that 6G technology will usher in radical paradigm shifts, new thrilling applications, and groundbreaking technologies. AI will undoubtedly play a huge role in this irresistible revolution emerging with 6G. Specifically, DL approaches have been building a concrete ground and will most likely continue to overtake other AI branches for the bright future of wireless communication technologies. In this article, we have provided a comprehensive overview of the existing DL-based PHY techniques and shed light on the huge potential of emerging DL approaches under four main research directions toward 6G, namely massive MIMO systems, MC waveform designs, RIS-aided communications, and PHY security. We have analyzed the progress made so far in detail and determined major future research directions in each of these fields, as demonstrated in Fig. <ref>. Please note that it is possible to add other phy research directions to the framework that we have covered in this article. In fact, the usage of DL grows gradually on other phy technologies. For instance, the literature also includes dl-powered studies on mmwave or terahertz communications. It has been shown that dl can be employed to efficiently solve various challenges of high-frequency communications. Moreover, dl is also applied for the challenges of drone or unmanned aerial vehicle communication networks. However, the dl literature on these technologies is still developing, and including them in our 6g framework is a future direction.

§.§ Future Directions on Massive MIMO Systems

We have investigated the major developments of DL applications for MIMO systems from various aspects and identified the missing points that should attract more attention. The first issue from an intelligent receiver perspective is that most detectors available in the literature require perfect CSI to operate successfully. Since it is challenging to estimate channels in practice and even harder in massive MIMO systems due to their plenty of transmit antennas, developing a system model with a perfect CSI assumption remains a pipe dream. Therefore, receiver frameworks based on DL techniques composing channel estimators and symbol detectors should become more common in the future to support practicability. Secondly, evaluating the proposed models in a computer simulation environment using generic channel model assumptions may be deceptive since there are many more impairments in real-life scenarios, and we do not know how they impact the performance of DL models. Thus, we suggest creating benchmark channel datasets that include real-life impairments and encapsulate diverse settings for various wireless communication standards, such as 5G and Wi-Fi 7. These benchmark datasets will allow for fair comparisons of DL models. Apart from these, the third vital topic for rapid progress in this field is the development of benchmark system models that researchers can compare their DL models. The number of novel DL frameworks has been increasing in tandem with the advancement of the literature. Hence, new DL-based frameworks should be compared to existing novel data-driven methods to prevent the conglomeration of similar models.