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Sec. <ref> - Localization & Positioning (L & P)

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A- Data Augmentation
& Imputation

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B- RF Fingerprinting
Aided L & P

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Tensor-GAN to enhance localization accuracy & reduce storage size <cit.>

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Generate missing fingerprints by predicting residual RSS via random sampling <cit.>

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Automating radio map creation for target intrusion sensing and localization <cit.>

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Generating fake fingerprints from a small dataset to tackle data insufficiency <cit.>

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Generation and selection of fake RSSI data to cover the entire indoor environment <cit.>

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Semi-supervised indoor localization approach for generating fake fingerprints <cit.>

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Generate synthetic RSSI to boost offline database density <cit.>

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Adaptive indoor tracking via  recurrent & GMs for data generation and recovery  <cit.>

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Using adaptive-depth ray-tracing to construct synthetic radio maps for intrusion sensing and loc. <cit.>

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Offline database construction cost by learning RSSI distribution and generating synthetic values <cit.>

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Improving feature maps generation and training to enhance localization accuracy  <cit.>

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Increasing feature diversity by conv. CSI to feature maps <cit.>

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Reference data imput. to synthesize dense fingerprint maps <cit.>

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Generate artificial fingerprints for improved loc. accuracy and reduced comput. resources <cit.>

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A transform-based 3D tensor to model fingerprint samples and a tensor-based super-resolution scheme for improved loc. <cit.>

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Semi-supervised approach reducing labeling cost  while achieving SL performance <cit.>

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CNN+LSTM+GAN based approach for augmentating training data and minimizing positioning error <cit.>

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Reducing costly offline training phase by generating new RSSI and localization models <cit.>

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Minimizing site survey time/cost and mitigating signal fluctuations for fingerprint-based IoT positioning <cit.>

GenAI applications for localization and positioning

§ GENAI FOR LOCALIZATION AND POSITIONING

GMs have also been applied to a variety of localization and positioning problems, including enhancing indoor localization, addressing WiFi localization issues, automating radio map creation, data augmentation for cost-effective localization, and improving indoor tracking. Furthermore, researchers have employed GMs to address challenges in RF fingerprinting-based localization, such as extending fingerprint databases, generating synthetic fingerprint maps, modelling fingerprint samples, and providing low-cost labeling, which are all discussed throughout this section.

§.§ DATA AUGMENTATION, IMPUTATION, AND MAP GENERATION

4 Tensor-GAN for Enhancing Indoor Localization:
In the realm of mobile computing and social networking, accurate localization in indoor environments is a critical necessity. The complexity of indoor environments, however, poses significant challenges to the traditional RF fingerprint-based localization methods, including limited sample availability, storage constraints, and real-time response requirements. To address these challenges, Liu et al. <cit.> devised a tensor GAN (Tensor-GAN) capable of enhancing localization accuracy and reducing storage consumption. This innovative scheme, incorporating a unique three-player game involving a regressor, a generator, and a discriminator, demonstrated impressive improvements in localization accuracy.

4 ACO-GAN for Addressing WiFi Localization Issues:
The intensifying demand for location information services underpins the attractiveness of WiFi-based indoor localization. A pressing issue associated with these technologies, however, is the time-consuming task of manually updating the WiFi fingerprint database due to environmental changes affecting signal propagation. To tackle this problem, Ren et al. <cit.> proposed the Adaptive Context GAN model, which efficiently rebuilds missing fingerprints by predicting residual received signal strength indicator (RSSI) values based on random sampling, thereby resolving the remeasurement problem.
4 RaGAM for Automating Radio Map Creation:
The use of indoor target intrusion sensing techniques is becoming prevalent in various fields, such as smart home management, security monitoring, and disaster relief. In response to the immense labor and time costs associated with traditional radio map construction, Zhou et al. <cit.> introduced the ray-aided generative adversarial model (RaGAM) to automate radio map creation for WLAN-based target intrusion sensing and localization. The RaGAM model utilizes adaptive-depth ray tree-based quasi three-dimensional ray-tracing, which results in a lower computational cost and increased accuracy.
4 Data Augmentation for Cost-effective Localization:
Despite their benefits, ML techniques require large amounts of data for training DNN models, which is often a costly endeavor. To address this challenge, Njima et al. <cit.> proposed the use of GANs to generate synthetic fingerprints based on limited real data samples. This data augmentation approach, combined with semi-supervised learning, demonstrated notable improvements in localization accuracy. Njima and colleagues addressed the challenge of data insufficiency in indoor localization by utilizing GANs to generate fake fingerprints from a small dataset <cit.>,  leading to a mean localization accuracy improvement of 9.66% in comparison to the conventional semi-supervised localization algorithm. They expanded upon this in subsequent studies, proposing a method for the generation and selection of fake RSSI data to cover the entire indoor environment, which improved localization accuracy  by 21.69% and 15.36% for simulated and experimental data, respectively <cit.>. Their latest work introduced a weighted semi-supervised DNN-based indoor localization approach and a system for generating fake fingerprints, called Weighted GAN based indoor localization <cit.>. This system trained a DNN on a mixed dataset of real and fake data samples, avoiding overfitting and enhancing location prediction performance by 8.53-10.11%. This set of studies collectively demonstrated significant improvements in localization accuracy compared to conventional supervised localization schemes.

4 VAEs for Boosting Offline Database Density:
DL techniques have been increasingly used for indoor localization, primarily for fingerprinting-based methods. The major drawback of these techniques is the high cost and effort associated with building a large offline database. To alleviate this, Suroso et al. <cit.> implemented VAEs, a popular deep GM, to learn the distribution of RSSI and generate synthetic RSSI values. This novel approach enhanced the density of the offline database grids, despite some challenges with the accuracy of synthetic RSSI values.

4 RecTrack-GAN for Improved Indoor Tracking:
The intricate signal propagation effects in indoor environments make accurate indoor positioning and tracking a daunting task. Leveraging advances in ML and DL models, Alberto et al. <cit.> proposed an innovative framework for adaptive indoor tracking using recurrent models, in conjunction with GANs for data generation and recovery (RecTrack-GAN). This approach improved the accuracy of tracking and reduced error deviation by up to 15% compared to previous methods.

4 Automated Indoor Intrusion Sensing with RaGAM: Zhou et al. propose the ray-aided generative adversarial model (RaGAM) for efficient WLAN-based indoor intrusion sensing and localization, leveraging adaptive-depth ray tree-based ray-tracing to construct synthetic radio maps and joint synthetic and unsupervised learning to refine these maps using unlabeled RSSI data. The refined map is used to train a probabilistic neural network (PNN) for classifying new RSSI data, leading to accurate intrusion sensing and localization <cit.>.

4 Indoor Localization Improvement with VAEs: Suroso et al. propose using VAEs to reduce the effort and cost of constructing large offline databases for fingerprinting-based indoor localization. They use VAEs to learn the distribution of the fluctuating RSSI signals and generate synthetic values, which shows potential for enhancing database density. However, the accuracy of the synthetic values is lower with smaller numbers of training epochs, suggesting a need for larger datasets and adjustments in the assumed RSSI-to-image size <cit.>.

§.§ RF FINGERPRINTING FOR  LOCALIZATION

4 AF-DCGAN for Fingerprint Database Extension:
Li et al. pioneered a method to extend fingerprint databases using a Wavelet Transform-Feature Deep Convolutional Generative Adversarial Network model, which accelerated the training phase and diversified generated feature maps, improving indoor localization accuracy <cit.>. In a follow-up work, they proposed an Amplitude-Feature Deep Convolutional GGAN  model, which converted collected CSI data into amplitude feature maps, significantly increasing CSI amplitude feature map diversity <cit.>.

4 Generating Synthetic Fingerprint Maps:
Lim et al. devised a method to minimize the number of reference points needed for Wi-Fi localization. Utilizing a GAN, they interpolated the values between reference points and synthesized a dense fingerprint map, which led to improved localization accuracy <cit.>. Meanwhile, Wei et al. suggested using a single AC-GAN to generate artificial fingerprints, achieving improved localization accuracy and reducing computational resources compared to multi-GAN approaches <cit.>.
4 3D Tensor for Modeling Fingerprint Samples:
Zhu et al. tackle the challenge of indoor localization for smartphones where GPS fails, by proposing a novel DL architecture, namely the tensor-GANs <cit.>, that uses a transform-based 3D tensor to model fingerprint samples and employs DL for network classifiers. Additionally, a tensor-based super-resolution scheme is introduced using a GAN, which improves localization accuracy, response time, and implementation complexity, outperforming existing, energy-intensive methods.

4 Device-Free Fingerprinting for Low-Cost Labeling:
Chen et al. introduce a semi-supervised, GAN-based device-free fingerprinting system for indoor localization, an essential technology for the IoT <cit.>. The proposed system leverages a minimal amount of labeled data and a larger pool of unlabeled data, thereby substantially reducing the need for costly data labeling. Compared to the current supervised schemes, this semi-supervised system offers comparable performance with equal amounts of labeled data, and superior performance with limited labeled data. The paper also provides a mathematical description of the system and discusses the interactions between its GAN components.

4 Enhanced Indoor Positioning with CNN, LSTM, and GAN: Darwin et al. propose a novel architecture combining CNN, LSTM, and GAN to improve indoor positioning accuracy <cit.>. This integrated approach, combining supervised and unsupervised ML models, aims to increase training data and minimize positioning error. Tested across 17 public datasets, the proposed model demonstrated a substantial reduction in positioning error in more than 70% of the datasets.

4 LTE-A Signal-Based Localization with GAN: Serreli et al. propose a fingerprint-based localization approach leveraging LTE-Advanced Pro Signal and ML techniques <cit.>. The approach analyses the RSSI between the base station and user equipment, using a GAN to understand the correlation between RSSI and the user's position. The authors address the typically costly and time-consuming offline training phase in fingerprinting-based localization by using the GAN to generate new RSSI and localization models. Experimental results indicate a significant improvement in accuracy compared to conventional methods, achieving an impressive 0.4 meters and 10 meters accuracy in indoor and outdoor environments, respectively, while saving time in the measurement campaign.

4 Fingerprint-Based IoT Positioning Using DCGANs: Tarekegn et al. propose a novel method of fingerprint-based positioning for IoT applications, utilizing DCGANs to minimize site survey time and cost, and to mitigate signal fluctuations <cit.>. The radio-map designed for receiving signals from WLAN and cellular networks in scalable environments is constructed using a sequential combination of a hybrid support vector machine and long short-term memory algorithms. Experimental results suggest that the proposed method provides promising and reasonable positioning performance for IoT devices operating in scalable wireless environments.

§.§ OWC LOCALIZATION AND POSITIONING

4 USL for LiFi Positioning in Indoor Environments: Hua et al. study the potential of light-fidelity (Li-Fi) indoor positioning systems due to its high accuracy, robustness, and absence of electromagnetic interference <cit.>. However, they note that DL models used for Li-Fi positioning are susceptible to device heterogeneity and setup deviations, leading to poor performance during the testing phase. To address this issue, the authors propose an USL method based on adversarial training for visible light network regression. The proposed method employs a feature extractor to align feature distributions from different domains and a domain classifier to differentiate data from the training and test sets. The adversarial network model outperforms directly applying a trained model to the test set, especially under multi-dimensional attack conditions.

4 Pedestrian Localization in V2X Networks: Liu et al. propose a GAN architecture to enhance the accuracy of pedestrian location estimations in Smart City and V2X systems <cit.>. Recognizing the challenges posed when multi-modal data is not associated, such as when pedestrians are outside the camera field of view or camera data is missing, their GAN learns the underlying linkage between pedestrians' camera-phone data correspondences during training, and generates refined position estimations based only on pedestrians' phone data during inference. The GAN shows promising results, producing 3D coordinates with a 1-2 meter localization error across various outdoor scenes. The authors also demonstrate that the proposed model supports self-learning, improving localization accuracy by up to 26% after fine-tuning on an automatically expanded dataset.

§.§ SUMMARY, INSIGHTS, AND FUTURE DIRECTIONS

This review has delved into the advancements and challenges in the realm of localization and positioning, particularly focusing on the potential and implications of ML techniques. In particular, GANs and VAEs have emerged as effective tools for enhancing indoor and outdoor localization, tracking, and data augmentation. These methods have significantly addressed the limitations of traditional RF fingerprint-based localization systems, such as storage constraints and limited sample availability.

GAN variations have displayed promising results in indoor localization and radio map automation. VAEs have been instrumental in increasing the density of offline databases. Data augmentation methods have shown substantial improvements in localization accuracy. Advanced models incorporating CNN, LSTM, and GANs offer integrated solutions that minimize positioning errors. In the domain of Li-Fi, adversarial training approaches have also been successful in tackling issues related to device heterogeneity and setup deviations.

While GMs have overcome several hurdles, they also introduce new challenges such as the high cost of data collection and susceptibility to setup deviations. Additionally, there is a need for more rigorous evaluation of these methods in real-world settings to validate their performance and applicability. Several avenues for future research are evident:

72 Data Efficiency: Further research could focus on developing models that require fewer training data but still deliver high localization accuracy.

72 Robustness: Studies could explore the stability of these models in dynamic indoor and outdoor environments with fluctuating signal conditions.

72 Scalability: Investigating the ability of these models to scale across different types of environments and use-cases is crucial for their broader applicability.

72 Multi-modal Fusion: Future work may also delve into the fusion of data from multiple sensors or networks to improve localization and tracking.

72 Cost-Effectiveness: Addressing the high costs associated with data collection and labeling can open the door for more cost-effective solutions.

72 Security and Privacy: As localization technologies become more pervasive, research into preserving user privacy and data security will become increasingly important.

In conclusion, advancements in GMs offer promising solutions to long-standing challenges in localization and positioning, setting the stage for more reliable, efficient, and versatile systems in the years to come.

§ NEW 6G FRONTIERS FOR GENERATIVE MODELS

Based on the GM tutorial presented in Sec. <ref> and literature on GenAI wireless networking use cases discussed through Sec. <ref>-Sec. <ref>, this section will share our vision on how GMs can be utilized for the new research frontiers of 6G networks.

GM Roles in Semantic Communications.

GMs     Semantic Roles     Description

Natural Language Understanding     Advanced interfaces for querying databases.

Contextual Analysis     Ensure transmitted data fits within receiving entity's context.

Semantic Compression     Generate concise summaries or coded representations.

Intent Prediction     Predict future user interactions for proactive data transmission.

Data Synthesis     Generate synthetic data for training.

Image Translation     Convert data into easier-to-interpret forms.

Adaptive Modulation     Adaptively modulate data signals.

Semantic Compression     Compress high-dimensional data into a latent semantic space.

Context-Aware Messaging     Produce a compressed semantic state for various applications.

Data Enhancement     Improve or restore signal quality.

Security     Employ for steganography, hiding information.

Variable Rate Communication     Adapt to varying degrees of data complexity.

Real-Time Adaptability     Provide real-time updates.

Efficient Encoding and Decoding     Maintain semantic integrity in limited bandwidth.

Probabilistic Reasoning     Provide probabilistic estimates for decision-making.

§.§ SEMANTIC COMMUNICATIONS

Semantic communication is poised to be a cornerstone technology in the development of 6G networks. Unlike traditional communication paradigms that prioritize the transmission of raw bits with minimal distortion or data loss, semantic communication aims to transmit "meaningful" data and considers various factors such as context, prior knowledge of the receiver, and the purpose of the transmission must be considered. This nuanced approach positions semantic communication as a critical component of future 6G networks, which are expected to leverage GenAI to understand, interpret, and act upon vast heterogeneous data streams. In the rest, we provide insights into how GMs can facilitate semantic communications by fulfilling various functions depending on their inherent capabilities explained in Sec. <ref>.

§.§.§ Contextual Analysis, Semantic Compression, and Intent Learning

LLMs play a pivotal role in revolutionizing the landscape of semantic communications, bringing about an era of sophisticated, intelligent, and context-aware data exchanges. LLMs have also envisioned to be the cornerstone toward realizing self-governed, interactive AI agents that understands telecom language <cit.>, which will eventually lead us into collective intelligence rather than previous concept of connected intelligence <cit.>. Moreover, LLMs can be designed as a multimodal large model trained over Telecom data and fine-tuned to perform several downstream tasks, which can eliminate the need for dedicated models and realized artificial general intelligence (AGI)-empowered wireless networks <cit.>.

Firstly, by harnessing the power of contextual analysis <cit.>, these models ensure that the data being transmitted is not just a mere collection of bits and bytes but is curated to fit seamlessly within the context of the receiving entity, enhancing the relevance and meaningfulness of the data exchange. Another transformative capability is semantic compression <cit.>, where these models can distill vast amounts of information into concise summaries or coded representations, preserving the essence while optimizing for efficiency. This not only economizes on the resources but also facilitates quicker and more effective comprehension. Furthermore, intent learning and prediction can foresee future user interactions <cit.>. This proactive approach allows the system to preemptively transmit data based on predicted needs, ensuring timely availability and enhancing user experience. Lastly, with their capabilities in natural language understanding <cit.>, LLMs pave the way for advanced interfaces that can be employed for querying databases. This means rather than using structured queries, users can pose questions in natural, conversational language, and the system would comprehend and fetch the desired data.

§.§.§ Data Synthesis, Image Translation, and Adaptive Modulation:

GANs have emerged as powerful tools that are reshaping the fabric of semantic communications, making the process not only more effective but also adaptive and insightful. Inherent data synthesis feature of GANs proves invaluable for training, allowing communication systems to be fine-tuned and optimized without the need for vast amounts of real-world data, which might be difficult to obtain or sensitive in nature. Moreover, GANs can adeptly convert complex or esoteric data forms into more accessible, easier-to-interpret visual representations, ensuring that the transmitted information is not just received, but also easily comprehended by the receiving end. For instance, image translations facilitate better understanding and can bridge the gap between raw data and actionable insights <cit.>. And in the dynamic and often unpredictable realm of data transmission <cit.>, GANs can adaptively modulate data signals based on the receiving environment, ensuring optimal transmission quality by dynamically adjusting to real-time conditions, ensuring data integrity, minimizes loss, and optimizes resource usage.

§.§.§ Context-Aware Messaging and Latent Space Compression:

VAEs are at the forefront of transforming the dynamics of semantic communications with their unique capabilities tailored to ensure meaningful data exchange. Unlike traditional compression mechanisms, VAEs can efficiently compress high-dimensional data into a latent semantic space, capturing the underlying essence or meaning of the data rather than just its raw form <cit.>. This compression is not about just reducing size but ensuring that the compressed data carries the most important and semantically rich information.  Moreover, VAEs are adept at encapsulating complex data scenarios into a compressed semantic state, which can be more efficiently transmitted across systems and be beneficial for various applications ranging from real-time analytics to context-sensitive response systems. By understanding the broader context, these models ensure that the transmitted data is not just a series of values, but a meaningful representation tailored for specific application needs <cit.>

§.§.§ Data Enhancement, Steganography, and Variable Rate Communication:

DGMs can bring a novel approach to semantic communications, addressing various challenges inherent to data transmission and interpretation. Primarily, DGMs possess the capability to refine or restore signal quality, ensuring the meaningful components of transmitted data remain undistorted even under suboptimal conditions <cit.>. This attribute proves invaluable in scenarios where signal degradation might obscure the semantic essence of the communicated data. On the security front, DGMs introduce a paradigm shift by enabling steganography—the art of hiding information within other non-secret data <cit.>. This not only ensures the concealment of sensitive data but also its seamless transmission without raising suspicions. Such covert communication modes elevate the security standards of semantic communications, ensuring that the intended message reaches its destination without external interference. Lastly, DGMs cater to the dynamic nature of communication by facilitating variable rate communication. Recognizing the varying complexities of data, DGMs can adapt their transmission rates accordingly, ensuring efficient data flow without overwhelming the communication channels. In this way, DGMs may ensure that the richness of semantics is relayed appropriately, regardless of the intricacy of the underlying data.

§.§.§ Real-Time Adaptability, Efficient Encoding and Decoding, and Probabilistic Reasoning:

FGMs are poised to redefine the landscape of semantic communications, drawing from their unique strengths in modeling complex data distributions. Unlike other GMs, FGMs excel in generating samples and adapting their parameters in real-time, making them suitable for scenarios where information changes rapidly and systems must update their understanding promptly <cit.>. This adaptability ensures that semantic communications remain current and reflective of the most recent data or context. By leveraging the bijective nature of flow models, FGMs can seamlessly transform high-dimensional data into compact representations without significant loss, ensuring that the semantic essence of information remains intact even in environments with constrained bandwidth <cit.>. This efficient data transformation is crucial for 6G networks, which will handle massive heterogeneous data from various devices. Lastly, by capturing the underlying data distribution, FGMs can offer probabilistic estimates about uncertain scenarios, aiding decision-making processes <cit.>. This is pivotal in semantic communications, where understanding the likelihood of various interpretations can greatly enhance the reliability and clarity of transmitted information. The GM roles and their brief description is summarized in Table <ref>.

GM Roles in ISAC

ISAC Roles            Role Descriptions                                                                                GMs
Data Augmentation
Creation of synthetic yet realistic sensory data to augment existing real-world data for robust ML training.
GANs, VAEs
Anomaly Detection
Identification of data points or patterns that deviate significantly from expected behavior, essential for early warning and reliability.
GANs, FGMs, DGMs
Resource Optimization
Simulation of various network conditions and prediction of future states for optimal resource allocation.
GANs, FGMs
Time-Series Forecasting        Prediction of future sensor readings based on past and current data, useful for proactive decision-making.              LSTMs, GMs
Sensor/Data Fusion       Combining data from multiple sensor types to create a more comprehensive and accurate environmental representation as well as capturing their complex distributions for advanced analytics and decision-making.     VAEs, FGMs, GTMs
Decision Making     Assistance in making a series of decisions that depend on a temporal sequence of sensor and communication data.         LSTMs, GTMs
Uncertainty Quantification     Estimation of the uncertainty or confidence level associated with sensor data or predictions.                           VAEs, DGMs

§.§ INTEGRATED SENSING AND COMMUNICATIONS

ISAC represents an exciting frontier for network technologies as the fusion of sensing and communication capabilities allows a network to adapt and respond dynamically to changes in its environment, thereby enabling more efficient and intelligent operations. GMs can play a pivotal role in realizing ISAC systems in multiple ways as summarized in Table <ref> and explained below.

§.§.§ Data Augmentation:
This role underscores the generation of synthetic yet convincingly real sensor data to bolster real-world data repositories, ensuring a comprehensive base for robust training. The GANs emerge as front-runners in this realm, renowned for their prowess in crafting high-fidelity and realistic data, rendering them exceptionally suited for such augmentation tasks <cit.>. VAEs, with their probabilistic nature, can also be leveraged to create a rich spectrum of data samples, accurately capturing environment nuances.

§.§.§ Anomaly Detection:
Here, the goal is to pinpoint data patterns or individual data points that starkly deviate from established norms, a vital mechanism for prompt alerts and sustained system reliability. GANs have made strides in this domain, adeptly delineating the expected distribution of sensor data and subsequently singling out anomalies <cit.>. Furthermore, FGMs, especially their subclass - normalizing flows, may excel in precise density evaluations, becoming foundational for statistical anomaly spotting <cit.>. DGMs also enrich this process, infusing uncertainty quantification within their iterative modus operandi, amplifying anomaly detection efficacy <cit.>.

§.§.§ Resource Optimization
This entails the simulation of varying network conditions and the prediction of future states to ensure judicious resource allocation. GANs shine in emulating a plethora of network scenarios, facilitating superior model training geared towards resource maximization <cit.>. Normalizing flows, under the FGM umbrella, with their adeptness in representing intricate network state distributions, emerge as invaluable aids for decision-making processes in this context.

§.§.§ Time-Series Forecasting
This function is centered on the anticipation of subsequent sensor readings, rooted in historical and present data. This foresight is instrumental for pre-emptive decision-making. DGMs, through their iterative refinement processes, are uniquely positioned to project impending states, drawing inspiration from both past data and current inferences <cit.>. LSTMs, architected specifically for time-series data, are naturally inclined to flourish in such forecasting roles <cit.>.

§.§.§ Sensor/Data Fusion
This duty revolves around amalgamating data stemming from a diverse set of sensors, crafting an exhaustive and precise representation of the environment. VAEs simplify this intricate task, condensing multifarious data into a streamlined latent space <cit.>. Normalizing flows can further bolster this task by adeptly modeling intricate data distributions <cit.>. GTMs, known for their sequence-to-sequence capabilities, also play an instrumental role in assimilating lengthy sequences of varied sensor data, underlining their significance in data fusion endeavors <cit.>.

§.§.§ Decision Making
This role aids in sequential decision-making rooted in a temporal cascade of sensor and communication data. GTMs, primed for processing extensive data sequences, are invaluable here, particularly in sequential decision-making scenarios. Additionally, LSTMs, with their design tailored for data sequences, are indispensable in contexts demanding a robust temporal or sequential backdrop <cit.>.

§.§.§ Uncertainty Quantification
Here, the focus is on discerning the confidence or uncertainty level tethered to sensor data or its corresponding predictions. VAEs, with their intrinsic probabilistic nature, facilitate not just precise estimates but also extrapolation of confidence intervals, cementing their place in this realm <cit.>. DGMs, through their innate ability to integrate uncertainty in their iterative procedures, further augment this estimation process.

GM Roles in 6G Digital Twins

DT Roles     Role Description     GMs

Network Simulation    Creating detailed and high-resolution simulations to represent 6G network states, which can be useful for testing scenarios, training other models, or predictive maintenance.     GANs

Uncertainty Quantification      Accurately capturing and depicting the inherent uncertainties of sensory data to provide confidence bounds of DTs.     VAEs

Temporal Evolution     Addressing the inherently temporal nature of 6G network conditions, tracking their changes over time and predicting future states for more proactive network management and adaptation, understanding patterns, and trends.     GTMs, LSTMs

Distribution  Modeling     Accurately representing intricate data distributions captured by sensors to ensure DTs can represent a wide range of scenarios and conditions.     FGMs

DT Refinement     Continual improvement and refinement of DTs, ensuring evolution and adaptation as more sensory data becomes available and as the real-world scenario it mirrors changes.     DGMs

§.§ NEXT-GENERATION PHYSICAL LAYER DESIGN

The core of 6G technological advancement is anchored in the integration of high-frequency spectrums (e.g., mmWave <cit.> and THz <cit.>), along with the implementation of massive size antenna arrays, i.e., RIS <cit.>, ELAA <cit.>, etc. This combination is pivotal in enhancing the density and effectiveness of connections. Advancements in the spectrum range and the increase of antenna concentration per area eventually lead us into the realm of NFC <cit.>, offering precise control over the orientation, form, and structure of beams. In this context, holographic communication comes into play, particularly in the near-field zone, to intricately craft a “hologram” of electromagnetic fields <cit.>. This technique uses expansive arrays to create beams with unparalleled spatial resolution, morphing them in sophisticated manners akin to the process of optical holography in shaping light. The convergence of THz, ELAA, NFC, and holographic beamforming strengthens the forefront of advanced wireless communications, though it also intensifies the complexity in channel estimation and beamforming processes. The remainder of this subsection will visit prominent GMs and elaborate their roles in mitigating related challenges of these technologies.

§.§.§ THz communications
Channel modeling and signal propagation are paramount for THz communications. GANs can provide a solid foundation in creating realistic simulations of THz channel conditions and analyzing signal propagation, particularly in data-scarce scenarios. VAEs may supplement this by generating synthetic data that encapsulates THz-specific characteristics like atmospheric absorption, aiding in signal reconstruction under challenging environmental conditions. FGMs might offer precision in simulating THz wave propagation, focusing on interactions with environmental elements, crucial for optimizing system design. Additionally, DGMs can play a significant role in simulating the propagation of electromagnetic waves in intricate scenarios, providing valuable insights into optimal configurations and beam paths, enhancing the understanding of NFC and holographic beamforming dynamics in the THz spectrum. In device design and optimization, VAEs could be notably effective, proposing innovative antenna configurations and assisting in material selection for THz devices. FGMs might complement this by determining optimal designs to maximize efficiency and coverage.

§.§.§ Extremely Large Antenna Arrays
GMs can collectively address the challenges of beamforming and interference management for ELAAs. GANs could be instrumental in generating dynamic beamforming algorithms and simulating various interference patterns. VAEs may contribute by modeling optimal beam patterns for different scenarios, reducing interference by understanding spatial interference distribution. FGMs might optimize the physical layout of ELAA to maximize signal coverage and minimize interference, while developing dynamic beamforming strategies. DGMs could further enhance this by simulating noise and interference patterns in wireless networks, assisting in the development of robust communication strategies that are critical in densely populated network environments. Network planning and resource management in ELAA benefit from GMs; GANs are valuable in simulating deployment scenarios and resource allocation, while VAEs optimize power distribution and signal routing. FGMs can predict the impact of resource allocation strategies on system performance. DGMs, with their ability to simulate complex noise and interference patterns, may inform strategies for efficient resource utilization and network management. Finally, in system performance analysis and predictive maintenance, VAEs might detect deviations for potential system issues, enhancing network health and security. FGMs could offer predictions on system behavior, essential for verifying and validating ELAA designs. DGMs, by simulating a wide range of noise and interference conditions, can also contribute to understanding and enhancing the robustness of communication systems.

§.§.§ Near-Field and Holographic Beamforming

The integration of GMs with NFC and holographic beamforming represents a pivotal advancement in these communication technologies. GMs can bring specialized capabilities that collectively address the complex challenges in NFC and holographic beamforming.

In NFC, GANs can play important roles in generating realistic electromagnetic wave propagation scenarios, which is key given NFC's sensitivity to environmental factors. GANs may create intricate simulations that closely mimic real-world conditions, thereby aiding in the optimization of NFC systems for diverse operational scenarios. Concurrently, VAEs can enhance NFC by modeling the unique propagation characteristics of electromagnetic fields at short distances, generating high-fidelity data crucial for system fine-tuning. FGMs in NFC might offer detailed simulations of electromagnetic field behaviors, providing deep insights into the subtle interactions within NFC environments, which is essential for designing more accurate and efficient systems. Additionally, DGMs can bring advanced capabilities in simulating complex wave interactions in NFC, offering valuable perspectives on optimal configurations and enhancing overall system performance.

Holographic beamforming benefits similarly from GMs; GANs are instrumental in modeling diverse beam patterns and operational environments, aiding in the development of beamforming strategies that optimize signal quality and efficiency. VAEs can play a significant role in holographic beamforming by analyzing and reconstructing beam patterns, thereby improving the resolution and focus of holographic beams in complex communication environments. FGMs might contribute to holographic beamforming by simulating the dynamic manipulation of electromagnetic waves, enabling the creation of sophisticated and adaptable beamforming strategies. This enhances the capability to focus and direct signals with high precision. Similarly, DGMs could be highly valuable in creating detailed simulations of electromagnetic field interactions for holographic beamforming. They assist in developing techniques that intricately control and shape beam patterns, which is crucial for high-resolution communication applications.

GM Roles in AIGC

AIGC Roles                                                                     Description                                                                                Generative Models
c]@l@Real-time Content
Creation/Customization     Generating or modifying content in real-time for personalized user experiences.                        c]@l@LLMs, GPTs,
GANs, DGMs
Enhanced VR/AR                                                                          Creating realistic textures and environments for VR and AR applications.                               GANs, DGMs, FGMs
Content Distribution                                                                    Optimizing content distribution for various devices and network conditions.                            VAEs, FGMs
Data Compression                                                                        Compressing and decompressing data while maintaining quality, crucial for images and videos.           VAEs, DGMs
Interactive AI Services                                                                 Generating interactive text-based content like conversational AI and personalized recommendations.     LLMs, GPTs
c]@l@Smart Cities and
IoT Integration             Simulating urban environments and generating data for IoT devices.                                     GANs, FGMs, DGMs
c]@l@EAI and Localized
Content Creation           Generating content directly on edge devices, reducing latency.                                         VAEs, GANs

§.§ DIGITAL TWINS OF 6G NETWORKS

The seamless integration of the digital and physical realms lies at the heart of the 6G vision <cit.>. As 6G networks emerge, accommodating a myriad of connected devices and facilitating large-scale IoT deployments, there is a dire need for tools that can create accurate digital representations of the vast and complex real-world scenarios 6G networks inhabit <cit.>. This is where ISAC and DT paradigm handshake, utilizing sensory data to craft intricate virtual replicas of both network conditions and the wireless environment. However, transforming vast, diverse, and dynamic sensory data into precise, actionable DT models presents its challenges. GMs stand out as potent solutions to these challenges, intertwining the promise of 6G with the efficacy of DTs.

§.§.§ High-resolution Network Simulations:
GANs, known for their aptitude in generating high-resolution, detailed simulations, become pivotal in translating sensory data into vivid digital replicas of 6G network states <cit.>. Given the dynamism of 6G environments, where network conditions can fluctuate based on myriad factors, GANs assist in simulating various potential network states. Their capability to produce realistic representations from sensory data can also enhance predictive maintenance, ensuring networks operate optimally and preemptively addressing potential bottlenecks.

§.§.§ Uncertainty Quantification:
VAEs, with their probabilistic underpinnings, are particularly adept at capturing and representing the inherent uncertainties of sensory data <cit.>. As sensors capture the multifaceted nuances of the environment, VAEs can effectively process this data, offering predictions alongside confidence intervals. This becomes essential when representing complex 6G network conditions, ensuring that the digital twin remains a true reflection of the ever-evolving physical state.

§.§.§ Temporal Modeling and Prediction:
The temporality of network conditions and their evolution finds a match in GTMs <cit.>, which can excel in processing sequential data, translating it into a coherent narrative of how 6G network conditions might evolve in response to changes in the environment. This time-series interpretation offers network operators foresight, allowing for timely interventions and adjustments.

§.§.§ Distribution Modeling:
FGMs provide a nuanced approach to modeling intricate data distributions <cit.>, ensuring that the digital representation encapsulates even the most subtle changes in network conditions as captured by sensors. In the fast-paced world of 6G, where real-time synchronization between digital twins and their physical counterparts is crucial, the swift density estimation capabilities of these models become indispensable.

§.§.§ Continuous DT Refinement:
Rounding off with DGMs, their iterative approach ensures continual refinement of the digital representation <cit.>. In a realm where sensory data might exhibit noise or where network conditions can change unpredictably, these models' emphasis on uncertainty provides a more layered, refined understanding.

In essence, as 6G networks embark on revolutionizing our digital landscapes, the confluence of sensory data, DTs, and GMs promises an ecosystem where network conditions and environments are not just monitored but truly understood. This understanding, rooted in the dynamic interplay of real-world data and advanced modeling techniques, is set to be the bedrock upon which 6G will thrive.

§.§ AI-GENERATED CONTENT FOR 6G NETWORKS

In the rapidly evolving landscape of telecommunications, the advent of 6G networks is set to revolutionize the way we interact with digital content. This new era is not just about faster speeds and lower latencies; it heralds a transformative shift in the creation, distribution, and consumption of AIGC. The integration of advanced generative models into the fabric of 6G technology is a critical factor in this transformation. GMs offer unprecedented capabilities in generating realistic, interactive, and personalized content. As summarized in Table <ref>, this section delves into the synergistic relationship between various GMs and their specific roles in enhancing the AIGC landscape within 6G networks. Through a comprehensive analysis, the remainder will illuminate how GMs are not just facilitating but actively shaping the future of digital media and communications.

§.§.§ Real-time Content Creation and Customization

LLMs and GPTs stand out for their ability to generate personalized text content, including chat responses and customized news articles. Meanwhile, GANs and DGMs are highly effective for generating or modifying images and videos in real-time. These models offer high-quality visual content, making them ideal for dynamic and interactive media applications.

§.§.§ Enhanced VR and AR

In the domain of enhanced VR and AR, GANs and DGMs excel in creating realistic textures and environments. Their proficiency in generating high-resolution, detailed images is crucial for the immersive experiences demanded by VR and AR technologies. Additionally, Flow-based Generative Models are instrumental in 3D object generation and transformations due to their ability to model complex distributions, further enhancing the realism and interactivity of virtual spaces.

§.§.§ Efficient Content Distribution

The improved efficiency in content distribution is another critical area where VAEs and FGMs play a significant role. They are capable of efficient data compression and reconstruction, which is vital for adaptive content streaming across diverse devices and network conditions. This efficiency is essential in optimizing network resource utilization and ensuring seamless content delivery.

§.§.§ Advanced Data Compression

Advanced data compression techniques, particularly relevant for images and videos, are effectively handled by VAEs and DGMs. These models are adept at compressing and decompressing data while maintaining high quality, a key requirement for the bandwidth-intensive media content typical in 6G networks.

§.§.§ Interactive AI Services

Interactive AI services, such as conversational AI and personalized recommendations, benefit greatly from the capabilities of LLMs and Generative Pre-trained Transformers. These models are best suited for generating interactive text-based content, enabling more engaging and responsive user experiences.

§.§.§ Smart cities and IoT Integration

In the context of smart cities and IoT integration, GANs and FGMs are invaluable for simulating urban environments and generating data for IoT devices. This capability aids in city planning and environmental monitoring. DGMs also contribute significantly, particularly for tasks like environmental modeling or generating realistic simulations of city dynamics, enhancing the efficiency and effectiveness of smart city operations.

§.§.§ Edge AI and Localized Content Creation

Lastly, in the sphere of edge AI and localized content creation, VAEs and Lightweight GANs are particularly suitable. Due to their relatively lower computational requirements, these models are ideal for edge devices, enabling them to generate content directly on the device. This reduces latency and is crucial for applications requiring quick response times, such as autonomous vehicles or local media generation.

Each of these GMs brings unique strengths to the table, making them well-suited for specific tasks within the evolving landscape of AIGC in 6G networks. Their integration promises to revolutionize how content is created, distributed, and experienced, paving the way for more immersive, efficient, and personalized digital interactions.

§.§ MOBILE EDGE COMPUTING AND EDGE AI

MEC and EAI represent a transformative step for 6G networks, particularly in the way they decentralize data processing. The traditional model, which relied heavily on central servers or cloud-based processing, introduced latency and inefficiencies—particularly for time-sensitive tasks or for devices in remote locations. By integrating computation and AI/ML capabilities directly at the network's edge, devices can process data and make decisions in real-time, right where they are. This shift is crucial for the evolving demands of 6G and its myriad applications. As summarized in Table <ref>, GMs are a potent tool in this landscape <cit.>, and below details how they fit into the MEC-EAI paradigm for 6G:

§.§.§ Localized Data Augmentation:
GANs can generate synthetic data samples that mimic real-world data <cit.>. In a MEC scenario, GANs can augment the data directly at the edge, useful for scenarios where large datasets are required for training or evaluation but are not available.

§.§.§ On-device Uncertainty Modeling:
VAEs are probabilistic models, and their inherent ability to quantify uncertainty can be crucial for edge devices <cit.>.

§.§.§ Real-time Sequence Processing:
Many edge applications, such as VR and AR, depend on sequential data. LSTMs and GTMs excel at processing sequences, enabling real-time adjustments in AR experiences based on the user's movements and interactions.

§.§.§ Complex Distribution Modeling at the Edge:
Edge devices often encounter intricate data distributions, especially in multi-modal or multi-sensor environments. Normalizing Flows can help in modeling these complex distributions, ensuring that the localized AI can understand and act upon the nuanced information it receives.

§.§.§ Iterative Refinements with DGMs:
In scenarios where the edge data might be noisy or unpredictable, DGMs can play a pivotal role. They iteratively refine their outputs, improving accuracy over time, which can be instrumental for applications like advanced robotics where environmental changes can be erratic.

The blend of MEC and EAI in 6G, supported by GMs, opens up enormous possibilities. It not only decentralizes data processing but also makes it smarter, faster, and more adaptable. The importance of real-time, efficient, and localized decision-making can't be overstated for future networks, especially as the demand for intelligent, autonomous systems continues to rise. GMs, with their varied capabilities, are poised to be pivotal players in the realization of the full potential of MEC in 6G networks.

GM Roles in MEC and EAI for 6G Networks

MEC Roles     Role Description     GMs

Local Data Augmentation      Generating synthetic data samples mimicking real-world data, aiding in scenarios where on-device datasets for training or evaluation are limited     GANs

Uncertainty Modeling     Predicting uncertain conditions due to their probabilistic nature, aiding edge devices in adjusting operations based on dynamic environments     VAEs

Real-time Processing     For applications like AR that rely on sequential data, processing data sequences in real-time, offering real-time interactions.     LSTMs GTMs

Distribution Modeling     Modeling intricate data distributions to ensure AI models using edge devices interprets nuanced information, especially in multi-sensor setups     FGMs

Iterative Refinements     In environments where data can be noisy, DGMs refine their outputs over iterations, improving accuracy, especially beneficial for unpredictable scenarios.     DGMs

§.§ ADVERSARIAL ML AND TRUSTWORTHY AI

AML focuses on understanding how AI models can be susceptible to specially crafted inputs that can deceive the model into making incorrect predictions or classifications. These adversarial examples are designed by introducing small, often imperceptible, perturbations to the input data, such that the model's output is manipulated, which may appear virtually unchanged to humans. The study of AML is crucial not just to understand these vulnerabilities, but also to develop defenses against potential adversarial attacks. In what follows, we explain how GMs can play role in AML domain.

§.§.§ Attack Mechanisms using GMs:

GMs can be used to generate adversarial examples or attack patterns <cit.>. For instance, GANs have been employed to craft adversarial samples that can fool a classifier. The generator in GANs can be trained to produce samples that, although they look genuine, lead a target classifier into making wrong decisions.

§.§.§ Defense Mechanisms using GMs:

On the flip side, GMs can also be leveraged to detect and counter adversarial attacks. For instance, a GAN can be trained where the generator creates adversarial examples and the discriminator learns to distinguish between genuine and adversarial samples <cit.>. Once well-trained, the discriminator can act as a filter to detect adversarial examples in real-world deployments. VAEs can be employed as a defense mechanism <cit.>. A VAE can be trained on the original dataset, and when an input (potentially adversarial) is passed through the VAE, the reconstructed output is fed into the classifier. Since VAEs tend to reconstruct what they have learned and ignore tiny perturbations, this can mitigate the impact of adversarial attacks.

§.§.§ Evolving Threats in 6G:
6G networks, with enhanced connectivity and distributed intelligence, will provide ample opportunities but will also be vulnerable to sophisticated cyber-attacks. With the convergence of MEC and EAI techniques in 6G, edge devices may be exposed to adversarial attacks aiming to deceive local AI inferences. For instance, a malicious actor could attempt to tamper with an IoT device's sensory data to alter its AI-driven decisions, necessitating robust defense mechanisms. GTMs and LSTMs, given their capacity to handle sequences, can be employed in AML to detect adversarial attacks in time-series data <cit.>, especially relevant for communications data in 6G networks. FGMs and DGMs can be applied for modeling the complex data distributions of regular traffic and identifying discrepancies in the form of adversarial attacks.

AML directly addresses the challenge of adversarial attacks, making AI systems more resilient and robust. On the other hand, as defenses against adversarial attacks are developed, there's a growing need for transparency to understand how these defenses work and how the model processes information, which falls within the realm of trustworthy AI (TAI) <cit.>.
Various frameworks have been proposed to guide the development of TAI, with the European Commission's High-Level Expert Group on Artificial Intelligence[<https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai>] being one of the more prominent bodies to have formalized TAI as a concept revolving around seven pillars that encompasses various facets of AI ethics and robustness. In the remainder, we discuss how GMs can contribute to each of these pillars, especially in the context of 6G networks:

38 Human Agency and Oversight: GMs can be used to create interpretable representations of complex data, helping human operators understand and oversee AI-driven processes. Especially in the dense, fast-paced environment of 6G networks, such insights can be invaluable for human administrators.

38 Privacy and Data Governance: GMs can help in data augmentation without breaching privacy. For instance, GMs can be used to create synthetic datasets that resemble the original without carrying sensitive information. Additionally, they can aid in federated learning, where model training occurs at the edge without centralizing data.

38 Transparency: GMs, especially VAEs, can create latent space representations of data that are more understandable. By visualizing these representations, one can glean insights into what the AI model deems significant, fostering transparency.

38 Diversity, Non-discrimination, and Fairness: GANs and other GMs can be trained to ensure that AI models are tested across diverse scenarios and data distributions, ensuring they don't inadvertently discriminate against particular groups or conditions.

38 Societal and Environmental Well-being: GMs can simulate various societal and environmental scenarios, allowing for the predictive assessment of AI's impact. In the context of 6G, this could involve understanding the network's response to different societal needs or environmental conditions.

38 Accountability: By creating simulations and synthetic scenarios, GMs can play a role in "stress-testing" AI systems. In cases where AI-driven decisions in 6G lead to unintended outcomes, these simulations can be revisited to understand the decision-making process, facilitating accountability.

As 6G networks pave the way for more pervasive and intricate AI integrations, the principles of trustworthy AI become even more vital. Generative models, with their versatile capabilities, will play a pivotal role in ensuring that the AI systems of the future are not only intelligent but also ethical, robust, and trustworthy.

§ OPEN CHALLENGES AND THE WAY FORWARD

In this section, we discuss multi-faceted open research challenges and point out strategies and candidate technologies as a remedy.

§.§ RAMIFICATIONS OF COMPUTATIONAL COMPLEXITY

The computational overhead associated with GMs presents a major challenge for real-time deployment in 6G networks, which are anticipated to have even more stringent latency requirements than their predecessors. Several dimensions add to this complexity:

§.§.§ Energy Efficiency
High computational demands generally translate to increased energy consumption. In the context of 6G, which aims for greener and more sustainable operations, the energy-intensive nature of GMs poses a significant hurdle. Energy-efficient algorithms and hardware acceleration techniques may be needed to make the deployment of GMs viable.

§.§.§ Real-time Requirements
6G networks are expected to support real-time applications like augmented reality, autonomous vehicles, and telemedicine, which have stringent latency requirements. Achieving low-latency operations with computationally demanding GMs remains a formidable challenge.

§.§.§ QoS and QoE
Meeting QoS and QoE expectations in a 6G environment laden with computational complexities is another challenge. Network operators will need to balance these quality metrics against the limitations and costs imposed by the computational complexity of GMs.

§.§.§ Trade-off Between Speed and Accuracy
Strategies to reduce computational complexity often involve simplifying models or using approximation techniques. However, this can come at the cost of accuracy or reliability, making it a challenging trade-off to navigate.

§.§.§ Distributed Computing
One potential solution could be the use of distributed computing or edge computing to offload some of the computational tasks. While this can mitigate latency, it introduces new complexities in data synchronization, resource allocation, and network architecture.

§.§.§ Hardware Considerations
Specialized hardware accelerators like GPUs or TPUs may be necessary to meet performance criteria. However, these come with their own set of challenges, including increased cost, power consumption, and potential issues with hardware-software compatibility.

Given the expected heterogeneity and dynamism of 6G networks, GMs may need to dynamically adapt to varying computational resources and latency requirements. This calls for the development of GMs and algorithms that can self-optimize based on the operating conditions. Addressing the challenges of computational complexity and latency will likely require a multi-disciplinary approach, involving advances in algorithm design, hardware acceleration, network architecture, and possibly even new paradigms in computing. The cost, energy efficiency, and user experience implications of these challenges make them central to the successful deployment of GMs in 6G networks.

§.§ SCALABILITY AND FLEXIBILITY

As 6G networks are expected to accommodate an exponential growth in connected devices, services, and types of data, generative models must be engineered for high scalability and flexibility, which involves several facets:

§.§.§ Horizontal Scaling
To manage increasing computational demands, the ability to distribute tasks across multiple machines or clusters becomes vital. Horizontal scaling is often more desirable than vertical scaling (adding more power to a single machine) because it offers greater flexibility in managing workloads and can adapt more fluidly to varying demand.

§.§.§ Resource Efficiency
For real-time or near-real-time applications, the GMs must be designed to perform tasks under stringent resource constraints. This could involve optimizing algorithms for speed and low memory usage, perhaps by simplifying GMs where possible without sacrificing performance quality.

§.§.§ Adaptive Architectures
GMs should feature architectures that can adapt based on the hardware availability. For example, a model deployed in a high-resource cloud environment may be able to utilize more layers or nodes, whereas the same model running on an edge device with limited computing capabilities might need to operate in a simplified mode.

§.§.§ Data Stream Adaptability
6G networks will not only deal with an increasing amount of data but also a more diverse range of data types and sources. GMs will need to handle streaming data effectively, making real-time adjustments to adapt to changes in data features or volume. This is especially important for time-sensitive applications like autonomous vehicles or real-time health monitoring.

Scalability and flexibility can be improved through following main approaches:

73 Decentralization and Edge Computing To further enhance scalability, employing decentralized approaches and edge computing can distribute the computational load. Instead of sending all data to a central server for analysis, pre-processing can be performed closer to the source of the data. This reduces latency, bandwidth usage, and the central computational load, but also poses challenges for coordinating and updating GMs across a distributed network.

73 Modularity and Interchangeability Scalability is not just about handling more devices or more data; it's also about flexibility in incorporating new features or capabilities. Modular design principles can help by allowing different parts of the GMs to be updated independently, without requiring a complete overhaul of the system. This makes it easier to adopt new algorithms, adapt to new data types, or interface with new devices, thereby ensuring that the system remains both scalable and flexible.

§.§ ROBUSTNESS AND RELIABILITY

For GMs to be effective in 6G networks, they must be both robust against various forms of errors/conditions and reliable in their performance. Several facets contribute to these challenges:

§.§.§ Environmental and Contextual Variability
6G networks will operate in diverse settings, from densely populated urban areas to remote rural locations. GMs need to be robust against different types of noise and interference, which can vary significantly across these environments.

§.§.§ Fault Tolerance
GMs should be designed to continue functioning even when certain network nodes or data streams fail. This involves engineering redundancy and self-healing mechanisms.

§.§.§ Uncertainty Quantification
The ability to estimate the uncertainty associated with GM predictions can improve robustness, as it allows for the quantification of confidence levels and assists in making more informed decisions.

§.§.§ Data Quality
The reliability of a GM is highly dependent on the quality of the data used for training and validation. Robust mechanisms for data verification and validation are therefore crucial.

Optimizing both the software and hardware aspects can lead to more robust and reliable GMs, but also adds complexity to the design and deployment process. To increase robustness, GMs could benefit from fusing data from multiple types of sensors, where ISAC can play a vital role.

§.§ INTEROPERABILITY, COMPATIBILITY, AND STANDARDIZATION

The challenges of making generative models (GMs) work smoothly across diverse devices, vendors, and networks involve numerous complexities:

§.§.§ Variability in GM Architectures
Given that GMs can vary widely in terms of architecture, data requirements, and output formats, achieving interoperability is highly challenging. Devices from different vendors may rely on different versions or types of models, adding further complexity to standardization efforts.

§.§.§ Legacy Systems and Backward Compatibility
As advanced GMs are deployed in newer devices, there's a pressing need to ensure that these new models can interact seamlessly with older, legacy systems. This involves intricate problems like data format conversion and feature degradation to make them compatible with older systems, often at the cost of limiting advanced functionalities.

§.§.§ Middleware and Translation Layers
To address the issues of interoperability and backward compatibility, middleware solutions or translation layers might be necessary. These would serve as intermediaries to convert and interpret data or commands between disparate GM architectures and legacy systems.

§.§.§ Versioning and Updates
Managing updates to GMs in a way that doesn't disrupt existing services is a delicate balance. Network operators will have to develop robust versioning strategies to roll out improvements without causing system-wide issues.

§.§.§ Security and Trust
Interoperability also brings challenges in ensuring that communications between different GMs and systems are secure. Standardized security protocols that work across various generative models will be essential.

§.§.§ Standardization Bodies and Consensus
The development of universally accepted standards for GMs in 6G networks is a massive undertaking requiring multi-stakeholder involvement. This includes device manufacturers, network operators, policy makers, and even end users. Given the fast pace of technological advancements, these standards also need to be agile and adaptive.

§.§.§ Global Coordination
6G networks are expected to be global, adding another layer of complexity to standardization efforts. International bodies may need to be involved to ensure that standards are universally applicable and that they meet the regulatory requirements of different countries.

Creating a standardized, interoperable, and backward-compatible ecosystem for GMs in 6G networks will be one of the defining challenges of this technological shift. It is a monumental task that involves not only technical solutions but also coordinated efforts from regulatory bodies and industry stakeholders. Without such standardization, the full potential of GMs in enhancing 6G services may remain unrealized.

§.§ REGULATION AND POLICY

As GMs become an integral part of 6G networks, they should be subject to a myriad of rules, regulations, and policies, both existing and those that are yet to be established. Here are some key challenges in this arena:

§.§.§ Data Protection, Ethics, and Social Impacts
GMs may  potentially breach privacy norms by inadvertently acquiring sensitive or personally identifiable information, especially when dealing with unstructured data such as text or images. Striking a balance between data privacy and the operational efficiency of semantic communication poses a substantial challenge. GMs are prone to incorporating the biases present in their training data. In a 6G context, these biases could manifest in various forms—such as discriminatory service delivery or unfair resource allocation—which may have direct social implications. With 6G networks enabling more powerful data analytics and AI capabilities, there's potential for mass surveillance and intrusions into personal privacy. Ethical guidelines are needed to define the acceptable limits of such technologies.

Above issues are generally governed by regulatory bodies which provide protection by restricting certain kinds of data collection or data usage. For instance, General Data Protection Regulation (GDPR) in the European Union requires a compliance checklist that spans over 7 principles: 1) lawfulness, fairness, consent, and transparency for automated decision-making in critical applications (e.g., healthcare, transportation, or law enforcement, etc.) ; 2) purpose limitation; 3) data minimisation; 4) accuracy; 5) storage Limitations; 6) integrity and confidentiality; and 7) accountability, some of which will be covered in the remainder. Ensuring compliance without compromising functionality is a challenge.

§.§.§ National Security
GMs could be exploited for malicious purposes, such as creating deepfakes or other forms of misinformation, which should be recognized by 6G networks up to a certain extend and transferred to a human-in-the-loop system for further analysis and actions. Governments may also regulate the export, import, and use of certain types of GMs for reasons of national security, which may hinder adoption of certain 6G technologies and services in some part of the globe.

§.§.§ Cross-Border Coordination & Public/Private Partnerships
6G networks are expected to be global in nature, raising the challenge of aligning different countries' policies and regulations related to GMs. This could involve mutual recognition of data protection laws, or establishing international standards for model behavior and interoperability. Moreover, implementing regulatory frameworks for 6G networks utilizing GMs is likely to require collaboration between the public and private sectors. This involves agreeing on shared responsibilities, aligning commercial incentives with public good, and negotiating potential conflicts of interest.

Addressing these regulatory and policy challenges would necessitate a multi-stakeholder approach that takes into account the technical complexities as well as the societal implications of using GMs in 6G networks. Industry standards, legal frameworks, and international cooperation will all play a critical role in navigating these challenges effectively.

§ CONCLUSIONS

In the dawn of the 6G era, the integration of novel communication paradigms and emerging technologies is rapidly transforming wireless communications, with ML and AI emerging as game-changers in addressing complex challenges. While DAI dominates the AI-driven wireless research landscape, the capabilities of GenAI in enhancing and supplementing DAI methods are becoming increasingly evident, especially in scenarios with limited or incomplete data. Our comprehensive tutorial-survey not only provides insights into the fundamentals of 6G and a taxonomy of cutting-edge DAI models but also delves deep into the pivotal role of GMs across various sectors of wireless research. Through our extensive analysis of numerous technical works, we debunk the misconception of GenAI being merely a nascent trend and highlight its profound implications in shaping the future of 6G networks. Furthermore, by pinpointing potential challenges and recommending promising strategies, we believe our paper equips researchers and professionals with valuable insights, positioning itself as a benchmark resource in this rapidly evolving field.

§ ACKNOWLEDGEMENTS

Fig. 1, Fig.3, and Fig.4 were produced by Aysenur Kucuksari, a freelancer scientific illustrator (e-mail: aysenurkucuksari22@gmail.com).

[]ABDULKADIR CELIK (Senior Member, IEEE) received the first M.S. degree in electrical engineering in 2013, the second M.S. degree in computer engineering in 2015, and the Ph.D. degree in co-majors of electrical engineering and computer engineering from Iowa State University, Ames, IA, USA, in 2016. He was a Postdoctoral Fellow with the King Abdullah University of Science and Technology, Thuwal, KSA, from 2016 to 2020, where he is currently a Senior Research Scientist with the Communications and Computing Systems Laboratory. Dr. Celik is the recipient of IEEE Communications Society's 2023 Outstanding Young Researcher Award for Europe, Middle East, and Africa (EMEA) region. He currently serves as an editor for IEEE Communications Letters, IEEE Wireless Communication Letters, and Frontiers in Communications and  Networks. His research interests are in the broad areas of next-generation wireless communication systems and networks.

[]AHMED M. ELTAWIL (Senior Member, IEEE) received the M.Sc. and B.Sc. degrees (Hons.) from Cairo University, Giza, Egypt, in 1999 and 1997, respectively, and the Ph.D. degree from the University of California, Los Angeles, CA, USA, in 2003. Since 2019, he has been a Professor with the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Since 2005, he has been with the Department of Electrical Engineering and Computer Science,
University of California at Irvine, where he founded the Wireless Systems and Circuits Laboratory. His research interests are in the general area of low power digital circuit and signal processing architectures with an emphasis on mobile systems. He has been on the technical program committees and steering committees for numerous workshops, symposia, and conferences in the areas of low power computing and wireless communication system design. He received several awards, as well as distinguished grants, including the NSF CAREER Grant supporting his research in low power systems.