# Active-IRS-Aided Wireless Communication:
Fundamentals, Designs and Open Issues

Zhenyu Kang, Changsheng You, and Rui Zhang  Z. Kang is with National
University of Singapore; C. You is with Southern University of Science and
Technology; R. Zhang is with The Chinese University of Hong Kong, Shenzhen,
Shenzhen Research Institute of Big Data, and National University of Singapore.

###### Abstract

Intelligent reflecting surface (IRS) has emerged as a promising technology to
realize smart radio environment for future wireless communication systems.
Existing works in this line of research have mainly considered the
conventional passive IRS that reflects wireless signals without power
amplification, while in this article, we give an overview of a new type of
IRS, called _active_ IRS, which enables simultaneous signal reflection and
amplification, thus significantly extending the signal coverage of passive
IRS. We first present the fundamentals of active IRS, including its hardware
architecture, signal and channel models, as well as practical constraints, in
comparison with those of passive IRS. Then, we discuss new considerations and
open issues in designing active-IRS-aided wireless communications, such as the
reflection optimization, channel estimation, and deployment for active IRS, as
well as its integrated design with passive IRS. Finally, numerical results are
provided to show the potential performance gains of active IRS as compared to
passive IRS and traditional active relay.

##  I Introduction

To accommodate assorted emerging new applications (e.g., holographic
communication, extended reality, and tactile Internet), the next-generation
wireless networks need to meet more stringent performance requirements than
today’s fifth-generation (5G) networks, such as unprecedentedly high
throughput, super-high reliability, ultra-low latency, extremely low power
consumption, etc. These requirements, however, may not be fully achieved by
existing 5G technologies such as massive multi-input multi-output (MIMO) and
millimeter wave (mmWave) communications cost-effectively [1], since they
enhance communication performance with significantly increased energy
consumption and hardware cost. Moreover, the performance of 5G networks is
still fundamentally constrained by the random and uncontrollable wireless
channels due to various propagation impairments and user mobility. To address
the above issues, a promising new technology, called _intelligent reflecting
surface_ (IRS), has been proposed to dynamically control the radio propagation
environment and improve the wireless network performance in a cost-effective
manner [2]. Specifically, IRS is a planar meta-surface consisting of massive
low-cost passive reflecting elements, which are able to reflect the incident
signals with individually tuned phase shifts and/or amplitudes. As such, the
reflected signals by all reflecting elements can be added constructively for
enhancing the signal power in desired direction (so-called passive
beamforming) or destructively for mitigating undesired interference. These
useful functions have spurred growing research interests to apply IRS in
various wireless systems for improving their performance, such as passive
relaying, wireless sensing, simultaneous wireless information and power
transfer (SWIPT), etc [2].

Figure 1: Hardware architectures of active IRS, passive IRS, and active relay.

However, conventional IRS is mounted with fully passive reflecting elements
(referred to as _passive_ IRS), thus facing several issues in practice. In
particular, the reflected signal by IRS suffers severe _product-distance path
loss_ , i.e., the cascaded path loss in the transmitter-IRS-receiver link is
the product of those in the transmitter-IRS and IRS-receiver links,
respectively. This fundamentally constrains the signal power at the receiver
and hence the effective coverage of each IRS. Two practical methods to resolve
this issue have been proposed, which are, respectively, deploying more passive
elements at the IRS to enhance its passive beamforming gain and placing the
passive IRS closer to either the transmitter or the receiver to reduce the
cascaded channel path loss. However, these methods may not be applicable in
practice for the following reasons. First, equipping IRS with massive
reflecting elements or placing it too close to the transceivers will impose
great difficulty in practical IRS deployment due to limited space and
unavailable site. Second, more reflecting elements generally incur higher
training overhead for their corresponding channel estimation and higher
complexity for optimizing their reflection coefficients in real time[2].

To tackle this problem, a new type of IRS, called _active_ IRS, has been
recently proposed to enable simultaneous signal reflection and _amplification_
, thus more effectively compensating the severe cascaded path loss as compared
to passive IRS without signal amplification [3, 4, 5]. As shown in Fig. 1,
active IRS comprises a number of meta-atoms with reflection-type amplifiers
that can simultaneously alter the signal’s phase and amplify its amplitude to
enhance the signal power at the receiver, albeit at modestly higher power
consumption and hardware cost compared to conventional passive IRS. Moreover,
the amplitude control with power amplification by active IRS provides more
degrees-of-freedom (DoFs) to achieve finer-grained reflective beamforming than
passive IRS. In addition, the reflection-type amplifiers of active IRS
directly amplify the incident signals in a full-duplex manner yet without the
need of high-cost and power-hungry radio frequency (RF) chains, which is thus
more cost and energy efficient than traditional half-duplex or more advanced
full-duplex active relay that requires sophisticated RF chains for signal
processing and amplification. Despite the above advantages, active IRS also
brings new design issues, which need to be studied to evaluate its practical
performance gain over passive IRS and traditional active relay. For example,
due to signal amplification, the reflecting components of active IRS generate
non-negligible amplification noise, which results in degraded signal-to-noise
ratio (SNR) at the receiver and thus calls for efficient techniques to balance
the trade-off between signal and noise amplification by active IRS. Moreover,
as active IRS usually has limited power supply in practice, it is of paramount
importance to design its size (number of reflecting elements) and placement
for optimizing the communication performance.

Motivated by the above, this article aims to provide a comprehensive overview
of active-IRS-aided wireless communications. To this end, we first present the
fundamentals of active IRS, including its hardware architecture, signal and
channel models, as well as practical constraints. Next, we discuss new
considerations and open issues in designing active-IRS-aided communication
systems, such as the reflection optimization, channel estimation, and
deployment for active IRS, as well as how to efficiently integrate it with
conventional passive IRS to achieve superior performance than stand-alone
active/passive IRS only. Finally, numerical results are presented to show the
potential performance gains of active IRS as compared to conventional passive
IRS and active relay.

##  II Fundamentals of Active IRS

In this section, we present the fundamentals of active IRS, and compare it
with conventional passive IRS and active relay for wireless relaying.

###  II-A Hardware Architecture

As shown in Fig. 1, active IRS mainly consists of a smart controller and a
number of properly designed reflecting elements/meta-atoms. Specifically, the
IRS controller provides appropriate stimulus to adjust the reflection
coefficients (i.e., amplitude and phase shift) of each reflecting element via
e.g., a field programmable gate array (FPGA)-based control board. In addition,
it is also responsible for communicating with the base station (BS)/access
point (AP) through dedicated wireless control links. On the other hand, each
active-IRS reflecting element can reflect the incident signal with desired
phase shift and amplitude adjustment based on the electromagnetic (EM)
scattering principle, which can be achieved by e.g., the cascaded amplifying
and phase shifting (APS) circuits [6, 7], and load modulation circuits [8, 9].
In Fig. 1, we illustrate two typical hardware architectures for implementing
active-IRS reflecting elements. The first one (type I) is implemented by the
APS circuit, which is the integration of an operational amplifier and a phase
shifter [6, 7]. Specifically, triggered by a biasing power source, the
operational amplifier amplifies the weak electric signal with desired
amplification factor. Meanwhile, the phase shifting circuit (e.g., positive-
intrinsic-negative (PIN) diode, varactor diode [6]) connected to the amplifier
enables flexible phase modulation to the reflected signal. Moreover, to
achieve maximum power transfer to the radiating structure, a matching/coupling
circuit is added to maintain a good impedance match between the APS circuit
and the radiating structure. In contrast, the second implementation of active-
IRS reflecting element (type II) can be achieved by load modulation circuits
based on e.g., tunnel diodes [8] and complementary metal-oxide semiconductor
(CMOS) [9]. Taking the tunnel diode [8] as an example, by imposing proper bias
voltage, it can operate in the region with negative differential resistance
such that the reflecting element has an active load impedance (negative
resistance). As such, the reflection amplitude of incident signals can be
increased by converting the direct current (DC) power into the RF power, while
the phase shift is adjusted by tuning the circuit capacitance.

###  II-B Signal and Channel Models

TABLE I: Comparisons of active IRS, passive IRS, and active relay.

| Active IRS | Passive IRS | Active relay
---|---|---|---
Hardware cost | Low | Very low | High
Power consumption | Moderate | Very low | High
Operation mechanism | Reflect and amplify | Reflect | Receive and amplify
Duplex mode | Full duplex | Full duplex | Full/half duplex
Amplification noise | Yes | No | Yes

The signal and channel models for active IRS are more complicated than those
for passive IRS. First, different from the noise-free passive IRS, the noise
introduced by active IRS is composed of both dynamic noise and static noise.
Specifically, the dynamic noise is related to the input noise which is
determined by the source resistance of the antenna and inherent device noise;
while the static noise is independent of the transmit/amplification power,
which is usually negligible compared to the dynamic noise. Second, the signal
at the receiver via active IRS is superposed by the desired signals over both
the cascaded transmitter-IRS-receiver and direct transmitter-receiver links,
the amplification noise generated at each reflecting element over the IRS-
receiver link, and the additive noise at the receiver. Let $M$ denote the
number of active-IRS elements and $x$ denote the transmitted signal. Then the
received signal $y$ at the receiver can be mathematically expressed as
$y=\left(\mathbf{h}^{H}\mathbf{\Psi}\mathbf{g}+t\right)x+\mathbf{h}^{H}\mathbf{\Psi}\mathbf{z}_{\mathrm{I}}+z_{0}$,
where $\mathbf{h}^{H}$, $\mathbf{g}$, and $t$ denote the equivalent baseband
channels of the IRS$\to$receiver, transmitter$\to$IRS, and
transmitter$\to$receiver links;
$\Psi\triangleq\operatorname{diag}\left(\alpha_{1},\cdots,\alpha_{M}\right)\operatorname{diag}\left(e^{\jmath\phi_{1}},\cdots,e^{\jmath\phi_{M}}\right)$
is the active-IRS reflection matrix with $\alpha_{m}$ and $\phi_{m}$
representing the amplification factor and phase shift of the $m$-th reflecting
element, respectively; $\mathbf{z}_{\mathrm{I}}$ and $z_{0}$ denote the
amplification noise induced by active IRS and the additive noise at the
receiver.

###  II-C Practical Constraints

Active IRS usually has smaller amplification power than active relay, which
thus limits its maximum amplification factors at the reflecting elements in
practice. Generally speaking, there are two practical amplification power
models applicable to active IRS. In the first model, the amplification factors
of all reflecting elements are subject to a total power constraint as they
share a common power source [3]. In this case, the total amplification power
can be allocated to reflecting elements based on the corresponding IRS-related
channels to achieve optimal performance. While for the second model, in
contrast, each active-IRS reflecting element is attached with a separate power
source with independent power control. As such, the per-element maximum
amplification factor is constrained by the _individual_ per-element
amplification power.

Moreover, in practice, other hardware constraints should be considered for
modeling active IRS. First, it is worth noting that the reflection amplitude
based on the negative resistance circuit is generally phase-dependent, because
both the amplification factor and phase shift are functions of the effective
capacitance and resistance of each reflecting element. Second, for the active-
IRS element based on tunnel diode, its load impedance is determined by the
effective capacitance and resistance; thus, its maximum amplification factor
is constrained by the inherent load impedance, which cannot be tuned
arbitrarily large regardless of the power supply. Last but not least, the
resolutions of active-IRS phase shift and amplification factor depend on the
specific hardware implementation, which need to take discrete values in
practice.

###  II-D Comparison with Passive IRS and Active Relay

In Table I, we compare the main characteristics of active IRS with its two
counterpart technologies for wireless relaying, namely, passive IRS and active
relay. First, in terms of hardware cost and power consumption, active IRS
surpasses passive IRS due to the additional reflection-type amplifiers, while
it falls below active relay since the latter requires power-hungry and costly
RF chains for signal processing and amplification.

Next, compared to passive IRS, active IRS endows additional amplitude
amplification for the reflected signal, thus attaining more flexible beam
steering and larger coverage (see Fig. 2(a)). However, its beamforming
performance is generally inferior to half-/full-duplex active relay, since
active IRS only reflects and amplifies signals in the RF band with an
equivalent diagonal baseband reflection matrix (i.e., $\mathbf{\Psi}$), while
active relay with multiple antennas have more sophisticated signal processing
capability with typically a full-rank MIMO signal processing matrix at the
baseband.

Last, wireless communication systems aided by active IRS generally attain
higher spectral efficiency than those aided by passive IRS and half-duplex
active relay. On one hand, active IRS enables power amplification when
reflecting signals, thus leading to a higher received SNR at the receiver than
passive IRS. On the other hand, practical active relay usually operates in the
half-duplex mode111Full-duplex relay requires higher hardware and
implementation cost than half-duplex relay due to the need to eliminate the
loop-back self-interference., thus suffering considerable rate performance
loss compared to full-duplex active IRS.

##  III New Design Considerations and Challenges

In this section, we present new considerations and open issues in designing
active-IRS-aided wireless communications, including the reflection
optimization, channel estimation, and deployment for active IRS, as well as
its efficient integration with passive IRS. Moreover, promising solutions for
tackling these issues are also proposed to inspire future work.

###  III-A Active-IRS Reflection Optimization

One key challenge in active-IRS-aided communication design is how to optimize
the reflection coefficients of active IRS for achieving the optimal
beamforming and SNR performance. To address it, several practical
considerations need to be taken into account such as the phase-amplitude
dependent control, maximum amplification factor, discrete phase
shifts/amplitude levels (cf. Section II-C). These considerations usually
result in non-convex constraints over the reflection coefficients and hence
non-convex optimization problems [5]. In addition, as the amplification factor
of active-IRS element can be controlled, the resulting design variables are
significantly more than those of passive IRS for which the reflection
amplitude is usually set as one or its maximum value. Several algorithms have
been proposed in the literature to solve the above problem (see, e.g., [3, 4,
5, 6]). For example, the alternating optimization (AO) method can be applied
to iteratively optimize the amplification factor and phase shift, until the
convergence is achieved. However, the performance of AO critically depends on
its initialization, which, if not properly designed, can result in
considerable performance loss. Moreover, existing works (e.g., [3, 4, 5, 6])
usually assumed ideal reflection coefficients with continuous and independent
phase-shift and amplitude control, which cannot be realized in practice due to
hardware limitations. Thus, efficient algorithms that can handle more
practical constraints are worthy of future investigation.

Several results on the active-IRS reflection design under different channel
conditions and/or system setups have been reported in the literature (e.g.,
[4, 10]). Taking the single-user case as an example, it was shown that the
reflected signals over different IRS elements should be phase-aligned such
that they can be constructively added at the receiver, for both the cases with
active or passive IRS [10]. Moreover, the amplification factor should be set
to the maximum available value for compensating the cascaded channel path
loss. Next, for the general multi-user case, there exists a new trade-off
between signal power enhancement and interference mitigation in designing the
active-IRS reflection coefficients given a total power budget constraint. In
this case, the amplification factors of reflecting elements should be properly
set, which may not necessarily equal to their maximum values. Generally
speaking, active IRS is expected to achieve better interference mitigation
performance than passive IRS, since its amplitude control endows more DoFs to
eliminate the undesired interference more effectively.

An important performance metric in the active-IRS reflection design is how the
received SNR scales with the number of reflecting elements, $M$, since such
scaling orders characterize the SNR performance when the number of IRS
elements is asymptotically large. First, consider the case with a total power
constraint over all active-IRS elements. It was reported in [3] that the
received SNR with an active IRS linearly increases with $M$ when $M$ is
sufficiently large. This is expected since the signal power at the receiver
quadratically increases with $M$ thanks to the IRS beamforming gain, while the
amplification noise power also increases linearly with $M$, thus leading to
the SNR scaling order of $M$. However, it is worth noting that although active
IRS has a smaller SNR scaling order than passive IRS without amplification
noise (i.e., $\mathcal{O}\left(M\right)$ versus
$\mathcal{O}\left(M^{2}\right)$ [2]), it provides superior rate performance
than passive IRS when the number of reflecting elements is moderate thanks to
the additional signal power amplification gain. Next, consider the case with
fixed per-element power for each active-IRS element. In this case, the total
power budget linearly increases with the number of reflecting elements. As
such, the active IRS is expected to achieve additional amplification power
gain in the order of $M$, hence yielding the same received SNR scaling order
with the conventional passive IRS, i.e., $\mathcal{O}\left(M^{2}\right)$ (such
SNR comparisons will be numerically shown in Fig. 5).

Figure 2: New design considerations for active-IRS-aided wireless
communications.

###  III-B Active-IRS Channel Estimation

Active-IRS reflection design requires accurate channel state information (CSI)
of IRS-related channels for fully exploiting its beamforming and amplification
gains. Compared with passive IRS, the CSI acquisition for active IRS is
generally more challenging due to the following reasons. First, the active-IRS
reflection design generally requires explicit CSI of the separate transmitter-
IRS and IRS-receiver links due to the new consideration of amplification noise
[10], which greatly differs from the passive-IRS case that requires cascaded
CSI only. Specifically, although the phase shift can be designed based on
cascaded CSI, the amplification factor control is generally dependent on the
incident signal power at active IRS and its power budget, while the incident
signal power depends on the transmitter-IRS CSI. Second, in active-IRS channel
estimation, it is difficult to set the amplification factor to meet the given
total/per-element power constraint exactly, since this requires the knowledge
of incident signal power that depends on the transmitter-IRS CSI, which is yet
to be estimated. Last, the amplification noise also affects the channel
estimation performance, while this issue does not exist in the channel
estimation for passive IRS.

In the following, we introduce two practical active-IRS channel estimation
approaches, with and without IRS-mounted sensors, respectively. The first
approach requires low-cost sensors installed on the active IRS, which are
interleaved with the reflecting elements. Equipped with these sensors that can
receive signals sent by the BS/users, active IRS can estimate the BS-IRS and
IRS-user channels separately by exploiting the strong channel correlations
between the IRS elements and sensors. It is worth noting that active IRS in
general requires much fewer reflecting elements than passive IRS for achieving
comparable rate performance, thus it is easier to place reflecting elements
with sensors close to each other to achieve high channel correlation. Second,
for active IRS without sensors installed, one promising approach is to combine
the separate and cascaded channel estimation methods. Specifically, the BS
first estimates the BS-IRS CSI based on the reflected signal over the BS-IRS-
BS link (i.e., by wireless sensing of the BS). Next, the BS estimates the
cascaded BS-IRS-user CSI based on the pilot signals from the user, and then
resolves the IRS-user CSI based on the estimated cascaded CSI and BS-IRS CSI.
Moreover, for the amplification factor control in channel estimation, one
practical method is by setting an equal IRS reflection amplitude over all
reflecting elements based on the received signal powers (with sensors) or
simply setting the amplification factor equal to one (without sensors) similar
to the case of passive IRS, to facilitate the estimation of IRS-user CSI.

###  III-C Active-IRS Deployment

How to deploy active IRSs in wireless networks to achieve the largest network
coverage and maximum throughput is another crucial problem to solve. Generally
speaking, several factors need to be considered in the active-IRS deployment
design, such as the locations of users, uplink or downlink communication,
number of reflecting elements, amplification power budget, amplification noise
power, etc.

First, consider the single-user case where the active-IRS location needs to be
optimized for achieving the maximum received SNR at the user. In this case, it
has been shown in [10] that active IRS should be properly deployed between the
transmitter and receiver to balance the large-scale path loss of the
transmitter-IRS and IRS-receiver channels. In particular, with a small
amplification power or a large number of reflecting elements, active IRS
should be deployed closer to the receiver; otherwise, the amplification
factors are too small to compensate the amplification noise. This is different
from the passive-IRS case where the IRS should be deployed near either the
transmitter or receiver to minimize the cascaded path loss. Besides, the
optimal active-IRS deployment locations are different in the uplink and
downlink for two-way communications in general, and thus their performance
should be balanced by choosing an appropriate location for active IRS (see
Fig. 2(b)). Second, for the multi-user case, the active-IRS deployment design
is more complicated. On one hand, it needs to strike the performance balance
among different users. On the other hand, the active IRS may not be deployed
at the BS/AP side as in the passive-IRS case, since it also needs to balance
the powers of desired signal and amplification noise. To tackle this problem,
instead of employing a computationally prohibitive brute-force search, one
efficient and low-complexity approach is by leveraging machine learning (ML)
and geometry-based optimization methods to deploy the active IRS at a location
with good statistical channel conditions. Besides active-IRS location, the
reflection gain of active IRS is also affected by its rotation angle. As shown
in [11], considerable rate performance improvement can be attained by properly
rotating the IRS without changing its locations; thus, the IRS rotation should
be carefully tuned to maximize the number of served users and IRS-user channel
gains.

Compared with single-IRS deployment, the multi-active-IRS deployment is
generally more challenging. In particular, there exists a fundamental trade-
off between the deployment cost (e.g., hardware cost, power consumption) and
communication performance (e.g., network coverage, received SNR). As such,
besides deployment locations, the number of active IRSs needs to be carefully
chosen to minimize the total deployment cost, while achieving the
communication performance requirements. Furthermore, for indoor environment,
how to deploy multiple IRSs to maximize the network connectivity subject to
the indoor topology constraints is a challenging problem, which deserves
future investigation.

###  III-D Integrated Design with Passive IRS

While the existing works mainly considered the wireless communication systems
aided by active or passive IRS only, it has been recently shown that the
integrated design with both active and passive IRSs has the potential to
achieve their combined advantages at the same time [12, 13, 14], as discussed
below.

First, it was shown in [12] that given the total deployment budget and
amplification power, a hybrid IRS with both passive and active elements (e.g.,
see Fig. 2(c)) can achieve better communication performance than the
conventional IRS with active or passive elements only. Specifically, there
generally exists a trade-off between deploying more active elements to achieve
the appealing power amplification gain and deploying more passive elements to
attain a higher beamforming gain, thus leading to the new design issue of
active versus passive elements allocation [12]. Generally speaking, when the
number of reflecting elements is small and/or the amplification power is
large, more active elements should be deployed to harness the power
amplification gain; otherwise, deploying more passive elements is expected to
achieve better performance thanks to their higher-order beamforming gain [12].

Another type of the integrated design is deploying both stand-alone active and
passive IRSs in the wireless network, as illustrated in Fig. 2(d), to improve
the communication performance [13, 14]. The advantages are two-folds. First,
deploying multiple cooperative IRSs provides the opportunity to construct a
multi-hop reflection path between the transmitter and receiver to bypass
environmental obstacles [15], which is particularly appealing in complex
environment (e.g., indoor). Second, compared with the conventional multi-
passive-IRS network, adding a number of active IRSs in the network can
substantially enhance the communication performance. This is because active
IRSs can opportunistically amplify the reflected signal along the multi-
reflection path, thus effectively compensating the severe end-to-end product-
distance path loss. Despite the advantages, the design of multi-active and
multi-passive (MAMP) IRS aided communications is also more challenging [13].
For example, the multi-IRS beam routing path with both active and passive IRSs
should be carefully devised to maximize the power amplification gain, and at
the same time, minimize the accumulated amplification noise power arising from
active IRSs. Moreover, the numbers of active and passive IRSs should also be
carefully chosen given limited deployment budget and their locations should be
optimally designed [14], which deserve further studies.

##  IV Numerical Results

Figure 3: Simulation setup

Numerical results are presented in this section to demonstrate the potential
performance gains of active IRS as compared to passive IRS and active relay.
Specifically, we consider a typical point-to-point communication system as
shown in Fig. 3, where an IRS or a relay is deployed at an altitude of 2
meters (m) to assist the communication between a BS and a user (both equipped
with single antenna), while the direct BS-user channel is assumed being
blocked. For each reflecting element, we consider the continuous and
independently controlled phase shift and reflection amplitude (the amplitude
is set to be one for passive IRS). Moreover, the line-of-sight (LoS) channel
model is assumed for all involved links, where the path loss exponent is 2 and
the reference channel power gain at a distance of 1 m is $-30$ dB. Both powers
of the receiver noise and per-element active-IRS amplification noise are set
as $-80$ dBm.

Fig. 4 shows the user’s maximum achievable rates versus the BS-user distance
in four wireless communication systems aided by: 1) an active IRS with 128
reflecting elements and a total amplification power of 10 dBm; 2) a passive
IRS with 128 reflecting elements; 3) an ideal full-duplex amplify-and-forward
(AF) relay with perfect self-interference cancellation and a total
amplification power of 15 dBm; and 4) a half-duplex AF relay with optimal time
division and a total amplification power of 15 dBm. It is observed that given
the same BS-user distance, active IRS always achieves a higher rate than
passive IRS and half-duplex relay. This is expected since active IRS provides
an additional power amplification gain compared to passive IRS, and it
achieves the full-duplex gain compared to half-duplex relay. Besides, active
IRS is inferior to full-duplex active relay due to its limited signal
processing capability and less power supply than active relay.

Figure 4: Achievable rate versus user location.

Figure 5: Received SNR versus number of reflecting elements.

Next, we show in Fig. 5 how the received SNR scales with the number of
reflecting elements in three wireless communication systems aided by: 1) an
active IRS with a fixed total power (FTP) of 0.2 W; 2) an active IRS with a
fixed per-element power (FPP) of 1 mW; and 3) a passive IRS. For the above
three systems, the corresponding IRS reflections are optimized for maximizing
the received SNR. First, it is observed that both active-IRS-aided systems
achieve much higher received SNRs than the passive-IRS counterpart. This is
expected because active IRS provides both the beamforming gain and power
amplification gain, while passive IRS only offers the beamforming gain.
Second, one can observe that with an increasing number of reflecting elements,
the received SNR of active IRS with FPP is firstly lower than and then exceeds
that with FTP. This is because in the latter case, the average per-element
power decreases with the number of reflecting elements, thus yielding a lower
received SNR scaling order.

##  V Conclusions

In this article, we provide a comprehensive overview of the newly proposed
active IRS that enables simultaneous signal reflection and amplification.
Specifically, we first present the fundamentals of active IRS, including its
hardware architecture, signal and channel models, as well as practical
constraints. Then, new considerations and open issues in designing active-IRS-
aided wireless communications are discussed to shed new light to promising
solutions, including the reflection optimization, channel estimation, and
deployment for active IRS, as well as its integrated design with passive IRS.
Last, we numerically show the potential performance gains of active IRS over
passive IRS and traditional half-duplex active relay, and the SNR scaling
orders of active IRS under different power constraints versus passive IRS.

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## Biographies

Zhenyu Kang<EMAIL_ADDRESS>is a Ph.D. candidate with National
University of Singapore, Singapore.

Changsheng You<EMAIL_ADDRESS>received his Ph.D. degree from The
University of Hong Kong. He is currently an Assistant Professor with Southern
University of Science and Technology, China.

Rui Zhang [F’17]<EMAIL_ADDRESS>received the Ph.D. degree from Stanford
University. He is now a Principal’s Diligence Chair Professor with The Chinese
University of Hong Kong, Shenzhen, and also a Provost’s Chair Professor with
National University of Singapore. He is a Fellow of the IEEE and the Academy
of Engineering, Singapore.