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智能反射面（IRS）是一种无线通信中的新兴技术，它通过大量低成本的被动反射元件来智能调整信号反射，从而动态改变无线信道，以提升通信性能。在未来的无线网络中，IRS-辅助的混合无线网络结合了主动和被动组件，有望以经济高效的方式实现可持续的容量增长。本文是一篇关于IRS-辅助无线通信的教程综述，主要探讨了以下几个方面： 1. **IRS反射模型**：IRS通过控制每个反射单元的相位来改变入射信号的方向和强度，实现对无线信号的智能操纵。这需要精确的相位控制，以确保信号的相位合成增强或抵消，从而提高通信质量。 2. **通道模型**：IRS引入了新的无线信道建模挑战，因为其改变了传统的发射机-接收机之间的传播路径。文章详细介绍了IRS信道的特性，包括多路径传播、反射损耗和相位变化等，并讨论了这些因素如何影响通信性能。 3. **硬件架构与实际约束**：IRS的硬件设计需要考虑能量效率、成本效益和大规模部署的可行性。文章讨论了IRS的物理构造，如反射单元的尺寸、材料选择以及控制单元的设计，同时提到了实际部署中可能遇到的功率限制和延迟问题。 4. **反射优化**：为了最大化通信效率，必须对IRS的反射系数进行优化。这涉及到复杂的优化问题，可能需要联合考虑发射机、IRS和接收机的策略，以及信道状态信息。 5. **信道估计**：准确估计IRS辅助下的信道状态至关重要，但因IRS的大量反射单元和可能的动态环境，这变得非常复杂。文章阐述了几种信道估计方法，如基于 pilots 的方法和基于机器学习的方法。 6. **部署策略**：IRS的部署位置和配置直接影响其效能。文章分析了不同的部署场景，如室内、室外以及高空平台部署，并讨论了如何根据网络需求和环境特征来选择最佳部署策略。 7. **应用案例**：IRS的应用范围广泛，包括但不限于增强覆盖、提高能效、减少干扰和实现物理层安全。文章列举了一些具体的无线网络应用场景，如多用户MIMO系统、物联网和毫米波通信。 8. **未来研究方向**：尽管IRS技术已经取得了显著进展，但仍有许多挑战需要解决，例如更精细的信道模型、动态环境下的快速反应机制、联合传输与反射的联合优化，以及IRS硬件的进一步小型化和低能耗设计。这篇论文为读者提供了理解IRS技术的全面视角，不仅深入解析了相关理论和技术，还指出了未来的研究热点，对于从事无线通信领域的研究人员和工程师来说，具有很高的参考价值。

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IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 69, NO. 5, MAY 2021 3313

Intelligent Reﬂecting Surface-Aided Wireless

Communications: A Tutorial

Qingqing Wu , Member, IEEE, Shuowen Zhang , Member, IEEE, Beixiong Zheng , Member, IEEE,

Changsheng You

, Member, IEEE, and Rui Zhang , Fellow, IEEE

(Invited Paper)

Abstract— Intelligent reﬂecting surface (IRS) is an enabling

technology to engineer the radio signal propagation in wireless

networks. By smartly tuning the signal reﬂection via a large

number of low-cost passive reﬂecting elements, IRS is capable of

dynamically altering wireless channels to enhance the communi-

cation performance. It is thus expected that the new IRS-aided

hybrid wireless network comprising both active and passive com-

ponents will be highly promising to achieve a sustainable capacity

growth cost-effectively in the future. Despite its great potential,

IRS faces new challenges to be efﬁciently integrated into wireless

networks, such as reﬂection optimization, channel estimation,

and deployment from communication design perspectives. In

this paper, we provide a tutorial overview of IRS-aided wireless

communications to address the above issues, and elaborate

its reﬂection and channel models, hardware architecture and

practical constraints, as well as various appealing applications in

wireless networks. Moreover, we highlight important directions

worthy of further investigation in future work.

Index Terms— Intelligent reﬂecting surface (IRS), smart and

reconﬁgurable environment, IRS-aided wireless communication,

IRS channel model, IRS hardware architecture and practical

constraints, IRS reﬂection optimization, IRS channel estimation,

IRS deployment, IRS applications.

I. INT RODUCTION

A. Motivation

LT HOUGH the ﬁfth-generation (5G) wireless network

is still under deployment worldwide, both academia

and industry have been enthusiastically looking into future

beyond 5G (B5G) such as the sixth-generation (6G) wireless

Manuscript received July 5, 2020; re vised November 27, 2020; accepted

December 28, 2020. Date of publication January 18, 2021; date of current

version May 18, 2021. The work of Qingqing W u is supported in part by

SRG2020-00024-IOTSC and FDCT 0108/2020/A. This work is supported

in part by the National University of Singapore under Research Grant

R-261-518-005-720. The associate editor coordinating the review of this

article and approving it for publication was D. W. K. Ng. (Corresponding

author: Rui Zhang.)

Qingqing Wu was with the Department of Electrical and Computer Engi-

neering, National Univ e rsity of Singapore, Singapore 117583. He is now with

the State Key Laboratory of Internet of Things for Smart City and Department

of Electrical and Computer Engineering, Univ ersity of Macau, Macau 999078,

China (e-mail: qingqingwu@um.edu.mo).

Shuowen Zhang was with the Department of Electrical and Computer

Engineering, National University of Singapore, Singapore 117583. She

is now with the Department of Electronic and Information Engineering,

The Hong Kong Polytechnic University, Hong Kong SAR, China (e-mail:

shuowen.zhang@polyu.edu.hk).

Beixiong Zheng, Changsheng You, and Rui Zhang are with the Depart-

ment of Electrical and Computer Engineering, National University of Sin-

gapore, Singapore 117583 (e-mail: elezbe@nus.edu.sg; eleyouc@nus.edu.sg;

elezhang@nus.edu.sg).

Color versions of one or more ﬁgures in this article are available at

https://doi.org/10.1109/TCOMM.2021.3051897.

Digital Object Identiﬁer 10.1109/TCOMM.2021.3051897

network that targets at meeting more stringent requirements

than 5G, such as u ltra high data rate and energy efﬁciency,

global coverage and connectivity, as well as extremely high

reliability and low latency [1]. These requirements, however,

may not be fully achieved with the existing technology

trends for accommodating 5G services (e.g., enhanced mobile

broadband (eMBB), ultra-reliable and low latency communi-

cation (URLLC), and massive machine-type communication

(mMTC)), which mainly include [2]–[5]

• deploying increasingly more active nodes such as base

stations (BSs), access points (APs), relays, and distributed

antennas/remote radio heads (RRHs) to shorten the com-

munication distance for achieving enhanced network cov-

erage and capacity, which, however, incurs higher energy

consumption and deployment/backhaul/maintenance cost,

as well as the more severe and complicated network

interference issues;

• packing substantially more antennas at the

BSs/APs/relays to harness the enormous massive

multiple-input-multiple-output (M-MIMO) gains, which

requires increased hardware and energy cost as well as

signal processing complexity;

• migrating to higher frequ ency bands such as millimeter

wave (mmWave) and even terahertz (THz) frequencies

to utilize their large and available bandwidth, which

inevitably results in deploying even more active nodes

and mounting them even more antennas (i.e., super

MIMO) so as to compensate for their higher propagation

loss over distance.

In view of the above issues and limitations, it is imperative

to develop disruptively new and innovative technologies to

achieve a sustainable capacity growth of future wireless net-

works with low and affordable cost, complexity, an d energy

consumption.

On the other hand, the fundamental challenge for achieving

ultra-reliable wireless communications arises from the time-

varying wireless channels due to user mobility. Traditional

approaches for tackling this challenge either compensate for

the channel fading b y exploiting various modulation, coding

and diversity techniques, or adapt to it via adaptive power/rate

control and beamforming techniques [6], [7]. However, they

not only need additional overhead but also have limited control

over the largely random wireless channels, thus leaving the

ultimate barrier to achieving high-capacity and ultra-reliable

wireless communications unconquered.

See https://www .ieee.org/publications/rights/index.html for more information.

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3314 IEEE TR ANSACTIONS ON COMMUNICATIONS, VOL. 69, NO. 5, MAY 2021

Fig. 1. Main functions of IRS for wireless channel reconﬁguration.

B. What Is IRS?

Motivated by the above, intelligent reﬂecting surface (IRS)

has recently emerged as a promising new paradigm to achieve

smart and reconﬁgurable wireless channels/radio propagation

environment for B5G/6G wireless communication systems

[8]–[10]. Generally speaking, IRS is a planar surface com-

prising a large number of passive reﬂecting elements, each

of which is able to induce a controllable amplitude and/or

phase change to the incident signal independently. By densely

deploying IRSs in wireless network and smartly coordinat-

ing their reﬂections, the signal propagation/wireless channels

between transmitters and receivers can be ﬂexibly recon-

ﬁgured to achieve desired realizations and/or distributions,

which thus provides a new means to fundamentally tackle

the wireless channel fading impairment and interference issue,

and potentially achieves a quantum leap improvement for

wireless communication capacity and reliability. As shown

in Fig. 1, IRS is able to achieve several appealing functions

in wireless channel reconﬁguration, such as creating virtual

line-of-sight (LoS) link to bypass obstacles between trans-

ceivers via smart reﬂection, adding extra signal paths toward

desired direction to improve the channel rank condition,

reﬁning the channel statistics/distribution by e.g. transfo rming

Rayleigh/fast fading to Rician/slow fading for ultra-high reli-

ability, suppressing/nulling co-channel/inter-cell interference

and so on.

Not only conceptually appealing, IRS also possesses various

practical advantages for implementation. First, its reﬂecting

elements (e.g., low-cost printed dipoles) only passively reﬂect

the impinging signals without requiring any transmit radio-

frequency (RF) chains, thus can be implemented/operated with

orders-of-magnitude lower hardware/energy cost as compared

to traditional active antenna arrays or the recently proposed

active surfaces [11]. Besides, IRS operates in full-duplex

(FD) mode and is free of any antenna noise ampliﬁcation

as well as self-interference, wh ich thus offers competitive

advantages over traditional active relays, e.g., half-duplex

(HD) relay that suffers from low spectral efﬁciency as well

as FD relay that needs sophisticated techniques for self-

interference cancellation. Furthermore, since IRS is generally

of low proﬁle, light weight, and conformal geometry, it can

be easily mounted on/removed from environment objects for

deployment/replacement. Finally, IRS serves as an aux iliary

device in wir eless networks and can be integrated into them

transparently, thus p roviding great ﬂexibility and compatibility

with existing wireless systems (e.g., cellular or WiFi).

Due to the above promising advantages, IRS is suitable to

be massively deployed in wireless networks to signiﬁcantly

enhance its spectral and energy efﬁciency cost-effectively.

As such, it is envisioned that IRS will lead to fundamental

paradigm shifts of wireless system/network designs, namely,

from the existing M-MIMO system without IRS to the new

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WU et al.: IRS-AIDED WIRELESS COMMUNICATIONS: A TUTORIAL 3315

Fig. 2. Potential paradigm shifts of wireless system/network designs with IRS.

IRS-aided small/mod erate MIMO system, as well as from

the existing heterogenous wireless network to the new IRS-

aided hybrid network in the future, as shown in Figs. 2 (a)

and (b), respectively. On one hand, different from M-MIMO

that leverages tens and even hundreds of active antennas to

generate sharp beams directly, an IRS-aided MIMO system

allows the BS to be equipped with substantially less antennas

without compromising the users’ quality-of-service (QoS),

by exploiting the large aperture of IRS to create ﬁne-grained

reﬂect beams via smart passive reﬂections [9]. As such,

the system hardware cost and energy consumption can be sig-

niﬁcantly reduced, especially for wireless systems migrating

to higher frequency bands in the future. On the other hand,

although existing wireless networks rely on a heterogenous

multi-tier architecture consistin g of macro and small BSs/APs,

relays, distributed antennas, etc., they are all active nodes that

generate new signals in the network, thus requiring sophisti-

cated coordination and interference management among them

in order to achieve the premise of enhanced network spatial

capacity with more active nodes deployed. However, this

approach inevitably aggravates the network operation overhead

and thus may not be able to sustain the wireless network

capacity growth cost-effectively in the future. In contrast,

integrating IRSs into wireless network will shift the existing

heterogeneous network with active components only to a

new hybrid architecture comprising both active and passive

components co-working in an intelligent way. Since IRSs are

of much lower cost as compared to their active counterparts,

they can be more densely deployed in wireless network at even

lower cost, yet without the need of sophisticated interference

management between IRSs thanks to their passive reﬂection

and resultant local coverage. By optimally setting the ratios

between active BSs and passive IRSs deployed in the hybrid

network given their total cost, a sustainable n etwork capacity

scaling with cost can be achieved [12].

In Fig. 3, we show an envisioned future wireless network

aided by IRSs with a variety of promising app lications. For

instance, IRS is p articularly useful for the coverage extension

in mmWave and THz communications that are highly vulner-

able to blockage. Besides, deploying IRSs at the cell edge not

only helps improve the desired signal p ower at cell-edge users

but also facilitates the suppression of co-channel interference

from n eighboring cells to them. Moreover, to improve the

efﬁciency of simultaneous wireless information and power

transfer (SWIPT) from the AP to wireless devices in e.g.,

smart ofﬁce/home, the large aperture of IRS can be leveraged

to compensate for the signiﬁcant power loss over long distance

via reﬂect beamforming to its nearby devices. In indoor

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3316 IEEE TR ANSACTIONS ON COMMUNICATIONS, VOL. 69, NO. 5, MAY 2021

Fig. 3. Illustration of IRS applications in future wireless network.

environment, IRS can also be attached to the ceilings, walls,

furniture, and even behind the paintings/decorations, to help

achieve enhanced coverage and high-capacity hot-spot, which

is particularly appealing for eMBB and mMTC a pplications

in smart factories, stadiums, shopping centers, airports, etc.

In particular, for mMTC scenarios usually with only a small

fraction of d evices that are active for communication at each

time instant, IRS can be leveraged to effectively improve the

device activity detection accuracy and efﬁciency by exploiting

its extra controllab le path s, especially when massive devices

are distributed in different propagation conditions [13]. While

in outdoor environment, IRS can be coated on the building

facade, lamppost, advertising board, and even the surface of

high-speed moving vehicles, to support various applications,

e.g., URLLC for remote control and smart transportation

by effectively compensating the Doppler and delay spread

effects [14]–[16]. For instance, the communication reliability

can be greatly improved by exploiting IRS to turn typically

random wireless channels into more deterministic ones, thus

helping reduce packet retransmissions and minimize the delay

which is critical for URLLC applications. Therefore, IRS

is a disruptive technology for making our current “dumb”

enviro nment in telligent, which can potentially b eneﬁt a wide

range of vertical industries in 5G/6G such as transportation,

manufacturing, smart city, etc. Recently, IRS has been recog-

nized as one promising technology for the future 6G ecosystem

[1], [17], [18]. In addition, there has been an upsurge of

interest in industry on implementing and commercializing

IRS-like technologies to create new value chains, and in

the meanwhile, several pilot projects have been launched

to advance the research in this new ﬁeld, with their more

information given in Table I. It is worth no ting that there

have been other terminologies similar to IRS named in the

literature, such a s intelligent wall [19], smart reﬂectarray [20 ],

and reconﬁgurable metasurface/intellig ent surface (RIS) [21],

[22], large intellig ent surface/antennas (LISA) [23], RFocus

[24], among others, which, despite different terminologies, are

essentially based on the same principle of passive and tunable

reﬂecting/refracting surfaces.

C. What’s New?

Although IRS can be regarded as a reconﬁgurable metasur-

face, it extends the traditional applications of metasurface via

controlling electromagnetic (EM) waves such as invisibility

cloaking, imaging, radar sensing, and hologram [33], to the

new frontier of wireless communication as an innovative

enabler for smart and reconﬁgurable propagation environment.

Moreover, compared with the traditional reﬂectarray [34]

where a passive mirror/lens with ﬁxed/reconﬁgurable beam

patterns is placed in the near ﬁeld of the wireless transceiver

for saving the active antennas/RF chains, IRS is ﬂexibly

located in the network to help alter the wireless communi-

cation channel via smart reﬂection. As such, the design of

IRS-aided wireless systems/networks faces new and unique

challenges that arise from a communication standpoint, which

are elaborated as follows.

Firstly, the passive reﬂections of all reﬂecting elements at

each IRS need to be properly designed to achieve cooper-

ative signal focusing a nd/or interfe rence cancellation in its

local proximity. Meanwhile, to serve all the users in the

network regardless of whether there is any associated IRS

nearby each user, the IRS passive reﬂections also need to be

jointly designed with the BSs’/users’ transmissions so as to

optimize their end-to-end communications over the reconﬁg-

ured wireless channels by IRSs. Secondly, as IRS does not

Also note that such surfaces can be practically manufactured as a mir-

ror or lens for signal reﬂection and refraction, respectively, depending on the

application scenarios where wireless transmitter and receiver are located on

the same side of the surface, or on its opposite sides.

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WU et al.: IRS-AIDED WIRELESS COMMUNICATIONS: A TUTORIAL 3317

TABLE I

IST OF MAI N INDUS TRY PROGRESS,PROTOTYPE S , AND PRO JECTS RELATED TO IRS

possess RF chains in general, how to acquire the channel state

information (CSI) between IRS and its serving BSs/users that

is essential to the afo rementioned IRS reﬂection optimization

becomes a practically difﬁcult task, especially considering that

IRS typically has a large number of reﬂecting elements and

thus associated channel coefﬁcients to be estimated. Thirdly,

the optimal deployment strategy of IRSs in wireless network to

maximize the network capacity is expected to be signiﬁcantly

different from that for the conventional wireless networks with

active BSs/APs and relays only due to their different array

architectures (passive versus active) and operating mechanisms

(reﬂect versus transmit/receive), which thus needs to be r e-

investigated thoroughly. In a nutshell, the efﬁcient integration

of IRSs into wireless network brings both new opportunities as

well as challenges, which deserve a new and dedicated study.

D. Objective, Contribution, and Organization

The promising prospects of IRS for future wireless networks

have spurred extensive research recently. A handful of articles

have appeared in the literature providing overview or survey

of the research work on IRS and its variants from different

perspectives, such as IRS implementation, channel modelling,

applications, etc. [10], [21]–[23], [35]–[42], which are sum-

marized in Table II for ease o f reference.

Compared with the above works, this paper is the ﬁrst tuto-

rial on IRS-aided wireless communications, with an emphasis

on addressing its main technical challenges from a com-

munication standpoint. In addition to reviewing the state-of-

the-art results on IRS, this paper aims to provide in-depth

technical discussion to facilitate and inspir e future research in

modelling, analysis, design, optimization, and implementation

of IRS-aided wireless networks. To this end, this tutorial paper

offers a new and systematic treatment on how to address three

key design issues in them, namely, IRS passive reﬂection

optimization, IRS channel estimation, and IRS deployment

from various communication perspectives.

The rest of this paper is organized as follows. Section II

introduces the fundamentals of IRS-aided wireless commu-

nication, including its signal and channel models, hard-

ware architecture as well as practical constraints. Section III

addresses the passive reﬂection optimization for IRS-aided

wireless communication under various system setups, namely,

from single-user to m ulti-user, single-antenna to m ulti-

antenna, narrow-band to broadband, as well as from single-

cell to multi-cell. In Section IV, we present promising methods

for IRS channel estimation under different IRS conﬁgurations

and communication setups. In Section V, we address the

optimal deployment of IRSs at both the link and network

levels. In Section VI, we discuss other relevant topics on IRS

for broadening its scope. Finally, we conclu de this paper in

Section VII.

Notations: In this paper, scalars are denoted by italic letters,

vectors and matrices are d enoted by bold-face lower-case and

upper-case letters, respectively. C

x×y

denotes the space of

x × y complex-valued matrices. For a complex-valued vector

x, x denotes its Euclidean norm and diag(x) denotes a

diagonal matrix with the elements in x on its main diagonal.

The distribution of a circularly symmetric complex Gaussian

(CSCG) random vector with mean vector x and covariance

matrix Σ is denoted by CN (x, Σ);and∼ stands for “dis-

tributed as”. For a square m atrix S, tr(S) and S

−1

denote

its trace and inverse, respectively, while S  0 means that S

is positive semi- deﬁnite, where 0 is a zero matrix of proper

size. For a general matrix A, A

∗

, A

, rank(A),and[A]

i,j

denote its conjugate, conjugate transpose, rank, and the (i, j)th

entry, respectively.  denotes the imaginary unit, i.e., 

= −1.

E(·) denotes the statistical expectation. Re{·} denotes the real

part of a complex number. The operator mod(a, b) returns the

remainder after division of a by b,wherea is the dividend

and b is the divisor;  denotes the convolution operation; x

and x return the nearest integer less than or equal to, and

greater than or equal to the real number x, respectively.

II. IRS F

UNDAMENTALS

In this section, we present the basics pertinent to

IRS-aided wireless communication, where the fundamen-

tal IRS signal and channel models are ﬁrst introduced,

followed b y its hardware architecture and practical con-

straints as well as their induced issues worthy of future

investigation.

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