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A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks
Jun Zhao
TL;DR
IRS research addresses how programmable reflecting surfaces can improve wireless efficiency for prospective 6G networks, while the literature remains broad and heterogeneous. This paper surveys and categorizes studies across communications, implementations, and applications, then identifies future research directions. It concludes that IRSs are a promising technology for facilitating 6G communications and may play a fundamental role in 6G networks.
Problem
The paper addresses the need to organize rapidly growing research on IRSs as a promising technology for improving spectrum and energy efficiency in 6G communications.
Method
The paper surveys and categorizes IRS studies across communication analyses and optimizations, implementations, secure communications, terminal positioning, and other applications.
Results
The survey identifies IRSs as a promising technology for facilitating 6G wireless communications and envisions a fundamental role for them in 6G networks.
Takeaways & Limitations
The paper provides a timely synthesis of IRS research and identifies future directions concerning real-world validation, physical-material-aware modeling, and performance scaling laws.
Abstract
from arXiv · showhide
Intelligent reflecting surfaces (IRSs) tune wireless environments to increase spectrum and energy efficiencies. In view of much recent attention to the IRS concept as a promising technology for 6G wireless communications, we present a survey of IRSs in this paper. Specifically, we categorize recent research studies of IRSs as follows. For IRS-aided communications, the summary includes capacity/data rate analyses, power/spectral optimizations, channel estimation, deep learning-based design, and reliability analysis. Then we review IRSs implementations as well as the use of IRSs in secure communications, terminal-positioning, and other novel applications. We further identify future research directions for IRSs, with an envision of the IRS technology playing a critical role in 6G communication networks similar to that of massive MIMO in 5G networks. As a timely summary of IRSs, our work will be of interest to both researchers and practitioners working on IRSs for 6G networks.
I. INTRODUCTION
IRSs are presented as a 6G technology that tunes wireless propagation to support more efficient and reliable communications. The survey distinguishes IRSs from related surface concepts and organizes the paper around recent studies and future directions.
- An intelligent reflecting surface comprises independently controllable units that modify incident signals, including their phase, amplitude, or frequency.
- IRS-aided links can connect a base station with mobile users, UAVs, smart vehicles, or other terminals, including when a tree blocks the line of sight.
- Unlike massive MIMO, which uses large antenna arrays, IRSs tune the wireless propagation environment for communication.
- The survey uses IRS as its consistent name, distinguishing it from actively transmitting large intelligent surfaces and frequency selective surfaces used for different purposes.
- The paper classifies recent IRS studies, compares its coverage with earlier reviews, identifies future research directions, and concludes with the survey’s findings.
II. CATEGORIZING RECENT STUDIES OF INTELLIGENT REFLECTING SURFACES
The survey categorizes recent IRS research across IRS-aided communications, implementations, and applications beyond conventional data transmission.
- The communication categories cover capacity and data rates, power and spectral optimization, channel estimation, deep-learning design, and reliability analysis.
- The survey also reviews IRS implementations and applications in secure communications, terminal positioning, and other novel areas.
A. Capacity/data rate analyses of IRS-aided communications
Capacity and data-rate research examines IRS-enabled information transfer under idealized, impaired, estimated, correlated, and multi-user channel conditions. Optimization studies jointly design active base-station transmission and passive IRS configurations.
- Capacity per square metre is linearly proportional to average transmit power rather than logarithmically related as in massive MIMO.
- For sufficiently large surfaces, matched filtering yields a sinc-function-like intersymbol-interference channel, with capacity per square metre converging to 2N0 as λ tends to zero.
- Splitting an IRS into multiple small units can mitigate capacity degradation caused by hardware impairments.
- Asymptotic uplink analysis incorporates channel-estimation errors, spatially correlated Rician fading, interference channels, and channel hardening.
- Multi-user designs optimize IRS phases with base-station beamforming, precoding, or power allocation to maximize weighted sum rates or SINR, sometimes using discrete phase shifts.
B. Power/spectral optimizations in IRS-aided communications
Power and spectral-efficiency studies formulate IRS-aided downlink designs around energy efficiency, transmit-power reduction, and spectral-efficiency maximization, jointly tuning base-station transmission and IRS phase shifts.
- The surveyed optimization studies target power or spectral efficiency in IRS-aided communications.
- Bit-per-Joule energy efficiency is maximized by jointly selecting the IRS phase matrix and base-station power allocation under zero-forcing precoding.
- Downlink transmit-power minimization optimizes both base-station beamformers and the IRS phase-shift matrix in a multiple-access network.
- Spectral-efficiency maximization can optimize access-point beamforming and IRS phases, while pilot-training structure affects achievable spectral efficiency when channel state information is acquired through pilots.
C. Channel estimation for IRS-aided communications
Channel estimation studies address passive IRS constraints by estimating channels at the base station and configuring IRS phase shifts, using staged protocols, active elements, or structured recovery methods.
- Passive IRSs rely on base-station downlink estimation, after which the IRS controller sets the phase shifts.
- An MMSE protocol estimates direct channels with all IRS units OFF, then estimates each IRS element while it is individually ON.
- Compressive sensing and deep learning estimate channels at passive IRS units from channels observed at active units connected to the controller baseband.
- A three-stage MIMO method uses sparse matrix factorization, ambiguity elimination, and matrix completion to recover the BS–IRS and IRS–user channels.The stages use bilinear generalized approximate message passing, greedy pursuit, and Riemannian manifold gradient algorithms, respectively.
- Channel estimation for IRS-aided energy transfer supports active energy beamforming at the power beacon and passive energy beamforming at the IRS.The protocol divides estimation into sub-phases with only one IRS unit ON in each sub-phase.
- Channel-estimation errors are incorporated into uplink data-rate analysis for IRS-aided communications.
D. Deep learning-based design for IRS-aided communications
Deep learning is used both to configure IRSs for wireless communications and to infer channel qualities at units whose channels are not directly observed.
- Wireless propagation is modeled as a deep neural network whose neurons are IRS units and whose links are their cross-interactions.Training on data enables the network to learn propagation basics and configure the IRS to an optimal setting.
- A deep neural network estimates channel qualities at all IRS units using observations from active units connected to the IRS controller baseband.Deep learning also guides the IRS in learning its optimal interaction with incident signals.
E. Reliability analysis of IRS-aided communications
Reliability analysis characterizes IRS-aided communications through rate distributions and outage probabilities, while related implementation studies examine surface geometry and active/passive architectures.
- E. Reliability analysis of IRS-aided communications: Uplink reliability is analyzed through the data sum-rate distribution and the probability that the sum-rate falls below a desired value.The distribution is obtained using the Lyapunov central limit theorem, and the resulting outage probability supports outage computation.
- F. IRS implementations: The hexagonal lattice minimizes IRS surface area for a desired number of independent signal dimensions when each antenna provides one signal-space dimension.
- F. IRS implementations: The lattice analysis draws on classical lattice theory, which also has applications in information theory, cryptography, machine learning, and knowledge representation.
- F. IRS implementations: IRS architectures can combine active units connected to the controller baseband with passive units that are not connected.Channel information from active units captures environmental conditions and sender/receiver locations for system optimization.
G. IRSs for secure communications
IRSs are studied for physical-layer security by shaping rates toward legitimate receivers and eavesdroppers, with optimization extending from one receiver and eavesdropper to multiple users.
- IRS-assisted secure communications build on wiretap-channel models including broadcast, compound, Gaussian, and MIMO variants.
- An IRS can increase a legitimate receiver’s data rate while decreasing an eavesdropper’s, improving the secrecy data rate.The secrecy data rate is the legitimate receiver’s rate minus the eavesdropper’s rate.
- For one legitimate receiver and one eavesdropper, studies jointly design base-station transmit beamforming and IRS phase shifts.One approach alternates between exact transmit-beamforming updates and approximate IRS phase-shift optimization.
- For multiple legitimate receivers and eavesdroppers, optimization maximizes the minimum secrecy data rate among all legitimate receivers.The design jointly searches for transmit beamforming and IRS phase shifts in an IRS-aided downlink broadcast system.
H. IRSs for terminal-positioning and other novel applications
The survey describes IRS applications beyond conventional communications, including terminal positioning, index modulation, over-the-air computation, access-point functionality, and wireless energy transfer.
- Terminal-positioning: IRS-aided terminal positioning yields CRLBs that generally decrease quadratically with IRS surface area.For a terminal perpendicular to the IRS center, the distance CRLB instead decreases linearly with surface area.
- Index modulation: IRS-space shift keying and IRS-spatial modulation convey information by selecting receive antenna indices according to information bits.These schemes realize index modulation through IRS-assisted spatial signaling.
- Over-the-air computation: IRS-assisted AirComp jointly optimizes IRS phase shifts and base-station decoding vectors to minimize mean squared decoding distortion.An alternating difference-of-convex algorithm is proposed to address the non-convex optimization problem.
- IRS as an access point: An IRS can operate as an access point by reflecting an unmodulated carrier from a nearby RF signal generator and encoding bits through adjustable phase shifts.
- Wireless energy transfer: IRS-assisted wireless energy transfer combines channel estimation with energy beamforming at a multi-antenna power beacon and the IRS.The application targets power delivery from the beacon to a single-antenna user.
III. RECENT REVIEWS OF INTELLIGENT REFLECTING SURFACES AND RELATED TECHNOLOGIES
The paper distinguishes its survey from earlier reviews by combining broad categorization with coverage of implementations, applications, and future research directions. It identifies real-world validation, physically grounded modeling, and scaling laws as major research needs.
- Recent reviews: Earlier reviews emphasize mathematical IRS communication analyses, artificial intelligence, or design and implementation challenges, whereas this paper provides a broader categorization.
- Related technologies: Other reviews focus on physical IRS implementations, massive MIMO 2.0, or technologies related to massive MIMO.
- Future research directions: The paper envisions IRSs playing a fundamental role in 6G networks similar to massive MIMO's role in 5G.
- Future research directions: Most existing IRS communication studies rely on theoretical analyses validated with simulations, motivating confirmation through real-world implementations and experiments.
- Future research directions: Existing models of IRS signal transformation are simple despite dependence on physical materials and manufacturing processes.More physically informed models can guide IRS optimization more accurately.
- Future research directions: Scaling laws are needed to establish fundamental performance limits for IRS-aided communications.The paper links this need to understanding how IRSs affect traditional information-theoretic models.
V. CONCLUSION
The survey categorizes IRS research and identifies future directions while presenting IRSs as a promising technology for improving spectrum and energy efficiencies in 6G communications.
- The survey categorizes IRS studies and identifies future research directions for IRS technology.
- IRSs are presented as a promising technology that induces smart radio environments to increase spectrum and energy efficiencies.