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Reconfigurable Intelligent Surfaces: Potentials, Applications, and Challenges for 6G Wireless Networks

Sarah Basharat, Syed Ali Hassan, Haris Pervaiz, Aamir Mahmood, Zhiguo Ding, Mikael Gidlund

arXiv:2107.05460v1cs.ITeess.SP

TL;DR

RIS is surveyed as a low-cost, energy-efficient approach for smart 6G wireless environments. The paper reviews its integration with emerging technologies, practical challenges, and a RIS-assisted NOMA case study under imperfect CSI. The case study highlights channel-estimation quality and RIS size as important performance considerations.

  • Problem

    6G networks require energy- and spectral-efficient solutions and smart, reconfigurable wireless environments, motivating RIS integration with future communication systems.

  • Method

    The paper provides a tutorial overview of RIS integration with emerging technologies, practical implementation challenges, and RIS-assisted NOMA under imperfect CSI.

  • Results

    The case study shows that channel-estimation errors reduce total spectral efficiency, while more RIS elements can improve spectral efficiency and reduce required transmit power.

  • Takeaways & Limitations

    RIS performance depends on channel-estimation quality, RIS element count, deployment choices, and integration with communication technologies such as NOMA and SWIPT.

Abstract

from arXiv · show

Reconfigurable intelligent surfaces (RISs), with the potential to realize a smart radio environment, have emerged as an energy-efficient and a cost-effective technology to support the services and demands foreseen for coming decades. By leveraging a large number of low-cost passive reflecting elements, RISs introduce a phase-shift in the impinging signal to create a favorable propagation channel between the transmitter and the receiver.~\textcolor{black}{In this article, we provide a tutorial overview of RISs for sixth-generation (6G) wireless networks. Specifically, we present a comprehensive discussion on performance gains that can be achieved by integrating RISs with emerging communication technologies. We address the practical implementation of RIS-assisted networks and expose the crucial challenges, including the RIS reconfiguration, deployment and size optimization, and channel estimation. Furthermore, we explore the integration of RIS and non-orthogonal multiple access (NOMA) under imperfect channel state information (CSI). Our numerical results illustrate the importance of better channel estimation in RIS-assisted networks and indicate the various factors that impact the size of RIS. Finally, we present promising future research directions for realizing RIS-assisted networks in 6G communication.

I. INTRODUCTION

RIS is presented as an energy- and cost-efficient technology for smart, reconfigurable 6G wireless environments, while the paper surveys its integration opportunities and practical challenges.

  • RIS concept: RIS can improve QoS and connectivity by making the wireless environment a controllable network-design parameter rather than a stochastic factor.
  • RIS concept: RIS uses low-cost passive reflecting elements to alter incident-signal phases and create favorable propagation between transmitters and receivers.A smart controller coordinates element reconfiguration, enabling reflected and direct signals to combine coherently or destructively.
  • Study scope: The paper discusses RIS integration with NOMA, SWIPT, UAVs, BackCom, mmWaves, and multi-antenna systems for 6G networks.
  • Practical challenges: Practical RIS deployment requires addressing reconfiguration, deployment and size optimization, and channel estimation.
  • Case study: A RIS-assisted NOMA case study under imperfect CSI examines channel-estimation errors and factors affecting the number of RIS elements.
  • Future research: The study introduces five research directions intended to guide future work on RIS-assisted networks.

II. INTEGRATING RIS WITH EMERGING COMMUNICATION

The paper examines performance gains from integrating RIS with emerging communication technologies as part of its discussion of RIS opportunities for 6G.

  • RIS is presented as a cutting-edge technology opening research opportunities toward 6G.
  • The section focuses on performance gains achievable by integrating RIS with emerging communication technologies.
  • RIS integration with emerging technologies is discussed in the context of future 6G wireless networks.

A. RIS and NOMA

The paper connects RIS with NOMA to support future-generation connectivity, while highlighting a trade-off between phase-shifting performance and implementation complexity.

  • RIS and NOMA: NOMA allows multiple users to share the same resource block through power-domain superposition coding and successive interference cancellation.
  • RIS and NOMA: RIS-assisted NOMA requires jointly designing the phase-shift matrix and beamforming vectors to exploit its potential gains.
  • RIS and NOMA: Coherent phase shifting offers superior performance, whereas random phase shifting reduces CSI-acquisition overhead and system complexity.
  • RIS and SWIPT: RIS can compensate practical SWIPT limitations by boosting signal strength at both information and energy receivers.
  • RIS and SWIPT: RIS-assisted SWIPT studies formulate joint optimization of base-station precoding matrices and RIS phase shifts under weighted sum-rate maximization.

C. RIS and UAVs

RIS is described as improving UAV and BackCom links by shaping propagation and reducing power or interference, with deployment location affecting UAV gains.

  • RIS and UAVs: RIS can create virtual line-of-sight paths between UAVs and blocked ground users in dense urban environments.Joint optimization of RIS beamforming and UAV trajectory can significantly enhance received signal power.
  • RIS and UAVs: Optimized RIS reflections can direct cellular base-station signals toward UAVs whose links otherwise rely on antenna side lobes.Even a small RIS on a building facade can substantially improve UAV communication; optimal location depends on distance and deployment height.
  • RIS and BackCom: RIS-assisted BackCom is presented as a way to address short operating range through joint optimization of RIS phase shifts and source transmit beamforming.
  • RIS and BackCom: RIS can reduce transmit power in BackCom, which can be mapped to improved operational range.
  • RIS and BackCom: A DDPG-based deep-reinforcement-learning approach jointly optimizes RIS and reader beamforming for ambient BackCom without channel or ambient-signal knowledge.

E. RIS and mmWaves

RIS-enhanced mmWave systems address blockage vulnerability by providing additional propagation paths. In RIS-assisted mmWave-NOMA, joint optimization improves coverage, particularly when direct BS-user links are blocked.

  • E. RIS and mmWaves: RIS can overcome mmWave blockage limitations by introducing effective additional propagation paths.High mmWave directivity makes communication vulnerable to blockage, especially indoors and in dense urban environments.
  • E. RIS and mmWaves: Joint optimization of beamforming vectors and power allocation via successive convex approximation supports RIS-assisted mmWave-NOMA design.
  • E. RIS and mmWaves: RIS enhances the coverage range of mmWave-NOMA systems, especially when direct BS-user links are blocked.

F. RIS and Multi-antenna Systems

RIS-aided multi-antenna systems seek improved performance with lower hardware and energy costs. Hybrid beamforming studies show that RIS size and phase quantization affect performance while reducing dedicated hardware requirements.

  • F. RIS and Multi-antenna Systems: RIS can improve MISO network performance with significantly lower hardware cost and energy consumption than conventional multi-antenna approaches.
  • F. RIS and Multi-antenna Systems: A two-step sum-rate maximization algorithm designs continuous digital beamforming and discrete analog beamforming for multi-user RIS-assisted MISO systems.
  • F. RIS and Multi-antenna Systems: System performance depends on RIS size and the number of quantization bits for discrete phase-shifts.
  • F. RIS and Multi-antenna Systems: RIS-based hybrid beamforming can reduce dedicated hardware requirements while providing satisfactory sum-rate.

A. RIS Reconfiguration for Controllable Reflections

Practical RIS design requires balancing controllable reflection quality, deployment constraints, and channel-estimation overhead. Discrete reflection settings, placement decisions, and imperfect CSI all affect implementation and performance.

  • A. RIS Reconfiguration for Controllable Reflections: Finite discrete phase-shift and amplitude levels reduce hardware cost, design complexity, and control overhead compared with continuous reflection control.Continuous reflection variation is communication-beneficial, but massive RIS implementations make high-resolution control costly and complex.
  • B. RIS Deployment and Size Optimization: RIS deployment must account for propagation conditions, deployment cost, user distribution, and available space when selecting grouping or partitioning strategies.
  • C. Channel Estimation in RIS-assisted Networks: Element-by-element ON/OFF channel estimation activates one RIS element at a time while the others remain OFF.The BS estimates channels and communicates CSI to the RIS controller, which adjusts the phase-shifts.
  • C. Channel Estimation in RIS-assisted Networks: Low-overhead channel-estimation protocols for future networks with many users remain an open problem.Existing works commonly assume perfect CSI, although channel-estimation errors should be included for accurate performance analysis.

IV. RIS-ASSISTED NOMA NETWORK UNDER IMPERFECT CSI: A CASE STUDY

The case study models a blocked RIS-assisted downlink PD-NOMA cluster with imperfect CSI and ordered users. It examines how RIS reflections and estimation errors affect the modeled wireless links and interference.

  • IV. RIS-ASSISTED NOMA NETWORK UNDER IMPERFECT CSI: A CASE STUDY: The considered system is a downlink PD-NOMA network with a single-antenna macro BS, an RIS with N passive elements, and clustered single-antenna users.The RIS is connected to a smart controller, and users are uniformly distributed and grouped into clusters.
  • IV. RIS-ASSISTED NOMA NETWORK UNDER IMPERFECT CSI: A CASE STUDY: The case study focuses on one blocked cluster containing K users, ordered from strongest to weakest by overall channel gain.Under fixed power allocation, the strongest user receives the minimum transmit-power share and the weakest receives the maximum.
  • IV. RIS-ASSISTED NOMA NETWORK UNDER IMPERFECT CSI: A CASE STUDY: BS-user and RIS-user links are modeled with Rayleigh fading, path loss, and AWGN, while channel estimates follow an MMSE error model.Estimation quality is represented by the channel-estimation error variance.
  • IV. RIS-ASSISTED NOMA NETWORK UNDER IMPERFECT CSI: A CASE STUDY: Channel-estimation error is treated as interference in the system and adversely affects performance.

A. Impact of Imperfect CSI and RIS Elements

RIS-assisted NOMA performance depends on channel-estimation quality, RIS size, target spectral efficiency, user count, and transmit power. Increasing RIS elements can improve spectral efficiency, reduce transmit power, and lower the elements needed relative to RIS-OMA.

  • Impact of imperfect CSI: Higher channel-estimation error variance decreases total spectral efficiency, while more RIS elements can sustain high spectral efficiency under imperfect CSI.The error acts as a source of interference, whereas additional reflecting elements provide performance gains.
  • Impact of RIS elements: For per-user target spectral efficiency of 1 bps/Hz, increasing RIS elements from 150 to 300 reduces transmit power from 32 dBm to about 26 dBm, a 6 dB gain.For a fixed target spectral efficiency, transmit power decreases as the number of RIS elements increases.

A. RIS-assisted Terahertz (THz) Communication

RIS can address coverage and blockage challenges across THz, aerial, and secure wireless systems, but each integration introduces distinct channel, placement, or estimation challenges. The discussed settings therefore require designs that account for their specific propagation and CSI constraints.

  • A. RIS-assisted Terahertz (THz) Communication: THz signals suffer severe attenuation and communication interruptions, motivating RIS use for better coverage but requiring exploitation of THz-specific propagation properties.The propagation challenge remains unresolved for RIS-assisted THz communication.
  • A. RIS-assisted Terahertz (THz) Communication: Aerial RISs can serve more users and mitigate blockages through full-space reflections, likely favorable LoS conditions, and UAV mobility.Their practical deployment introduces three-dimensional placement and channel-estimation challenges.
  • A. RIS-assisted Terahertz (THz) Communication: RIS-assisted physical-layer security can boost the intended-user beam while suppressing the unintended-user beam, but it requires difficult eavesdropper-to-RIS and eavesdropper-to-BS channel information.The paper identifies channel estimation and passive beamforming under imperfect CSI as necessary design challenges.

D. RIS-assisted Optical Wireless Communication

RIS integration extends the potential of optical wireless and massive MIMO systems by addressing propagation or implementation constraints. The paper situates these integrations within a broader 6G overview of RIS applications and challenges.

  • D. RIS-assisted Optical Wireless Communication: RIS can mitigate optical wireless line-of-sight blockages by directing the optical beam toward a desired direction.This supports RIS-assisted OWC in both indoor and outdoor scenarios.
  • D. RIS-assisted Optical Wireless Communication: RIS offers a potentially energy-efficient and cost-effective way to complement massive MIMO despite massive MIMO’s high hardware cost and power consumption.The paper identifies low-complexity beamforming and resource-allocation algorithms as an open need.
  • D. RIS-assisted Optical Wireless Communication: The article overviews RIS performance gains with OWC, NOMA, SWIPT, UAVs, BackCom, mmWaves, and multi-antenna systems while highlighting practical integration challenges.Its case study emphasizes channel estimation and factors affecting RIS size under imperfect CSI.
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