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Reconfigurable Intelligent Surface for Physical Layer Security in 6G-IoT: Designs, Issues, and Advances

Waqas Khalid, M. Atif Ur Rehman, Trinh Van Chien, Zeeshan Kaleem, Howon Lee, Heejung Yu

arXiv:2311.08112v1cs.NI

TL;DR

Resource-constrained IoT devices may not support complex transmitter and receiver designs, and PLS can be ineffective under unfavorable propagation conditions. The article surveys RIS-aided PLS designs and their research challenges for 6G-IoT. Its simulations report improved secrecy with RIS and show that secrecy rate increases as the number of RIS elements increases.

  • Problem

    Resource-constrained IoT devices may not support complex transmitter and receiver designs, and PLS may be ineffective under unfavorable propagation conditions.

    Source: §I · p. 2

  • Method

    The article reviews RIS-aided PLS against eavesdropping and jamming and presents designs using resource allocation, beamforming, artificial noise, and cooperative communication.

    Source: §I · p. 2

  • Results

    The simulations report higher secrecy rate with RIS than with a direct link alone, and secrecy rate increases while secrecy outage probability decreases as RIS elements increase.

    Source: C. Simulation Results · p. 8

  • Takeaways & Limitations

    RIS-aided PLS design choices can be matched to the scenario and communication objective while balancing security performance and implementation complexity.

    Source: §I · p. 2

    RIS modeling remains an active area, with limited research incorporating element coupling, impedance matching, hardware impairments, and element scattering properties.

    Source: 1) Research Issues · p. 10

Abstract

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Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including STAR-RIS and malicious RIS.

I. INTRODUCTION

The introduction frames 6G-IoT security as difficult because wireless channels broadcast confidential information, while resource-constrained devices cannot support highly complex transmitter and receiver designs. It presents RIS-aided PLS as a configurable approach and outlines the paper’s design, simulation, research-issue, and recent-advance coverage.

I. INTRODUCTION

  • 6G-IoT faces security risks from broadcast transmission of sensitive information and increasing attack vectors, while PLS secures legitimate links by leveraging wireless-channel dynamics.Source: §I · p. 1
  • Resource-constrained IoT and industrial devices may not support complex transmitter and receiver designs, and PLS can be ineffective under unfavorable propagation conditions.Source: §I · p. 2
  • RIS dynamically controls channels to enhance PLS, with design choices balancing security performance and implementation complexity across scenarios and communication objectives.Source: §I · p. 2
  • The article develops RIS-aided PLS designs against eavesdropping and jamming through resource allocation, beamforming, artificial noise, cooperative communication, simulations, research issues, and recent advances.Source: §I · p. 2

II. OVERVIEW: RIS AND PLS

The overview introduces RIS and PLS, then explains how RIS hardware reconfigures wireless channels through controllable reflection. Its layered structure and controller support programmable scattering and reflection.

II. OVERVIEW: RIS AND PLS

  • The overview covers RIS principles and hardware alongside PLS background concepts, security sources, requirements, and methods.Source: §II · p. 3

A. RIS: Fundamental Principle and Hardware Architecture

B. PLS: Security Concepts and Methods

The section contrasts cryptography’s operational demands with PLS methods that use signal design and processing. It presents PLS as a lightweight option whose suitability depends on the application and security requirements.

1) Background Concepts:

  • Cryptography and encryption require complicated Tx/Rx resources, and are described as unsuitable for hierarchical decentralized, delay-sensitive, power-limited, large-scale, and highly mobile settings.Source: 1) Background Concepts · p. 3
  • PLS can complement or replace cryptography through signal design and processing without key management, exchange, or maintenance, and some solutions may require no end-user computation.Source: 1) Background Concepts · p. 3
  • PLS is presented as simple, flexible, scalable, and lightweight, with solution choice depending on security requirements, attack type, and communication system; combined approaches can provide an advanced security level.Source: 1) Background Concepts · pp. 3–4

2) Leveraging Sources for Security:

The section distinguishes channel-based PLS from RF- and environment-based approaches, then outlines security requirements, attacks, and countermeasures. It frames RIS-aided PLS as a focus for 6G-IoT security analysis.

2) Leveraging Sources for Security:

  • Channel-based PLS uses wireless-channel propagation characteristics, with RIS, and is presented as more appropriate for relatively stationary and indoor settings than RF-based PLS.The article focuses on channel-based solutions for 6G-IoT; RF-based PLS is described as more stable in rapidly changing environments.Source: 2) Leveraging Sources for Security · p. 4

3) Security Requirements and Malicious Attacks:

4) Security Methods:

  • Countermeasures include anti-jamming techniques and anti-eavesdropping methods, but resource demands and unfavorable propagation can limit their feasibility or effectiveness.Frequency hopping and spread spectrum may require extra spectrum; beamforming, artificial noise, and cooperation can involve hardware, computation, deployment, or power costs.Source: 4) Security Methods · p. 4

III. RIS-AIDED PHYSICAL LAYER SECURITY

  • The RIS-aided PLS section covers theoretical background, literature, attack-focused designs, and simulation results.Source: §III · p. 4

A. Background and Research Review

The review describes RIS as a programmable way to control radio propagation and surveys RIS-aided PLS research and design approaches. Its design discussion covers resource adaptation and joint transmitter–RIS beamforming, while noting open challenges in 6G-IoT settings.

A. Background and Research Review

  • RIS can dynamically control the radio environment, and its placement and reflection coefficients can strengthen the legitimate link or degrade an eavesdropper’s channel and a jammer’s effect.The review also identifies full-space implementations such as STAR-RIS and IOS.Source: A. Background and Research Review · pp. 4–5
  • Prior work reports improved PLS across varied scenarios and optimization problems, but 6G-IoT evaluation remains early and needs further study across network topologies and applications.Table II surveys recent RIS-aided PLS efforts.Source: A. Background and Research Review · p. 5, Table II · p. 6

B. RIS-Aided PLS: Design Solutions

  • RIS-aided designs are presented as useful in challenging conditions where conventional MIMO or combined beamforming and artificial-noise methods may not provide sufficient secrecy.Correlated channels can also mean independent observations cannot be assumed, motivating combinations of RIS-aided solutions.Source: B. RIS-Aided PLS: Design Solutions · p. 5

1) RIS-Based Secure Resource Management:

  • RIS-based link adaptation can adjust transmission and reflection parameters to steer signals toward the receiver while canceling jammer interference or degrading eavesdropper SNR.Adjustable parameters include transmit power, modulation, coding, subcarriers, and RIS element count and reflection coefficients.Source: 1) RIS-Based Secure Resource Management · p. 5

2) RIS-Based Secure Beamforming:

  • Joint transmitter beamforming and RIS phase shifts can degrade the eavesdropping channel, strengthen the decoding signal at the receiver, and counter jamming.The appropriate technique depends on transmission rank and security requirements; RIS elements can provide secrecy gain with fewer transmitter antennas.Source: 2) RIS-Based Secure Beamforming · pp. 5–6

3) RIS-Based Secure AN Injection:

The section describes RIS-assisted artificial-noise injection and cooperative relaying or jamming as approaches to PLS. It also highlights implementation challenges and trade-offs that shape solution choice.

3) RIS-Based Secure AN Injection:

  • RIS can reflect artificial noise to interfere with eavesdroppers, including when the eavesdropper’s CSI is unknown, but sufficiently capable attackers may still intercept the signal.The design can use real-time CSI to select transmitter or receiver beams and RIS phase shifts; artificial noise also competes for transmit power and can impair legitimate decoding.Source: 3) RIS-Based Secure AN Injection · pp. 6–7

4) RIS-Based Secure Cooperative Communications:

  • Cooperative relaying adds distributed diversity, while cooperative jamming uses coordinated relay signals to disrupt eavesdropping; RIS control can further enhance cooperative diversity.Open issues include relay selection, reliability, power control, placement, hardware imperfections, computational complexity, incentives, imperfect CSI, and multiple eavesdroppers.Source: 4) RIS-Based Secure Cooperative… · pp. 7–8
  • High-dimensional channels, precise element configuration, strategic placement, parameter coupling, and hardware constraints complicate RIS-aided secrecy gains.Solution selection must balance security performance against hardware complexity, latency, and overhead.Source: 4) RIS-Based Secure Cooperative… · p. 8

C. Simulation Results

The simulations compare RIS-aided and direct-link secrecy, then examine how RIS element count, phase quantization, and placement affect secrecy rate and outage. Results favor RIS assistance and show that placement and propagation conditions shape performance.

C. Simulation Results

  • The simulations use path loss and Rayleigh fading, with 1-, 2-, and 3-bit phase quantization and assumed Tx access to RIS-link channel coefficients.The baseline settings include 20 dBm transmit power, −100 dBm noise variance, 50 RIS elements, and 50 dB direct-link blocking loss.Source: C. Simulation Results · p. 8
  • RIS plus direct links achieves a much higher secrecy rate than direct links alone across the simulated RIS locations.The comparison uses unquantized RIS phases; the setup includes single-antenna transmitter, receiver, and eavesdropper.Source: C. Simulation Results · p. 8, Figure 5 · p. 9
  • Increasing the number of RIS elements raises secrecy rate and lowers secrecy outage probability; 3-bit quantization suffices for optimal performance in this scenario.The outage threshold is 2.5 bps/Hz in the element-count comparison.Source: C. Simulation Results · pp. 8–9, Figure 5 · p. 9
  • Under the particular setup α = 2.5 and dte = 30 m, θ = 25° yields the highest secrecy rate, while θ = 30° yields the lowest secrecy outage probability.Source: C. Simulation Results · p. 9

IV. RESEARCH ISSUES AND POSSIBLE SOLUTIONS

The section identifies gaps in RIS hardware modeling and discusses model-based design options. It highlights unresolved complexities in representing different implementations and their electromagnetic interactions.

1) Research Issues:

  • Unified, physics-compliant, ubiquitous hardware models remain an active research area because RIS implementations and electromagnetic interactions are complex.Limited work incorporates element coupling, impedance matching, hardware impairments, and element scattering properties.Source: 1) Research Issues · p. 10

2) Possible Solutions:

  • Phase-shift, load-impedance, and generalized sheet-transition models can characterize RIS reconfigurability, while quasi-continuous control trades implementation complexity against performance improvement.Novel hardware and manufacturing solutions are proposed to improve scalability and affordability while retaining tuning and continuous control.Source: 2) Possible Solutions · p. 10

B. Channel Modeling

RIS-aided secure 6G-IoT needs reproducible channel models and practical channel estimation. Proposed directions address scenario-specific propagation, estimation overhead, and imperfect CSI.

1) Research Issues:

  • RIS-assisted PLS lacks numerically reproducible channel models that capture varied propagation settings, including mmWave/sub-THz, massive MIMO, RIS, industrial IoT, UAV, and satellite links.The models should be physically implementable and mathematically traceable across deployment scenarios and system settings.Source: 1) Research Issues · p. 10, 2) Possible Solutions · p. 10

2) Possible Solutions:

  • Path-loss models should incorporate RIS size, placement, element count, and transmission distance; near-field propagation and indoor solutions matter for large-aperture, higher-frequency 6G RISs.The cited possible-solution discussion also calls for a general channel-modeling framework that accommodates application-specific propagation.Source: 2) Possible Solutions · p. 10

1) Research Issues:

  • Perfect CSI for legitimate and malicious users is an upper-bound assumption, while hardware constraints, unknown passive eavesdroppers, estimation errors, and quantization impede its acquisition.Estimation also faces phase noise, spatial correlation, real-time RIS configuration, random large-scale deployments, and mmWave/sub-THz propagation characteristics.Source: 1) Research Issues · p. 10, 2) Possible Solutions · pp. 10–11

2) Possible Solutions:

  • Low-power RIS RF chains can estimate individual channels; alternatively, a configured RIS can support concatenated-channel estimation, trading computational complexity and performance degradation.Passive beamforming can also use BS and user feedback without explicitly estimating individual channels; sparse mmWave/sub-THz channels can reduce training burden and error.Source: 2) Possible Solutions · p. 10
  • Jointly designing training signals and estimators is challenging, and imperfect CSI degrades security performance while uncertainty constraints complicate robust optimization.Optimizing a training signal for a particular estimation algorithm may be efficient.Source: 2) Possible Solutions · pp. 10–11

D. Optimization Approaches

RIS-aided PLS optimization is difficult because design variables and constraints are coupled and often non-convex. The section surveys approximation, relaxation, and iterative methods for addressing these problems.

1) Research Issues:

  • RIS-aided PLS optimization couples resource allocation, RIS configuration, beamforming, artificial-noise power, and relay choices with non-convex objectives and constraints.Large channel spaces, fading functions, complex topologies, and nonlinear 6G components further complicate global optimization.Source: 1) Research Issues · p. 11

2) Possible Solutions:

  • Approximation, relaxation, iterative search, and heuristic methods can address high-dimensional or multiobjective problems, including through successive convex approximation and semidefinite relaxation.The paper lists integer, quadratic, mixed-integer, difference-of-convex, fractional, and semidefinite programming approaches.Source: 2) Possible Solutions · p. 11

E. ML-Based approaches

The article presents ML as a way to address complex RIS-aided PLS design challenges, while identifying training burdens and real-world mismatch. It also discusses STAR-RIS capabilities and the modeling, implementation, and optimization work they require.

1) Research Issues:

  • Analytical solutions rely on approximate models and limited operating conditions, while complex RIS configurations and dynamic channels motivate ML-based design and optimization.The identified ML applications include receiver-side optimization, passive beamforming, channel modeling and estimation, and defense against unknown jammers and eavesdroppers.Source: 1) Research Issues · p. 11

2) Possible Solutions:

  • DL supports channel estimation, tracking, and prediction, while DRL handles high-dimensional objectives; training overhead, power use, and real-world mismatch remain concerns.The authors call for lower-complexity learning algorithms that retain accuracy and stability.Source: 2) Possible Solutions · p. 11

V. RECENT ADVANCES

  • The recent-advances section covers STAR-RIS and malicious RIS in relation to eavesdropping and jamming attacks.Source: §V · p. 11

A. STAR-RIS

  • STAR-RIS serves users on both sides by providing transmission and reflection signals, using energy-splitting, mode-selection, or time-splitting protocols.Its adjustable transmission and reflection links mean conventional RIS-aided PLS designs cannot be applied directly.Source: A. STAR-RIS · pp. 11–12
  • STAR-RIS security analysis still needs suitable hardware, channel models and estimation, and performance evaluation across users, elements, and operating protocols.The evaluation scope includes pilot overhead, accuracy, and complexity.Source: A. STAR-RIS · p. 12
  • Full-space multiuser beamforming and protocol selection complicate STAR-RIS implementation, while coupled or unit-modulus constraints and location uncertainty challenge optimization.The authors identify a need for novel convex and ML-based algorithms for hybrid continuous and discrete control.Source: A. STAR-RIS · p. 12

B. Malicious RIS

Malicious RIS can manipulate interference or eavesdropper channels, and covert deployment can undermine legitimate RIS design and channel estimation. The article calls for countermeasures and future validation in practical settings.

B. Malicious RIS

  • Malicious RIS can strengthen jamming interference or increase eavesdropper SNR, exploiting low-cost hardware, imperfect element reconfiguration, or illegal deployment.Jamming can reduce receiver SINR through altered phase shifts; eavesdropping can use eavesdropper CSI to maximize its SNR.Source: B. Malicious RIS · p. 12
  • Covert malicious RIS links can prevent legitimate RIS cancellation and degrade pilot-based CSI estimation, making instantaneous-CSI optimization insufficient or outdated.The authors call for imperfect-CSI-based attack optimization and evaluations of countermeasures.Source: B. Malicious RIS · p. 13

VI. CONCLUSION AND FUTURE WORK

  • Future work includes hardware-platform experiments and practical settings such as mmWave/THz propagation, RIS hardware limits, learning attackers, malicious RIS, and large-scale networks.Source: §VI · p. 13
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