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Eavesdropping with Intelligent Reflective Surfaces: Near-Optimal Configuration Cycling

Francesco Malandrino, Alessandro Nordio, Carla Fabiana Chiasserini

TL;DR

The paper tackles passive eavesdropping in IRS-aided THz networks by proposing a strategy that cycles among a small set of IRS-to-UE configurations rather than optimizing IRS phase shifts for a single setup. It proves NP-hardness of the underlying selection problem and introduces ParallelSlide, a polynomial-time heuristic with a constant competitive ratio that yields near-optimal secrecy-rate versus data-rate trade-offs. The method secures configuration sequencing with hash-chain based synchronization while modeling a worst-case eavesdropper that adapts to observed patterns. Numerical experiments demonstrate that ParallelSlide outperforms simple baselines and closely matches the optimum across various scenario sizes, supporting its practicality for real-time network management in 6G-era systems.

Abstract

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users that are facilitated by the presence of IRSs. Specifically, we envision a transmission scheme that cycles across a number of IRS-to-user assignments, and we select them in a near-optimal fashion, thus guaranteeing both a high data rate and a good secrecy rate. Unlike most of the existing works addressing passive eavesdropping, the strategy we envision has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users' data rate and secrecy rate, and how the system parameters affect such a trade-off.

Eavesdropping with Intelligent Reflective Surfaces: Near-Optimal Configuration Cycling

TL;DR

The paper tackles passive eavesdropping in IRS-aided THz networks by proposing a strategy that cycles among a small set of IRS-to-UE configurations rather than optimizing IRS phase shifts for a single setup. It proves NP-hardness of the underlying selection problem and introduces ParallelSlide, a polynomial-time heuristic with a constant competitive ratio that yields near-optimal secrecy-rate versus data-rate trade-offs. The method secures configuration sequencing with hash-chain based synchronization while modeling a worst-case eavesdropper that adapts to observed patterns. Numerical experiments demonstrate that ParallelSlide outperforms simple baselines and closely matches the optimum across various scenario sizes, supporting its practicality for real-time network management in 6G-era systems.

Abstract

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users that are facilitated by the presence of IRSs. Specifically, we envision a transmission scheme that cycles across a number of IRS-to-user assignments, and we select them in a near-optimal fashion, thus guaranteeing both a high data rate and a good secrecy rate. Unlike most of the existing works addressing passive eavesdropping, the strategy we envision has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users' data rate and secrecy rate, and how the system parameters affect such a trade-off.
Paper Structure (14 sections, 28 equations, 5 figures, 1 algorithm)

This paper contains 14 sections, 28 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Communication Model
  4. Channel model and IRS characterization
  5. Behavior of BS, UEs, and MN
  6. Network Management Mechanism
  7. Performance metrics
  8. Communication scheme
  9. Problem Formulation and Solution Strategy
  10. Problem formulation
  11. Solution strategy: The ParallelSlide algorithm
  12. Algorithm analysis
  13. Performance Evaluation
  14. Conclusions

Figures (5)

  • Figure 1: Communication model: a base station (BS) is transmitting toward the $k$-th UE, thanks to the help of the $n$-th IRS. The LoS link between BS and UE is blocked by an obstacle. The $k$-th UE is the victim of the malicious node (MN), which intercepts the signals reflected by the IRSs.
  • Figure 2: Two possible configurations for a network with $N=K=3$. The top configuration corresponds to the map $\nu_a(1)=1$, $\nu_a(2)=3$, and $\nu_a(3)=2$, while the bottom configuration corresponds to the map $\nu_b(1)=3$, $\nu_b(2)=1$, and $\nu_b(3)=2$.
  • Figure 3: Base scenario.
  • Figure 4: Data rate and secrecy rate achieved as the number $C^\text{T}$ of configurations to choose changes, under the base (left) and extended (right) scenarios.
  • Figure 5: Trade-offs between data rate and secrecy rate attained by different strategies, under the base (left) and extended (right) scenarios.

Theorems & Definitions (5)

  • proof
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