Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks

TL;DR

This paper introduces a generative AI approach using GFlowNets to optimize time-modulated intelligent reflecting surfaces for secure multi-user OFDM communication, significantly improving security and efficiency.

Contribution

It presents a novel GFlowNet-based method for designing TM-IRS parameters that maximizes sum rate and enhances security in multi-user OFDM systems, outperforming traditional rule-based methods.

Findings

GFlowNet efficiently converges with minimal training data.

The method significantly improves security in multi-user scenarios.

It outperforms exhaustive search in configuration space.

Abstract

We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative AI-based approach to design good sets of TM-IRS parameters out of a set of all possible quantized ranges of parameters. The design objective is to maximize the sum rate across the authorized directions. We model the TM-IRS parameter selection as a deterministic Markov decision process (MDP), where each terminal state corresponds to a specific configuration of TM-IRS parameters. GFlowNets are employed to learn a stochastic policy that samples TM-IRS parameter sets with probability proportional to their associated sum rate reward. Experimental results demonstrate that the proposed method effectively enhances the security of the TM-IRS-aided OFDM systems with multi-users. Also, despite the vast size of the TM-IRS configuration space, the GFlowNet is able to converge after training on fewer than 0.000001% of all possible configurations, demonstrating remarkable efficiency compared to exhaustive combinatorial search. Implementation code is available at https://github.com/ZhihaoTao/GFN4TM-RIS to facilitate reproducibility.