Resource Allocation for IRS-Enabled Secure Multiuser Multi-Carrier Downlink URLLC Systems

TL;DR

This paper proposes a joint IRS and secure URLLC network design to minimize BS power while ensuring ultra-reliable, low-latency secure communication, addressing practical impairments and channel challenges.

Contribution

It introduces a novel joint optimization framework for IRS-assisted secure URLLC systems, enhancing power efficiency and robustness under practical channel impairments.

Findings

Significant power savings over baseline schemes

Enhanced robustness to channel impairments

Convergence of the proposed algorithm in few iterations

Abstract

Secure ultra-reliable low-latency communication (URLLC) has been recently investigated with the fundamental limits of finite block length (FBL) regime in mind. Analysis has revealed that when eavesdroppers outnumber BS antennas or enjoy a more favorable channel condition compared to the legitimate users, base station (BS) transmit power should increase exorbitantly to meet quality of service (QoS) constraints. Channel-induced impairments such as shadowing and/or blockage pose a similar challenge. These practical considerations can drastically limit secure URLLC performance in FBL regime. Deployment of an intelligent reflecting surface (IRS) can endow such systems with much-needed resiliency and robustness to satisfy stringent latency, availability, and reliability requirements. We address this problem and propose a joint design of IRS platform and secure URLLC network. We minimize the total BS transmit power by simultaneously designing the beamformers and artificial noise at the BS and phase-shifts at the IRS, while guaranteeing the required number of securely transmitted bits with the desired packet error probability, information leakage, and maximum affordable delay. The proposed optimization problem is non-convex and we apply block coordinate descent and successive convex approximation to iteratively solve a series of convex sub-problems instead. The proposed algorithm converges to a sub-optimal solution in a few iterations and attains substantial power saving and robustness compared to baseline schemes.