Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems: A Novel Rank Relaxation Method

TL;DR

This paper introduces a novel energy-efficient resource allocation algorithm for IRS-assisted URLLC networks, optimizing beamformers and phase shifts to enhance performance with a new rank relaxation method.

Contribution

It presents a new iterative rank relaxation technique within an AO framework for energy-efficient resource allocation in IRS-assisted URLLC systems.

Findings

The proposed method outperforms existing benchmarks in energy efficiency.

The iterative rank relaxation approach effectively approaches rank-one solutions.

The algorithm demonstrates convergence and improved performance in simulations.

Abstract

This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features a multi-antenna base station (BS) transmitting data traffic to a group of URLLC users with short packet lengths. We maximize the total network's energy efficiency (EE) through the optimization of active beamformers at the BS and passive beamformers (a.k.a. phase shifts) at the IRS. The main non-convex problem is divided into two sub-problems. An alternating optimization (AO) approach is then used to solve the problem. Through the use of the successive convex approximation (SCA) with a novel iterative rank relaxation method, we construct a concave-convex objective function for each sub-problem. The first sub-problem is a fractional program that is solved using the Dinkelbach method and a penalty-based approach. The second sub-problem is then solved based on semi-definite programming (SDP) and the penalty-based approach. The iterative solution gradually approaches the rank-one for both the active beamforming and unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy of the proposed solution compared to existing benchmarks.