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

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