Joint Beamforming and Trajectory Optimizations for Statistical Delay and Error-Rate Bounded QoS Over MIMO-UAV/IRS-Based 6G Mobile Edge Computing Networks Using FBC

Abstract

Tremendous research efforts have been made in conceptualizing 6G mobile wireless networks to support unprecedented scenarios with extremely diverse and challenging delay and error-rate bounded quality-of-services (QoS) requirements for ultra-reliable and low latency communications (URLLC), especially for cell-edge users. However, QoS performance is greatly limited by the computation capacity and finite battery capacity. To address this issue, mobile edge computing (MEC) has been developed by enabling mobile users to offload partial or complete computation-intensive tasks to MEC servers for computing. In addition, leveraging the significant improvements in coverage rate and spectral efficiency, intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) integrated MEC systems, which smartly reconfigure and design wireless propagation environments by bypassing blockage of line-of-sight (LOS) communications, can avoid service starvation of cell-edge users while supporting QoS for URLLC. However, how to statistically upper-bound both delay and error rate for URLLC in multiple-input multiple-output (MIMO)-UAV/IRS-based MEC systems still remains a challenging problem, especially when considering short-packet communications, such as finite blocklength coding (FBC). To overcome these difficulties, in this paper we propose FBC-based joint beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS for URLLC with MEC. First, we develop MIMO-UAV/IRS-based 3D wireless channel models using FBC. Second, we formulate and solve the ϵ-effective energy-efficiency maximization problems by converting non-convex problems into convex problems in both single-user and multiple-user scenarios. Finally, the obtained numerical analyses validate and evaluate our developed MIMO-UAV/IRS-based schemes.