Latency and Reliability Aware Edge Computation Offloading in IRS-aided Networks

Seeing the poor wireless conditions caused by obstacles and deep fading that often face the access network, intelligent reflective surfaces (IRS) have been recently studied for enhancing the quality of the wireless signals using a set of passive reflecting elements. Due to the channel quality issue severely impacting the performance of edge computation offloading, the IRS technology can be applied to enhance the edge offloading performance, especially for devices with strict requirements. In this paper, we study the optimized use of the IRS along with the design of the offloading and resource allocation parameters for maximizing the UEs' sum of offloaded bits, considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a sub-optimal solution based on the alternating optimization technique. The offloading decision is solved through a customized matching game algorithm, and then the IRS phase shifts and resources allocation are optimized through in alternating fashion using the Difference of Convex approach. Finally, numerical results demonstrate the improvement in the offloading performance provided by the optimized use of the IRSs, and highlights on the IRSs' influence on the design of the MEC parameters.