Joint Power Allocation and Task Offloading for Reliability-Aware Services in NOMA-Enabled MEC
Chongwu Dong, Yirui Tian, Zhi Zhou, Wushao Wen, Xu Chen
Abstract
With the proliferation of 5G networks, mobile edge computing (MEC) has emerged as a promising technology to fulfill the stringent requirements for reliability-aware services in the Internet of Things (IoT). However, in such networks, the wireless channel states and the task arrivals are stochastic and hard to predict well. Under this scenario, tasks generated from mobile devices would pile up in the transmission queue and edge computing queue when offloading to the edge cloud via a 5G network, resulting in quality degradation for reliability-aware services. To tackle the above challenges, we introduce non-orthogonal multiple access (NOMA) in MEC to meet the requirements of ultra-reliable and low-latency communications (URLLC), in which task queuing delay violation probability and transmission error probability are both considered. Furthermore, we explore the closed-form expression based on the effective capacity (EC) to derive the performance boundary of service reliability under a general model that multiple data sources are from different IoT devices and tasks are offloaded through two-stage transmission-computing tandem queues. Based on the above mathematical analysis for service reliability, we propose an efficient strategy combining power allocation and task offloading to reduce energy consumption for all devices in NOMA-enabled MEC. Extensive simulation studies are further conducted to validate the advantage of our strategy and show the significant performance gain of nearly up to 20% over other alternatives.