Multi-IRS Aided Mobile Edge Computing for High Reliability and Low Latency Services
IEEE Transactions on Network and Service Management(2023)
摘要
Although multi-access edge computing (MEC) has allowed for computation
offloading at the network edge, weak wireless signals in the radio access
network caused by obstacles and high network load are still preventing
efficient edge computation offloading, especially for user requests with
stringent latency and reliability requirements. Intelligent reflective surfaces
(IRS) have recently emerged as a technology capable of enhancing the quality of
the signals in the radio access network, where passive reflecting elements can
be tuned to improve the uplink or downlink signals. Harnessing the IRS's
potential in enhancing the performance of edge computation offloading, in this
paper, we study the optimized use of a system of multi-IRS along with the
design of the offloading (to an edge with multi MECs) and resource allocation
parameters for the purpose of minimizing the devices' energy consumption
considering 5G services with stringent latency and reliability requirements.
After presenting our non-convex mathematical problem, we propose a suboptimal
solution based on alternating optimization where we divide the problem into
sub-problems which are then solved separately. Specifically, the offloading
decision is solved through a matching game algorithm, and then the IRS phase
shifts and resource allocation optimizations are solved in an alternating
fashion using the Difference of Convex approach. The obtained results
demonstrate the gains both in energy and network resources and highlight the
IRS's influence on the design of the MEC parameters.
更多查看译文
关键词
Computation offloading,intelligent reflecting surface,multi-access edge computing,ultra-reliable low-latency communication
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要