计算机科学
电信线路
最优化问题
延迟(音频)
无线
水准点(测量)
边缘计算
移动边缘计算
无线网络
计算机网络
计算复杂性理论
分布式计算
物联网
算法
服务器
嵌入式系统
电信
大地测量学
地理
作者
Gen Li,Ming Zeng,Deepak Mishra,Hao Li,Zheng Ma,Octavia A. Dobre
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-07-15
卷期号:10 (14): 12156-12168
被引量:4
标识
DOI:10.1109/jiot.2023.3240395
摘要
Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.
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