Energy-Efficient Task Processing in Cell-Free Massive MIMO-Aided MEC Networks via Heuristic Algorithm

Energy-Efficient Task Processing in Cell-Free Massive MIMO-Aided MEC Networks via Heuristic Algorithm | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 February 2025 V1 Latest version Share on Energy-Efficient Task Processing in Cell-Free Massive MIMO-Aided MEC Networks via Heuristic Algorithm Authors : Jiahui Mao 0009-0005-6402-2641 , Wensong Liao , Chong Tan , Hong Liu , and Min Zheng [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174038612.22984116/v1 249 views 119 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper investigates a cell-free massive multiple-input multiple-output (MIMO)-aided binary task offloading mobile edge computing (MEC) system. The goal is to optimize computation task processing for user equipment (UE). We formulate an optimization problem for jointly allocating computation and communication resources under a power constraint. The objective is to minimize weighted energy consumption subject to a maximum delay constraint. We simplify the original problem and transform it into an NP-hard problem under fractional non-convex constraints. A heuristic scheme is proposed based on a genetic algorithm (GA) and alternating iterations. Simulation results show that the proposed algorithm significantly reduces energy consumption in task processing. Supplementary Material File (wiley_itl (1).pdf) Download 1.17 MB Information & Authors Information Version history V1 Version 1 24 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cell-free genetic algorithm (ga) mimo mobile edge computing (mec) Authors Affiliations Jiahui Mao 0009-0005-6402-2641 Shanghai Institute of Microsystem and Information Technology View all articles by this author Wensong Liao Shanghai Institute of Microsystem and Information Technology View all articles by this author Chong Tan Shanghai Institute of Microsystem and Information Technology View all articles by this author Hong Liu Shanghai Institute of Microsystem and Information Technology View all articles by this author Min Zheng [email protected] Shanghai Institute of Microsystem and Information Technology View all articles by this author Metrics & Citations Metrics Article Usage 249 views 119 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jiahui Mao, Wensong Liao, Chong Tan, et al. Energy-Efficient Task Processing in Cell-Free Massive MIMO-Aided MEC Networks via Heuristic Algorithm. Authorea . 24 February 2025. 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