Mobile Edge Computing Aided Cell-Free Massive MIMO Networks

• Published in: IEEE transactions on mobile computing Vol. 23; no. 2; pp. 1 - 16
• Main Authors: Femenias, Guillem, Riera-Palou, Felip
• Format: Magazine Article
• Language: English
• Published: Los Alamitos IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cell-free massive MIMO; Computation offloading; Edge computing; Energy consumption; Iterative algorithms; Iterative methods; Massive MIMO; MIMO communication; Mobile computing; mobile edge computing; Network latency; Optimization; Resource management; Servers; Task analysis; task offloading; Wireless communication
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2022.3232510

A mobile edge computing (MEC) aided cell-free massive MIMO network is investigated in this paper that aims at optimizing the task offloading process from the wireless devices to the MEC server. Partial computation offloading strategies are designed that aim at optimizing either the aggregated latency or the energy consumption by considering the finite computational capability of the MEC server and assuming that the tasks are subject to a maximum latency constraint. The proposed optimization approach considers the joint optimization of both the per-task offloading ratio and the corresponding computational resources allocated to the tasks at the MEC server. This optimization is performed by taking into account the computing capabilities of both the wireless devices and the MEC server and both the UL and DL spectral efficiencies provided by the cell-free massive MIMO network. The proposed optimization problems are shown to be convex and, inspired by the well-known waterfilling algorithm, optimal solutions based on low-complexity iterative algorithms are devised. Extensive numerical results reveal the potential of the proposed MEC-enabled cell-free massive MIMO network.