联合优化移动边缘电子计算-可加入的无细胞无大规模MIMIMO的上端连通电力和计算资源 (Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO)

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The coupling of cell-free massive MIMO (CF-mMIMO) with Mobile Edge Computing (MEC) is investigated in this paper. A MEC-enabled CF-mMIMO architecture implementing a distributed user-centric approach both from the radio and the computational resource allocation perspective is proposed. An optimization problem for the joint allocation of uplink powers and remote computational resources is formulated, aimed at minimizing the total uplink power consumption under power budget and latency constraints, while simultaneously maximizing the minimum SE throughout the network. In order to efficiently solve such a challenging non-convex problem, an iterative algorithm based on sequential convex programming is proposed, along with two approaches to priory assess the problem feasibility. Finally, a detailed performance comparison between the proposed MEC-enabled CF-mMIMO architecture and its cellular counterpart is provided. Numerical results reveal the effectiveness of the proposed joint optimization problem, and the natural suitability of CF-mMIMO in supporting computation-offloading applications with benefits over users' transmit power and energy consumption, the offloading latency experienced, and the total amount of allocated remote computational resources.

翻译：本文调查了无细胞大型MIMO(CF-MMIMO)与移动边缘计算(MEC)的连接问题,提出了从无线电和计算资源分配角度采用分散用户中心办法的MEC辅助CF-MIMIMO结构,提出了联合分配上行连线权力和远程计算资源的最佳问题,目的是在电力预算和延缓限制下最大限度地减少总连通电力消耗,同时在整个网络中尽量扩大最低SE值。为了有效解决这种具有挑战性的非电离子问题,建议了基于连续的convex编程的迭代算法,同时提出了两种预先评估问题可行性的办法。最后,提供了拟议的MEC的CFC-MIMIMO结构与其移动电话对应方之间详细的业绩比较。数字结果揭示了拟议的联合优化问题的有效性,以及CFMMIMO在支持计算-卸载应用用户输电和能源消耗的惠益、负载拖拉和分配的远程计算资源总量方面自然的适宜性。