Joint Pilot and Data Transmission Power Control and Computing Resource Allocation Algorithm for Massive MIMO-MEC Networks

Wenjiang Feng,Weiheng Jiang,Jiali Zheng

doi:10.1109/access.2020.2991108

Abstract

This paper designs a single-cell multi-user massive MIMO-MEC network. In order to ensure the fairness of users, a joint pilot transmission, data transmission and resource allocation model during the computation execution process with the goal of minimizing the maximum offload computing delay for all users is constructed. The resulted problem is non-convex and non-linear optimization, thus difficult to be solved optimally. To tackle this challenge, an improved fruit fly optimization algorithm (FOA) based on the external penalty function steepest descent algorithm (IFOA-PFSA) is proposed. The point obtained by the steepest descent algorithm based on the external penalty function has been employed as the initial point of the fruit fly optimization algorithm, which can greatly reduce the population size and the maximum number of iterations in the random search process of the traditional fruit fly optimization algorithm, reducing the algorithm complexity. Simulation results show that the proposed algorithm IFOA-PFSA has a smaller delay than the traditional FOA (TFOA) algorithm. The complexity of the proposed algorithm is also lower than the TFOA algorithm.

Highlights

Massive multiple-input multiple-output and mobile edge computing (MEC) have received widespread attention in recent years, and the combination of massive MIMO and MEC will undoubtedly become one of the hot areas of 5G mobile communications [1]–[3]
PROBLEM FORMULATION The massive MIMO-MEC network optimization model constructed in this paper aims to minimize the maximum computing offload delay for all users
This paper combines mobile edge computing with massive MIMO and designs a single-cell multi-user massive MIMO-MEC system based on the study of joint resource allocation

Summary

Introduction

Massive multiple-input multiple-output (mMIMO) and mobile edge computing (MEC) have received widespread attention in recent years, and the combination of massive MIMO and MEC will undoubtedly become one of the hot areas of 5G mobile communications [1]–[3]. In MEC networks, the employment of massive MIMO technology can improve spectrum efficiency and energy efficiency [4]. Massive MIMO-MEC networks should jointly consider pilot transmission, data transmission, and computing resource allocation. After obtaining channel state information (CSI), add it to the offload computing process to study the joint pilot and data transmission and computing resource allocation in a multi-user massive MIMO-MEC network. The performance obtained by MEC offload computing largely depends on the joint allocation of computing and communication resources, and the communication resources need to consider the pilot and data transmission processes separately.

Objectives

Results

Conclusion