Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

TL;DR

This paper addresses joint optimization of radio and computational resources in multicell mobile-edge computing systems to minimize energy consumption while satisfying latency constraints, proposing algorithms for both single-user and multiuser scenarios.

Contribution

It introduces a novel joint optimization framework for radio and computational resources in multicell MEC, including a closed-form solution for single-user and an iterative algorithm for multiuser cases.

Findings

Proposed algorithms outperform disjoint optimization methods.

Closed-form solution derived for single-user case.

Distributed implementation reduces coordination overhead.

Abstract

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to express the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. Then, we reformulate the algorithm in a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.