Distributed Massive MIMO-Aided Task Offloading in Satellite-Terrestrial Integrated Multi-Tier VEC Networks

TL;DR

This paper introduces a distributed massive MIMO-enabled multi-tier vehicular edge computing system that optimizes task offloading, resource allocation, and precoding to reduce delay and energy use in satellite-terrestrial networks.

Contribution

It develops a joint optimization framework for task offloading, subchannel allocation, and precoding in a novel satellite-terrestrial VEC network using an iterative solution approach.

Findings

Proposed algorithm outperforms benchmark methods in simulations.

Joint optimization reduces total delay and energy consumption.

Convex transformation techniques enable efficient solution of complex subproblems.

Abstract

This paper proposes a distributed massive multiple input multiple-output (DM-MIMO) aided multi-tier vehicular edge computing (VEC) system. In particular, each vehicle terminal (VT) offloads its computational task to the roadside unit (RSU) by orthogonal frequency division multiple access (OFDMA), which can be computed locally at the RSU and offloaded to the central processing unit (CPU) via massive satellite access points (SAPs) for remote computation. By considering the partial task offloading model, we consider the joint optimization of the task offloading, subchannel allocation and precoding optimization to minimize the total cost in terms of total delay and energy consumption. To solve this non-convex problem, we transform the original problem into three sub-problems and use the alternate optimization algorithm to solve it. First, we transform the subcarrier allocation problem of discrete variables into convex optimization problem of continuous variables. Then, we use multiple quadratic transformations and Lagrange multiplier method to transform the non-convex subproblem of optimizing precoding vectors into a convex problem, while the task offloading subproblem is a convex problem. Given the subcarrier and the task allocation and precoding result, we finally find the joint optimized results by iterative optimization algorithm. Simulation results show that our proposed algorithm is superior to other benchmarks.