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

TL;DR

This work addresses joint task offloading, subcarrier allocation, and precoding in a DM-MIMO-aided, satellite-terrestrial VEC network, aiming to minimize a weighted sum of total delay and energy. It models a two-layer architecture where VTs offload to a Road-Side Unit (RSU) and, if needed, remotely to SAPs connected to a CPU, using OFDMA and DM-MIMO in the SAP cluster; the problem is nonconvex and tackled via alternating optimization. Key contributions include relaxing discrete subcarrier allocation, applying fractional programming and quadratic transforms to convert the RSU-SAP association and precoding subproblems into convex forms, and solving the resulting convex programs with CVX in an iterative framework. Simulation results show rapid convergence and clear performance gains over baseline offloading schemes, demonstrating the practicality of DM-MIMO in enhancing energy-latency efficiency for real-time vehicular computing in integrated satellite-terrestrial networks.

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.

Figures (3)

• Figure 1: Convergence curve.
• Figure 2: Performance comparison under different task data size.
• Figure 3: Performance comparison under different VT numbers.