Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network

TL;DR

This paper proposes a joint resource allocation and computation offloading scheme for mmWave train-ground networks in high-speed rail scenarios, significantly reducing latency and improving user service efficiency.

Contribution

It introduces a novel JRACO scheme combining matching game theory and energy constraints to optimize offloading and resource allocation in high-speed railway communication systems.

Findings

JRACO reduces average task latency.

JRACO increases the number of served users.

Simulation results validate effectiveness over baseline schemes.

Abstract

In this paper, we consider an mmWave-based trainground communication system in the high-speed railway (HSR) scenario, where the computation tasks of users can be partially offloaded to the rail-side base station (BS) or the mobile relays (MRs) deployed on the roof of the train. The MRs operate in the full-duplex (FD) mode to achieve high spectrum utilization. We formulate the problem of minimizing the average task execution latency of all users, under local device and MRs energy consumption constraints. We propose a joint resource allocation and computation offloading scheme (JRACO) to solve the problem. It consists of a resource allocation and computation offloading (RACO) algorithm and an MR Energy constraint algorithm. RACO utilizes the matching game theory to iterate between two subproblems, i.e., data segmentation and user association and sub-channel allocation. With the RACO results, the MR energy constraint algorithm ensures that the MR energy consumption constraint is satisfied. Extensive simulations validate that JRACO can effectively reduce the average latency and increase the number of served users compared with three baseline schemes.