Multiagent Deep Deterministic Policy Gradient-Based Computation Offloading and Resource Allocation for ISAC-Aided 6G V2X Networks

Bintao Hu, Wenzhang Zhang, Yuan Gao, Jianbo Du, Xiaoli Chu

Abstract: Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

External IDs:dblp:journals/iotj/HuZGDC24