Integrated Sensing, Communication, and Computing for NR-V2X: A Cross-Layer Resource Allocation Framework Using Multi-Agent Reinforcement Learning
For vehicular networks requiring joint sensing, communication, and computing, this work addresses the lack of cross-layer resource allocation in existing SB-SPS protocols.
The paper proposes MAPPO-SPS, a multi-agent reinforcement learning framework for integrated sensing, communication, and computing in NR-V2X networks, achieving balanced tradeoffs among sensing accuracy, packet reception ratio, throughput, energy, and delay.
Integrated sensing, communication, and computation (ISCC) is emerging as a unified design paradigm for future vehicular networks that require joint environment perception, safety-critical information exchange, and latency-sensitive task processing. In New Radio Vehicle-to-Everything (NR-V2X) Mode 2, autonomous resource selection is performed through sensing-based semi-persistent scheduling (SB-SPS), which is effective for distributed communication resource reservation but does not explicitly consider sensing-resource demand, task-induced computation workload, and the additional latency introduced by mobile edge computing (MEC) offloading. This paper develops multi-agent proximal policy optimization-based SB-SPS (MAPPO-SPS), an ISCC-aware cross-layer scheduler that jointly adapts SB-SPS reservation, radio-resource partitioning, and overflow-driven computation-offloading decisions at control epochs. The scheduling problem is formulated as a cooperative partially observable Markov game and solved using MAPPO with centralized training and decentralized execution (CTDE). Simulation results show that MAPPO-SPS achieves a balanced tradeoff among CRLB-based sensing accuracy, packet reception ratio (PRR), effective throughput, energy consumption, and end-to-end delay.