Beam Scheduling for Cross-Layer ISAC: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a deep reinforcement learning-based method for beam scheduling in cross-layer ISAC systems, optimizing resource allocation for low latency and high sensing accuracy in dynamic multi-user environments.

Contribution

It proposes a DRL-assisted beam allocation approach that reduces feedback overhead and effectively balances communication and sensing requirements without relying on explicit channel state information.

Findings

DRL framework adapts to buffer status and wireless environment.

Multi-beam scheme improves throughput with modest delay.

Achieves near-optimal performance compared to genie-aided benchmark.

Abstract

Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering complicated cross-layer data traffic and queue status in dynamic multi-user environments. This paper studies the beam allocation for cross-layer ISAC that achieves low-latency communication and minimizes sensing parameters estimation error. To handle the complex coupling between practical data buffer dynamics and varying wireless channels, we propose a deep reinforcement learning (DRL)-assisted approach. Rather than relying on explicit channel state information, the DRL-assisted beam allocation reduces feedback overhead by leveraging sensing observations. Simulation results verify that the DRL framework effectively takes buffer status into account and adapts to the wireless environment while allocating resources. The proposed multi-beam scheme improves overall throughput with only modest delay increases. Finally, the DRL-assisted beam management achieves both communication and sensing performance close to that of the genie-aided benchmark with perfect angle-of-departure (AoD) knowledge. These contributions advance the state-of-the-art intelligent resource management for ISAC systems.