Learning-Based Resource Management in Integrated Sensing and Communication Systems

TL;DR

This paper presents a novel deep reinforcement learning approach for adaptive resource management in integrated sensing and communication systems, optimizing time allocation for tracking and data transmission to improve performance under constraints.

Contribution

The paper introduces a constrained deep reinforcement learning method specifically designed for resource allocation in dual-function radar-communication systems, addressing a key challenge in dynamic environments.

Findings

The CDRL approach effectively maximizes communication quality.

It adapts well to highly dynamic environments.

It respects time budget constraints during operation.

Abstract

In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.