The Application of Deep Reinforcement Learning in Integrated Sensing and Communication
Abstract
power consumption, and signal overhead and supports various emerging applications by sharing spectrum, hardware platforms, and joint signal processing frameworks. Traditional ISAC systems have problems such as high complexity, poor performance, and a lack of autonomous
decision-making ability in practical applications. In recent years, deep reinforcement learning (DRL) has provided feasible methods for solving these problems owing to its powerful learning and decision-making abilities. In this article, we introduce the basic principles of ISAC
systems and DRL, review the typical applications of DRL in ISAC system, and discuss the challenges and future research directions of DRL
in ISAC systems.
Keywords
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DOI: http://dx.doi.org/10.70711/frim.v3i1.5889
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