a cognitive frequency allocation strategy for multi-carrier radar against communication interference

TL;DR

This paper proposes a novel deep reinforcement learning-based frequency allocation strategy for multi-carrier radar to effectively avoid communication interference, utilizing a Markov decision process and an iterative selection technique.

Contribution

It introduces a new frequency allocation method based on deep reinforcement learning and a Markov decision process, addressing combinatorial decision challenges in spectrum sharing.

Findings

Outperforms existing frequency allocation methods.

Effectively avoids communication interference in multi-carrier radar.

Demonstrates robustness in dynamic spectrum environments.

Abstract

Modern radars often adopt multi-carrier waveform which has been widely discussed in the literature. However, with the development of civil communication, more and more spectrum resource has been occupied by communication networks. Thus, avoiding the interference from communication users is an important and challenging task for the application of multi-carrier radar. In this paper, a novel frequency allocation strategy based on the historical experiences is proposed, which is formulated as a Markov decision process (MDP). In a decision step, the multi-carrier radar needs to choose more than one frequencies, leading to a combinatorial action space. To address this challenge, we use a novel iteratively selecting technique which breaks a difficult decision task into several easy tasks. Moreover, an efficient deep reinforcement learning algorithm is adopted to handle the complicated spectrum dynamics. Numerical results show that our proposed method outperforms the existing ones.