论文标题
多载波雷达反对交流干扰的认知频率分配策略
a cognitive frequency allocation strategy for multi-carrier radar against communication interference
论文作者
论文摘要
现代雷达通常采用多载波波形，在文献中已广泛讨论。但是，随着民间交流的发展，通信网络已经占据了越来越多的频谱资源。因此，避免通信用户的干扰是应用多载波雷达的重要任务。在本文中，提出了一种基于历史经验的新型频率分配策略，该策略被称为马尔可夫决策过程（MDP）。在决策步骤中，多载波雷达需要选择多个频率，从而导致组合动作空间。为了应对这一挑战，我们使用一种新颖的迭代选择技术，将一项艰难的决策任务打破了几个简单的任务。此外，采用了有效的深度强化学习算法来处理复杂的频谱动力学。数值结果表明，我们提出的方法优于现有方法。
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.
