论文标题
分散的匪徒，具有认知雷达网络的反馈
Decentralized Bandits with Feedback for Cognitive Radar Networks
论文作者
论文摘要
完全分散的多人匪徒模型以长期收敛时间在认知雷达网络中的成本表现出很高的定位精度。与其将每个雷达节点建模为独立学习者，不如完全无法与网络中的其他节点交换信息，而是在这项工作中构建了一个“中央协调员”，以促进雷达节点之间的信息交换。我们表明，在干扰限制的频谱中，可用频带的信号与干扰噪声（SINR）的比率可能因位置而异，认知雷达网络（CRN）能够使用中央协调器中的信息来减少获得给定定位误差所需的时间步骤的数量。重要的是，每个节点仍然能够单独学习。我们提供了一个网络的描述，该网络在中央协调员和每个认知雷达节点中都具有混合认知，并检查可以在此结构中实现的在线机器学习算法。
Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks. Rather than model each radar node as an independent learner, entirely unable to swap information with other nodes in a network, in this work we construct a "central coordinator" to facilitate the exchange of information between radar nodes. We show that in interference-limited spectrum, where the signal to interference plus noise (SINR) ratio for the available bands may vary by location, a cognitive radar network (CRN) is able to use information from a central coordinator to reduce the number of time steps required to attain a given localization error. Importantly, each node is still able to learn separately. We provide a description of a network which has hybrid cognition in both a central coordinator and in each of the cognitive radar nodes, and examine the online machine learning algorithms which can be implemented in this structure.
