We investigate a joint communication and sensing (JCAS) framework in which a transmitter concurrently transmits information to a receiver and estimates a state of interest based on noisy observations. The state is assumed to evolve according to a known dynamical model. Past state estimates may then be used to inform current state estimates. We show that Bayesian filtering constitutes the optimal sensing strategy. We analyze JCAS performance under an open loop encoding strategy with results presented in terms of the tradeoff between asymptotic communication rate and expected per-block distortion of the state. We illustrate the general result by specializing the analysis to a beam-pointing model with mobile state tracking. Our results shed light on the relative performance of two beam control strategies, beam-switching and multi-beam.

翻译：我们研究一个联合通信与感知框架，在该框架中，发射机同时向接收机传输信息，并基于噪声观测估计感兴趣的状态。假设该状态根据已知的动态模型演化。过去的状态估计可用于辅助当前的状态估计。我们证明了贝叶斯滤波构成了最优的感知策略。我们分析了在开环编码策略下的JCAS性能，结果以渐近通信速率与状态每块期望失真之间的权衡关系呈现。我们通过将分析具体应用于具有移动状态跟踪的波束指向模型来阐释一般性结果。我们的结果揭示了两种波束控制策略——波束切换与多波束——的相对性能。