Sensing capabilities as an integral part of the network have been identified as a novel feature of sixth-generation (6G) wireless networks. As a key driver, millimeterwave (mmWave) communication largely boosts speed, capacities, and connectivity. In order to maximize the potential of mmWave communication, precise and fast beam acquisition (BA) is crucial, since it compensates for a high pathloss and provides a large beamforming gain. Practically, the angle-of-departure (AoD) remains almost constant over numerous consecutive time slots, the backscatter signal experiences some delay, and the hardware is restricted under the peak power constraint. This work captures these main features by a simple binary beam-pointing (BBP) channel model with in-block memory (iBM) [1], peak cost constraint, and one unit-delayed feedback. In particular, we focus on the sensing capabilities of such a model and characterize the performance of the BA process in terms of the Hamming distortion of the estimated channel state. We encode the position of the AoD and derive the minimum distortion of the BBP channel under the peak cost constraint with no communication constraint. Our previous work [2] proposed a joint communication and sensing (JCAS) algorithm, which achieves the capacity of the same channel model. Herein, we show that by employing this JCAS transmission strategy, optimal data communication and channel estimation can be accomplished simultaneously. This yields the complete characterization of the capacity-distortion tradeoff for this model.

翻译：感知能力作为网络不可或缺的一部分已被视为第六代（6G）无线网络的新特性。作为关键驱动力，毫米波通信大幅提升了速度、容量和连接性。为充分发挥毫米波通信潜力，精确且快速的波束获取至关重要，因为它能补偿高路径损耗并提供巨大的波束赋形增益。实际中，离开角（AoD）在多个连续时隙内几乎保持恒定，反向散射信号经历一定延迟，且硬件受到峰值功率约束的限制。本文通过一个具有块内记忆（iBM）[1]、峰值成本约束和单位延迟反馈的简单二元波束指向（BBP）信道模型捕捉了这些主要特征。具体而言，我们聚焦于该模型的感知能力，并通过估计信道状态的汉明失真来表征波束获取过程的性能。我们对离开角位置进行编码，并推导出无通信约束下峰值成本约束BBP信道的最小失真。我们之前的工作[2]提出了一种联合通信与感知（JCAS）算法，该算法可实现相同信道模型的容量。在此，我们证明通过采用这种JCAS传输策略，可以同时实现最优数据通信和信道估计。这为该模型的容量-失真权衡给出了完整表征。