具有通信约束的多基地综合感知与通信系统中的数据辅助目标定位
Data-Aided Target Localization in Multistatic ISAC Systems With Communication Constraints
浏览论文内容
中文总结 AI 辅助
研究多基地ISAC系统中利用通信信号进行目标定位的问题，提出含统计数据辅助感知和联合数据辅助感知与解码两种策略的数据辅助框架，推导性能极限，制定联合设计问题并开发算法，仿真验证方案有效性。
中文摘要 AI 辅助
综合感知与通信（ISAC）使未来无线网络能够通过共享波形执行感知和通信（S&C）。然而，在多基地ISAC系统中，感知接收机不知道传输数据符号的实现，这使得利用通信信号进行感知具有挑战性。本文提出了一种用于目标定位的数据辅助框架，具有两种接收机策略，即统计数据辅助感知和联合数据辅助感知与解码，前者将随机未知数据符号边缘化，后者将可靠解码的数据符号作为已知虚拟导频重用。在正交频分复用（OFDM）信号下，我们推导了两种策略中目标定位的性能极限，并采用可实现的遍历数据速率作为通信度量。然后，我们针对通信约束下的目标定位制定了联合时间分配和发射数据协方差设计问题，该问题表征了联合S&C边界并量化了数据符号提供的感知增益。此外，我们开发了两种实现所提出的数据辅助接收机处理的目标定位算法，并将框架扩展到有限字母信号。仿真结果验证了理论分析和所提出的数据辅助方案的有效性。
英文摘要
Integrated sensing and communication (ISAC) enables future wireless networks to perform sensing and communication (S&C) over a shared waveform. In multistatic ISAC systems, however, the sensing receivers do not know the realizations of transmitted data symbols, making it challenging to exploit communication signals for sensing. In this paper, we propose a data-aided framework for target localization with two receiver strategies, namely statistical data-aided sensing and joint data-aided sensing and decoding, where the former marginalizes the random unknown data symbols and the latter reuses the reliably decoded data symbols as known virtual pilots. Under orthogonal frequency division multiplexing (OFDM) signaling, we derive the performance limits for target localization in both strategies and adopt the achievable ergodic data rate as the communication metric. Then, we formulate a joint time-allocation and transmit data-covariance design problem for target localization under communication constraints, which characterizes the joint S&C bound and quantifies the sensing gain provided by data symbols. In addition, we develop two target localization algorithms that implement the proposed data-aided receiver processing, and extend the framework to finite-alphabet signaling. Simulation results validate theoretical analysis and the effectiveness of the proposed data-aided schemes.
