The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

翻译：第六代（6G）网络预计将同时提供通信与感知服务。然而，频谱稀缺对通信与感知系统的和谐共存构成了重大挑战。若缺乏有效协作，频谱共享所产生的干扰将损害两个系统的性能。本文针对分布式网络中的通信与感知干扰问题展开研究。不同于传统方案需要通信与感知系统间基于导频的高频交互，我们引入名为无线电地图的第三方来提供大规模信道状态信息。利用大规模信道状态信息，我们在满足受干扰用户遍历速率要求的同时，优化通信与感知系统的发射功率，以最大化雷达检测的信干噪比。鉴于目标函数与约束条件均具有非凸性，我们采用基于辅助函数的尺度变换与分式规划技术进行简化。随后，我们提出一种迭代算法来求解该问题。仿真结果验证了我们的观点：外部信息（即位置与环境信息）能有效解耦通信与感知干扰。