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 fraction programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results collaborate 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重大挑战。若缺乏有效协作，频谱共享导致的干扰将削弱两系统的性能。本文针对分布式网络中的通信与感知干扰问题展开研究。与需要通信与感知系统间基于导频的高频交互的传统方案不同，我们引入名为"无线地图"的第三方，提供大尺度信道状态信息。利用大尺度信道状态信息，我们优化通信与感知系统的发射功率，以最大化雷达探测的信干噪比，同时满足受干扰用户的遍历速率要求。针对目标函数和约束条件的非凸性，我们采用基于辅助函数的缩比技术和分式规划进行简化，进而提出迭代算法求解该问题。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。