Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. The performance limits of a system are crucial for guiding its design; however, the performance limits of integrated sensing and communication (ISAC) systems remain an open question. This paper focuses on the bistatic ISAC systems with dispersed multi-receivers and one sensor. Compared to the monostatic ISAC systems, the main challenge is that that the communication messages are unknown to the sensor and thus become its interference, while the channel information between the transmitters and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce the blind interference alignment strategy for various bistatic ISAC settings, including interference channels, MU-MISO channels, and MU-MIMO channels. Under each of such system, the achieved ISAC tradeoff points by the proposed schemes in terms of communication and sensing degrees of freedom are characterized, which outperforms the time-sharing between the two extreme sensing-optimal and communication-optimal points.Simulation results also demonstrate that the proposed schemes significantly improve on the ISAC performance compared to treating interference as noise at the sensor.

翻译：相较于独立的感知与通信系统，集成感知与通信（ISAC）系统在性能和资源效率方面均有显著提升，这主要归功于无线资源、无线电波形和硬件平台的协同使用。系统的性能极限对于指导其设计至关重要；然而，集成感知与通信（ISAC）系统的性能极限仍是一个开放性问题。本文聚焦于具有分散多接收器和一个传感器的双基地ISAC系统。与单基地ISAC系统相比，主要挑战在于通信消息对传感器而言是未知的，因此成为其干扰，同时发射机与传感器之间的信道信息对发射机也是未知的。为了在最大化通信自由度的同时减轻传感器处的干扰，我们针对多种双基地ISAC场景（包括干扰信道、MU-MISO信道和MU-MIMO信道）引入了盲干扰对齐策略。在每种系统设置下，本文所提方案在通信与感知自由度方面所实现的ISAC权衡点均得到了刻画，其性能优于在感知最优与通信最优两个极端点之间进行时间共享的方案。仿真结果也表明，与在传感器处将干扰视为噪声的处理方式相比，所提方案显著提升了ISAC性能。