Blind and Topological Interference Managements for Bistatic Integrated Sensing and Communication

TL;DR

This paper introduces novel interference management strategies for bistatic integrated sensing and communication systems, improving resource efficiency and channel estimation accuracy by mitigating interference without prior message knowledge.

Contribution

It applies blind interference alignment and topological interference management to bistatic ISAC, a novel approach in this context, and characterizes the resulting tradeoffs.

Findings

Achieves better ISAC tradeoff points than time-sharing.

Significantly improves channel estimation error over traditional methods.

Demonstrates effectiveness through simulation results.

Abstract

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. This paper focuses on the bistatic ISAC systems with dispersed multi-receiver and one sensor. Compared to a monostatic ISAC system, the main challenge in the bistatic setting is that the information messages are unknown to the sensor and therefore they are seen as interference, while the channel between the transmitters (TX) 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 two strategies, namely, blind interference alignment and topological interference management. Although well-known in the context of Gaussian interference channels, these strategies are novel in the context of bistatic ISAC. For the bistatic ISAC models with heterogeneous coherence times or with heterogeneous connectivity, the achieved ISAC tradeoff points in terms of communication and sensing degrees of freedom are characterized. In particular, we show that the new tradeoff outperforms the time-sharing between the sensing-only and the communication-only schemes. Simulation results demonstrate that the proposed schemes significantly improve the channel estimation error for the sensing task, compared to treating interference as noise at the sensor and successive interference cancellation.