Optimal Coordinated Transmit Beamforming for Networked Integrated Sensing and Communications

TL;DR

This paper develops optimal coordinated transmit beamforming strategies for multi-antenna networked ISAC systems, enhancing joint sensing and communication performance under various synchronization and receiver capabilities.

Contribution

It introduces a comprehensive optimization framework for beamforming in networked ISAC, considering synchronization, receiver types, and joint detection, solved via SDR techniques.

Findings

Optimized beamforming improves detection probability in ISAC systems.

Synchronization among BSs enhances target detection capabilities.

The proposed solutions effectively balance sensing and communication requirements.

Abstract

This paper studies a multi-antenna networked integrated sensing and communications (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to serve multiple single-antenna communication users (CUs) and perform joint target detection by exploiting the reflected signals simultaneously. To facilitate target sensing, the BSs transmit dedicated sensing signals combined with their information signals. Accordingly, we consider two types of CU receivers with and without the capability of canceling the interference from the dedicated sensing signals, respectively. In addition, we investigate two scenarios with and without time synchronization among the BSs. For the scenario with synchronization, the BSs can exploit the target-reflected signals over both the direct links (BS-to-target-to-originated BS links) and the cross-links (BS-to-target-to-other BSs links) for joint detection, while in the unsynchronized scenario, the BSs can only utilize the target-reflected signals over the direct links. For each scenario under different types of CU receivers, we optimize the coordinated transmit beamforming at the BSs to maximize the minimum detection probability over a particular targeted area, while guaranteeing the required minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs. These SINR-constrained detection probability maximization problems are recast as non-convex quadratically constrained quadratic programs (QCQPs), which are then optimally solved via the semi-definite relaxation (SDR) technique.