Blockage-Resilient Integrated Sensing and Communication in mmWave Networks: Multi-View Collaboration and Efficient Task Allocation

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets’ radar cross section (RCS), the blockage-resilient multi-BS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages.