StarBOA: Real-Time Mamba State-Space Unrolling for Sparse Radar Micro-Doppler in ISAC Networks

Abstract:In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90\% missing data. An attention-based baseline, limited to a 52~ms buffer, collapses toward maximum uniform entropy ($H=2.584$ bits) as sparsity increases, failing to capture long-range gait-cycle context. We propose StarBOA, which replaces attention with a causal Mamba state-space model that updates incrementally on a per-window basis without re-scanning past reconstructions. By maintaining a persistent state, StarBOA integrates over $100\times$ more temporal history at no additional per-step computational cost. StarBOA outperforms the baseline's published results across all sparsity levels, with SSIM gains increasing from $+0.0379$ at 50\% missing data to $+0.2472$ at 90\%. Each window is processed in 1.53~ms with zero lookahead, demonstrating efficient causal reconstruction under extreme chirp subsampling.

Machine Learning (cs.LG); Information Theory (cs.IT); Networking and Internet Architecture (cs.NI)

arXiv:2609.33408 [cs.LG]

(or arXiv:2609.33408v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.33408 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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