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Chongqing University of Posts and Telecommunications

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Research library236linked papers
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Selected work

Representative Papers

Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning

Nov 23, 2024arXiv.org

Catastrophic forgetting severely hinders long-term adaptability in continual learning. This paper proposes the first Mamba-based, forgetfulness-free fine-tuning framework for class-incremental continual learning. Our method maps historical task features into a subspace and applies orthogonal parameter updates within its nullspace—thereby preserving output consistency of the State Space Model (SSM) core across tasks. We theoretically derive and enforce consistency constraints on four time-invariant SSM parameters, simplifying both the recurrent structure and discretization procedure. Crucially, this work introduces nullspace projection to the Mamba architecture for the first time, enabling efficient, replay-free, and regularization-free continual learning. Evaluated on four standard class-incremental benchmarks, our approach consistently outperforms state-of-the-art methods. The implementation is publicly available.

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Beamforming Design for Joint Target Sensing and Proactive Eavesdropping

Jul 09, 2024arXiv.org

This work addresses beamforming design for joint target sensing and active physical-layer eavesdropping (JTSAPE) systems, where a shared waveform at the base station simultaneously enables radar-like target parameter estimation, conveys information to the legitimate receiver, and acts as artificial noise to jam the illegitimate receiver—thereby enhancing eavesdropping performance. We propose the first normalized weighted framework jointly optimizing sensing accuracy (by minimizing the Cramér–Rao bound) and eavesdropping efficacy (by maximizing the eavesdropping signal-to-interference-plus-noise ratio). To tackle the resulting non-convex optimization under strong eavesdropper channels, we develop a stepwise iterative algorithm based on sequential rank-one constraint relaxation (SROCR). Simulation results demonstrate that the proposed method significantly improves both SINR and estimation accuracy in multi-target and time-varying channel scenarios, yielding high-quality suboptimal beam covariance solutions with strong robustness and practical applicability.

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