Institution profile

Chongqing University of Posts and Telecommunications

Academic institutionasia · cn
Official website
Research library233linked papers
Opportunities0open roles
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.

1 citationsRead paper

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.

1 citationsRead paper
Recent publications

Latest Papers

QGB-W$k$NN: Quantum Granular-Ball Learning for Robust Classification

Sep 05, 2026

Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited robustness in noisy environments. To jointly address these challenges, this paper proposes an efficient and reliable weighted $K$-nearest neighbor classification framework based on quantum granular balls, termed QGB-W$k$NN. The proposed framework improves computational efficiency by integrating quantum-enhanced granular-ball representation with hierarchical nearest-neighbor search, while enhancing classification reliability through a purity-aware weighted decision mechanism. Specifically, quantum-kernel granular balls are constructed to reduce retrieval redundancy and strengthen nonlinear feature representation under limited quantum resources. A granular-ball purity-guided HNSW optimization strategy is developed to exploit structural reliability for hierarchical graph construction during neighbor retrieval, alleviating the local optimality issue caused by conventional random layering. Finally, a weighted voting mechanism jointly incorporating granular-ball similarity and purity is introduced to produce more reliable classification decisions in noisy environments. Extensive experiments on benchmark datasets demonstrate that QGB-W$k$NN achieves competitive classification accuracy while exhibiting favorable Pareto trade-offs between classification performance and computational cost. Moreover, the proposed framework consistently improves robustness under various noisy conditions, suggesting that reliability-aware quantum granular-ball learning provides a promising paradigm for efficient and robust nearest-neighbor classification.

0 citationsRead paper