Multi-source localization (MSL) plays a vital role in cognitive radio (CR) networks and spectrum monitoring by enabling timely and reliable positioning. Existing MSL methods typically follow a decoupled two-stage pipeline: they either regress source coordinates directly after estimating the source count, or first construct a spatial representation and then apply non-differentiable post-processing operations for coordinate estimation. However, such a disjoint design blocks gradient propagation and prevents end-to-end (E2E) optimization, leading to misaligned objectives and error accumulation, which ultimately limits localization performance. To overcome this, we propose a lightweight End-to-End MSL (E2E-MSL) framework that unifies local radio map construction with a novel differentiable localization module (DLM), enabling seamless gradient propagation and E2E optimization. The DLM, through differentiable coordinate selection (DCS) and physics-informed mean-shift (PIMS) clustering, allows the entire framework to be optimized in an E2E manner under a unified localization objective and naturally accommodates a variable number of sources without requiring architectural modifications or retraining. Extensive experiments on the VaryTxLoc dataset demonstrate that E2E-MSL significantly outperforms existing state-of-the-art (SOTA) approaches in accuracy, robustness, and inference efficiency, underscoring the critical advantage of E2E optimization for enhancing MSL performance. Our code is available at https://github.com/QLMSL/E2E-MSL
Qi-Lu Zhang, Hong-Ying Tang, Zi-Yi Song et al.· IEEE Transactions on Cogniti...· 0 citations
In this paper, we propose a robust beamforming algorithm for reconfigurable holographic surface (RHS)-enhanced uplink covert satellite communication systems. Specifically, the jamming signal is utilized to confuse multiple eavesdropping satellites. We derive the minimum detection error probability (DEP) and the optimal noise parameters to maximize the ability of noise to mask covert transmission. Under this paradigm, we formulate a covert sum rate maximization problem by jointly optimizing the digital beamforming and holographic radiation coefficient while satisfying the transmit power budget, covertness constraints, and radiation coefficient constraints. The problem is inherently non-convex and presents challenges due to the imperfect channel state information (CSI) and the high coupling among variables. Hence, we transform the original non-convex problem into a series of convex approximations by utilizing the Lagrangian duality, quadratic transformation, and alternating optimization. We then propose a robust joint covert beamforming algorithm (RJCB) to achieve near-optimal solutions for holographic and digital beamforming vectors. Finally, simulation results demonstrate that the proposed algorithm exhibits superior robustness and significantly enhanced covert communication capacity compared to benchmark schemes.
Ce Guo, Ying Wang, Zhendong Li et al.· IEEE Transactions on Communi...· 0 citations
