Jiaying Li, Haifeng Wen, Changsheng You, Yuanwei Liu, Hong Xing
原文摘要
Accurate and reliable near-field multi-source localization is essential for location-aware processing in future wireless systems. However, coherent multipath propagation complicates the direct recovery of line-of-sight (LoS) source positions, while existing estimation methods generally lack explicit control of the risk of source underestimation and positioning uncertainty. In this paper, we investigate reliable source enumeration and positioning in near-field coherent multipath environments. Specifically, we develop a conformal risk control (CRC) based source enumeration scheme that calibrates the penalty coefficient of an eigenvalue-based criterion to control the expected miss-detection ratio at a prescribed level. We further propose MUSIC-Net, a model-based deep learning framework informed by two-stage MUltiple SIgnal Classification (MUSIC), which learns the LoS-related subspace and surrogate distances through subspace-distance-based training, thereby enabling direct source positioning without estimating non-line-of-sight (NLoS) path parameters or performing subsequent path association. To quantify the uncertainty of the estimated positions, we develop a split conformal prediction (SCP) based calibration method that constructs angle-range predicted regions with finite-sample simultaneous coverage guarantees. Numerical results show that the proposed framework effectively controls the risk of source underestimation, while providing improved positioning accuracy with tighter predicted regions than benchmarks given the same target coverage levels.
While extremely large-scale antenna arrays (XL-arrays) hold immense promise for next-generation wireless networks, the paradigm shift encompassing new near-field communications renders conventional discrete Fourier transform (DFT)-based Type-I codebooks inefficient. Meanwhile, existing near-field alternatives usually impose prohibitively high storage and/or feedback burdens. To tackle these challenges, we propose in this letter a unified codebook applicable to both far-field and near-field XL-array systems. Our key idea is to express the near-field channel steering vector in an equivalent form, which is a Hadamard product of an angular vector and a curvature-related vector. This new representation allows direct reuse of the legacy Type-I codebook for angle quantization, while only a lightweight auxiliary codebook is newly added for additional curvature quantization. Subsequently, we optimize the auxiliary curvature codebook by maximizing the average beamforming gain given the information of user distribution. By leveraging the local monotonicity of the beamforming gain with respect to curvature mismatch, the optimization problem is further reduced to a quantization-error minimization problem, for which suboptimal curvature codewords are obtained. Numerical results demonstrate that the proposed unified codebook achieves near-optimal beamforming performance that approaches its upper bound based on perfect channel state information (CSI), while significantly reducing the required feedback overhead as compared to various benchmark schemes.