Multiple access (MA) design is investigated to facilitate pinching-antenna systems (PASS)-based multi-user communications. By exploiting the newly introduced waveguide domain and existing frequency domain, two MA schemes are explored, namely pure waveguide division multiple access (WDMA) and hybrid WDMA. For each MA scheme, the corresponding resource allocation problem is formulated to maximize the rate fairness via the joint optimization of pinching beamforming and power allocation. For both schemes, a majorization-minimization (MM)-based alternating optimization (AO) algorithm is proposed that alternately optimizes pinching beamforming and transmit power. A low-complexity framework is further developed, including a two-stage pinching beamforming design and successive convex approximation (SCA)-based power allocation. Numerical results demonstrate that: 1) PASS significantly improve communication rate performance over conventional antenna systems; 2) The proposed MM-based AO algorithm provides higher performance at the cost of increased complexity, while the low-complexity framework achieves comparable performance with lower computational complexity; and 3) Pure WDMA achieves better performance compared to hybrid WDMA, efficiently supporting multi-user communications enabled by pinching beamforming.
Qiao Ren, Xi-Dong Mu, Siyu Lin et al.· IEEE Transactions on Wireles...· 0 citations
Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.
Jia-Ying Li, Hai-Feng Wen, Chang-Sheng You et al.· 0 citations
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