Channel charting (CC) is a self-supervised learning technique which aims to construct a lower-dimensional representation of the channel measurements, while preserving the neighboring relationship of users. In this letter, we propose a machine learning approach for radio-based localization task with the aid of CC, which fully exploits the dissimilarity extracted solely from the channel measurements. To this end, a hybrid model structure inspired by physical principles is employed, which is realized by a computationally efficient two-layer neural network initialized with a channel chart. The training process employs a weakly-supervised approach that combines: 1) a Siamese network architecture preserving relative user neighborhood relationship through channel dissimilarity metrics, and 2) a limited set of anchor points with ground-truth location annotations to establish absolute positional references. The proposed approach is empirically validated on realistic channel data, achieving encouraging localization accuracy compared to benchmark approaches.
Tianxiang Xu, Li You, Jue Wang et al.· IEEE Wireless Communications...· 0 citations
Orthogonal frequency division multiplexing (OFDM) is widely adopted in modern wireless systems for high spectral efficiency, yet its high peak-to-average power ratio (PAPR) limits power amplifier efficiency. Mixed-numerology transmission, which enables flexible subcarrier spacing for diverse services, further aggravates the PAPR problem. Although PAPR-aware precoding has been investigated in single-numerology multi-user multiple-input multiple-output (MU-MIMO) OFDM systems, the PAPR suppression in mixed-numerology MU-MIMO OFDM systems has not been thoroughly addressed. In this letter, we propose a low-complexity precoding method to jointly suppress per-antenna PAPR, inter-numerology interference (INI), and multi-user interference (MUI) in mixed-numerology MU-MIMO OFDM systems. By decomposing the complex precoding design problem into low-dimensional subproblems, we develop an efficient algorithm with closed-form or semi-closed-form updates. Simulation results demonstrate that the proposed algorithm effectively reduces transmit power while satisfying per-antenna PAPR, INI, and MUI constraints, highlighting its application potential for MU-MIMO data-channel transmission with available channel state information (CSI).
Shuai Cui, Ruiding Hou, Jiaheng Wang et al.· IEEE Communications Letters· 0 citations
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