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Non-Uniform Quantization Codebook Based Hybrid Precoding to Reduce Feedback Overhead in Millimeter Wave MIMO Systems
摘要： In this paper, we propose two non-uniform quantization (NUQ) codebook based hybrid precoding schemes for two main hybrid precoding implementations, i.e., the full-connected structure and the sub-connected structure, to reduce the feedback overhead in millimeter wave single user multiple-input multiple-output (SU-MIMO) systems. Specifically, we firstly group the angles of the arrive/departure (AOAs/AODs) of the scattering paths into several spatial lobes by exploiting the sparseness property of the millimeter wave in the angular domain, which divides the total angular domain into effective spatial lobes' coverage angles and ineffective coverage angles. Then, we map the quantization bits non-uniformly to different coverage angles and construct NUQ codebooks, where high numbers of quantization bits are employed for the effective coverage angles to quantize AoAs/AoDs and zero quantization bit is employed for ineffective coverage angles. Finally, two low-complexity hybrid analog/digital precoding schemes are proposed that utilize the NUQ codebooks. Simulation results demonstrate that, the proposed two NUQ codebook based hybrid precoding schemes achieve near-optimal spectral efficiencies and show the superiority in reducing the feedback overhead compared with the uniform quantization codebook based works.
关键词： feedback overhead,non-uniform quantization,spatial lobe,Millimeter wave communication,hybrid precoding
更新于2025-09-23 15:23:52
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Optimization of Nonlinear Optical Properties of Tellurium-Oxide-Coated Silicon Nitride Waveguides
摘要： In low-power wireless neural recording tasks, signals must be compressed before transmission to extend battery life. Recently, compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, a deep learning framework of quantized CS, termed BW-NQ-DNN, is proposed, which consists of a binary measurement matrix, a non-uniform quantizer, and a non-iterative recovery solver. By training the BW-NQ-DNN, the three parts are jointly optimized. Experimental results on synthetic and real datasets reveal that BW-NQ-DNN not only drastically reduce the transmission bits but also outperforms the state-of-the-art CS-based methods. On the challenging high compression ratio task, the proposed approach still achieves high recovery performance and spike classification accuracy. This framework is of great values to wireless neural recoding devices, and many variants can be straightforwardly derived for low-power wireless telemonitoring applications.
关键词： deep learning,quantized compressive sensing,non-uniform quantization,Wireless neural recording
更新于2025-09-19 17:13:59