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Please use this identifier to cite or link to this item: http://scholars.ntou.edu.tw/handle/123456789/26310
Title: Intelligent Detection with Sparse Computations for Massive MIMO Systems
Authors: Yen, Mao-Hsu 
Lu, Hoang-Yang 
Chen, Hsin 
Li, Chia-Hsun
Keywords: Massive multiple-input multiple-output;Deep learning;Symbol detection;Orthogonal approximate message passing;Very large-scale integration
Issue Date: 2026
Publisher: SPRINGER
Start page/Pages: 17
Source: WIRELESS PERSONAL COMMUNICATIONS
Abstract: 
In this paper, we propose an enhanced orthogonal approximate message passing network (OAMP-Net) algorithm, called intelligent detection with sparse computations (IDSC), to significantly reduce the computational complexity of OAMP-Net for massive multiple-input multiple-output (MIMO) systems. While OAMP-Net delivers excellent detection performance, its practical deployment is limited by the high computational cost arising from frequent matrix inversions. To overcome this challenge, the proposed IDSC algorithm adopts a matrix approximation technique to reduce both the dimension and frequency of matrix inversions, effectively eliminating computationally intensive operations. Simulation results and complexity analysis demonstrate that IDSC achieves detection performance comparable to that of OAMP-Net, while greatly reducing computational overhead. To further validate its practical feasibility, a corresponding VLSI architecture is developed and implemented using the TSMC 90-nm CMOS process.
URI: http://scholars.ntou.edu.tw/handle/123456789/26310
ISSN: 0929-6212
DOI: 10.1007/s11277-025-11873-6
Appears in Collections:輪機工程學系
資訊工程學系
電機工程學系

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