Deep-Variational-Inference-Learning Detection for Cell-Free Massive MIMO With Quantization Error

IF 7.1 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Vehicular Technology Pub Date : 2025-01-28 DOI:10.1109/TVT.2025.3534820

Feng Li;Dou Zhang;Zikun Yang;Honglin Li

引用次数: 0

Abstract

A deep variational inference learning (DVIL) framework is proposed for data detection for cell-free massive multiple-input multiple-output (MIMO). The unknown model of the superimposed noise of quantization error and the environment noise is extracted based on the mixed Gaussian (MG) model, in order to make the proposed method have greater adaptability over variable scenarios. An iterative solution is obtained using VI. After that, the proposed algorithm is divided into two parts including the VI part and the trainable projected gradient (TPG) part. The TPG part is used to calculate the variable which has the highest complexity induced by matrix inversion. The numerical results show the merits of the proposed algorithm over the traditional algorithms.

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具有量化误差的无单元大规模MIMO深度变分-推理学习检测

提出了一种深度变分推理学习框架，用于无单元的海量多输入多输出（MIMO）数据检测。基于混合高斯（MG）模型提取量化误差与环境噪声叠加的未知模型，使所提方法对可变场景具有更强的适应性。然后，将该算法分为两部分，分别是VI部分和可训练投影梯度（TPG）部分。TPG部分用于计算由矩阵反演引起的复杂度最高的变量。数值结果表明，该算法优于传统算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Vehicular Technology 工程技术-电信学

CiteScore

6.00

自引率

8.80%

发文量

1245

审稿时长

6.3 months

期刊介绍： The scope of the Transactions is threefold (which was approved by the IEEE Periodicals Committee in 1967) and is published on the journal website as follows: Communications: The use of mobile radio on land, sea, and air, including cellular radio, two-way radio, and one-way radio, with applications to dispatch and control vehicles, mobile radiotelephone, radio paging, and status monitoring and reporting. Related areas include spectrum usage, component radio equipment such as cavities and antennas, compute control for radio systems, digital modulation and transmission techniques, mobile radio circuit design, radio propagation for vehicular communications, effects of ignition noise and radio frequency interference, and consideration of the vehicle as part of the radio operating environment. Transportation Systems: The use of electronic technology for the control of ground transportation systems including, but not limited to, traffic aid systems; traffic control systems; automatic vehicle identification, location, and monitoring systems; automated transport systems, with single and multiple vehicle control; and moving walkways or people-movers. Vehicular Electronics: The use of electronic or electrical components and systems for control, propulsion, or auxiliary functions, including but not limited to, electronic controls for engineer, drive train, convenience, safety, and other vehicle systems; sensors, actuators, and microprocessors for onboard use; electronic fuel control systems; vehicle electrical components and systems collision avoidance systems; electromagnetic compatibility in the vehicle environment; and electric vehicles and controls.