Transformer learning-based efficient MIMO detection method

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-02-24 DOI:10.1016/j.phycom.2025.102637

Burera, Saleem Ahmed, Sooyoung Kim

引用次数: 0

Abstract

Signal detection for multiple-input-multiple-output (MIMO) systems is a challenging problem due to its computational complexity. The conventional algorithms used in this problem often are either impractical or suffer from performance limitations. In this paper, we propose a machine learning-based MIMO detection method. The proposed method employs the encoder block of a transformer learning approach that has been tailored for MIMO detection. The input to the network of the proposed method is prepossessed using a simple linear decomposition method. Simulation results show that the proposed method achieves a significant enhancement in bit error rate (BER) performance and ultimately produces performance approaching that of the maximum likelihood (ML) detection method.

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基于变压器学习的高效MIMO检测方法

多输入多输出（MIMO）系统的信号检测由于其计算复杂性而成为一个具有挑战性的问题。在这个问题中使用的传统算法通常要么不切实际，要么受到性能限制。在本文中，我们提出了一种基于机器学习的MIMO检测方法。所提出的方法采用了为MIMO检测量身定制的变压器学习方法的编码器块。采用简单的线性分解方法对所提方法的网络输入进行预占有。仿真结果表明，该方法显著提高了误码率（BER）性能，最终性能接近最大似然（ML）检测方法。

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

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.