{"title":"Convergence-enhanced 1-bit DACs precoding for massive MIMO-OFDM systems via Anderson acceleration","authors":"Guodong Xue, Hui Li, Rui Liang","doi":"10.1016/j.dsp.2025.105545","DOIUrl":null,"url":null,"abstract":"<div><div>In the downlink of massive multiple-input multiple-output (MIMO) systems, high-resolution digital-to-analog converters (DACs) are a major source of power consumption. This paper mainly focuses on the precoding design for the downlink of massive MIMO-orthogonal frequency division multiplexing (OFDM) systems employing 1-bit DACs to reduce power consumption. We propose a Douglas-Rachford splitting (DRS)-based 1-bit precoding algorithm, where operator linearization is adopted during iterations to avoid matrix inversion, thereby reducing computational complexity. To enhance convergence efficiency, we demonstrate that the proposed precoding algorithm is a fixed-point iteration and introduce an Anderson acceleration module, developing an Anderson acceleration linearized DRS (LDRS-AA) precoding algorithm. Notably, we introduce a decision function to improve the stability of classical Anderson acceleration. A detailed analysis of the convergence and computational complexity for the proposed algorithms is also provided. Simulation results show that the proposed Anderson acceleration scheme achieves significant convergence speed improvement without performance loss. In addition, the proposed precoding algorithm achieves superior bit error rate (BER) performance, exhibiting approximately 1.25 dB and 1 dB performance gains over existing advanced algorithms under quadrature phase shift keying (QPSK) modulation and 16-ary quadrature amplitude modulation (QAM), respectively.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105545"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425005676","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In the downlink of massive multiple-input multiple-output (MIMO) systems, high-resolution digital-to-analog converters (DACs) are a major source of power consumption. This paper mainly focuses on the precoding design for the downlink of massive MIMO-orthogonal frequency division multiplexing (OFDM) systems employing 1-bit DACs to reduce power consumption. We propose a Douglas-Rachford splitting (DRS)-based 1-bit precoding algorithm, where operator linearization is adopted during iterations to avoid matrix inversion, thereby reducing computational complexity. To enhance convergence efficiency, we demonstrate that the proposed precoding algorithm is a fixed-point iteration and introduce an Anderson acceleration module, developing an Anderson acceleration linearized DRS (LDRS-AA) precoding algorithm. Notably, we introduce a decision function to improve the stability of classical Anderson acceleration. A detailed analysis of the convergence and computational complexity for the proposed algorithms is also provided. Simulation results show that the proposed Anderson acceleration scheme achieves significant convergence speed improvement without performance loss. In addition, the proposed precoding algorithm achieves superior bit error rate (BER) performance, exhibiting approximately 1.25 dB and 1 dB performance gains over existing advanced algorithms under quadrature phase shift keying (QPSK) modulation and 16-ary quadrature amplitude modulation (QAM), respectively.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,