用于 OTFS 的低复杂度期望传播探测器

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Signal Processing Pub Date : 2024-10-25 DOI:10.1049/2024/3256977

Xumin Pu, Zhinan Sun, Wanli Wen, Qianbin Chen, Shi Jin

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引用次数: 0

摘要

本文提出了一种低复杂度期望传播（EP）检测器，适用于具有实用矩形波形的正交时频空间（OTFS）系统。在高移动性场景中，OTFS 正在成为第六代（6G）无线通信系统的一种潜在方案。然而，有效延迟-多普勒（DD）域信道矩阵的巨大尺寸给基于矩阵反演的信号检测算法带来了难以承受的计算复杂度。我们提出了一种基于 DD 域有效信道协方差矩阵的稀疏性和块环状结构的低复杂度 EP 检测器。所提出的算法只需要对数线性复杂度。此外，仿真结果表明，所提算法不仅具有复杂度低的优势，而且性能良好，实现了性能与复杂度之间的权衡。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A Low-Complexity Expectation Propagation Detector for OTFS

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A Low-Complexity Expectation Propagation Detector for OTFS

In this paper, we propose a low-complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high-mobility scenario, OTFS is becoming a potential scheme for the sixth-generation (6G) wireless communication system. However, the large size of the effective delay-Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low-complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log-linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity.

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf