{"title":"基于机器学习的宽带大规模多输入多输出系统信道预测,在线训练开销小","authors":"Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi","doi":"10.1109/OJCOMS.2024.3449341","DOIUrl":null,"url":null,"abstract":"Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10647106","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Channel Prediction in Wideband Massive MIMO Systems With Small Overhead for Online Training\",\"authors\":\"Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi\",\"doi\":\"10.1109/OJCOMS.2024.3449341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. 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引用次数: 0
摘要
信道预测可以补偿多输入多输出(MIMO)系统中过时的信道状态信息。最近,人们利用无线信道的时间相关性,采用机器学习(ML)技术设计信道预测器。然而,大多数基于 ML 的信道预测技术在生成信道预测器时只考虑了离线训练,这可能导致在遇到与训练时不同的信道环境时性能不佳。为了确保在不同信道条件下的预测性能,我们提出了一个在线再训练框架,从头开始训练信道预测器,以有效捕捉和应对无线环境的变化。训练时间包括数据收集时间和神经网络训练时间,对于实用的信道预测器来说,训练时间应最小化。为了减少训练时间,尤其是数据收集时间,我们提出了一种基于 ML 的新型信道预测技术,即针对宽带大规模 MIMO 系统的聚合学习(AL)方法。在所提出的 AL 方法中,训练数据可以在阵列域或频域(即 MIMO-OFDM 系统的信道域)中进行拆分和聚合。这种处理方法可以大大减少数据收集的时间。我们的数值结果表明,AL 方法甚至能在各种情况下提高信道预测性能,而训练时间开销很小。
Machine Learning-Based Channel Prediction in Wideband Massive MIMO Systems With Small Overhead for Online Training
Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
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