Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin
{"title":"基于多变量 BiLSTM 模型的分布式大规模多输入多输出系统中的轨迹无感知信道增益预测","authors":"Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin","doi":"10.1109/OJCOMS.2024.3451313","DOIUrl":null,"url":null,"abstract":"Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5348-5363"},"PeriodicalIF":6.3000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654334","citationCount":"0","resultStr":"{\"title\":\"Trajectory-Unaware Channel Gain Forecast in a Distributed Massive MIMO System Based on a Multivariate BiLSTM Model\",\"authors\":\"Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin\",\"doi\":\"10.1109/OJCOMS.2024.3451313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.\",\"PeriodicalId\":33803,\"journal\":{\"name\":\"IEEE Open Journal of the Communications Society\",\"volume\":\"5 \",\"pages\":\"5348-5363\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654334\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Communications Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10654334/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10654334/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Trajectory-Unaware Channel Gain Forecast in a Distributed Massive MIMO System Based on a Multivariate BiLSTM Model
Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.
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.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.