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

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of the Communications Society Pub Date : 2024-08-28 DOI:10.1109/OJCOMS.2024.3451313
Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin
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引用次数: 0

摘要

无小区大规模多输入多输出(MIMO)网络最近成为一种有吸引力的解决方案,能够解决蜂窝网络小区边缘性能下降的问题。出于可扩展性考虑,最近提出了以用户为中心的集群,通过 AP 子集为用户提供服务。在动态移动场景中,这种网络组织需要预测传播参数的预测算法,以便通过主动为用户分配新的接入点来保持性能。然而,考虑到用户移动性带来的不确定性,如何以较低的计算复杂度准确预测非稳态信道中的信道增益演变是一项重大科学挑战。本文的新颖之处在于设计了一种基于多维 BiLSTM 的多变量信道增益预测算法,在降低计算复杂度的同时,达到了与以往研究类似的精度。事实上,由于多维 BiLSTM 结合了双重预测,并利用了来自多个天线的信道多样性,我们的模型减轻了顺序神经网络通常面临的误差传播问题。与混合 CNN-LSTM 模型相比,我们的模型误差至少降低了 2.7 倍,复杂度降低了 3.6 倍(对于单预测)。此外,与并行变压器解决方案相比,我们算法的复杂度增长率要低得多。
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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.
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来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
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.
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