Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

IF 2.2 3区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Symmetry-Basel Pub Date : 2023-10-24 DOI:10.3390/sym15111968
Wen-Yen Lin, Tin-Hao Chang, Shu-Ming Tseng
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Abstract

We propose a deep learning-based cross-layer power allocation method for asymmetric cell-free massive MIMO video communication systems. The proposed cross-layer approach considers physical layer channel state information (CSI) and the application layer rate distortion (RD) function, and it aims to enhance video quality in terms of peak signal-to-noise ratio (PSNR). Our study develops a decentralized deep neural network (DNN) model to capture intricate system patterns, enabling accurate and efficient power allocation decisions. The proposed cross-layer approach includes unsupervised and hybrid (supervised/unsupervised) learning models. The numerical results show that the hybrid method achieves convergence with just 50% of the iterations required by the unsupervised learning model and that it achieves a 1 dB gain in PSNR over the baseline physical layer scheme.
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基于深度学习的下行无小区大规模多输入多输出视频通信系统跨层功率分配
针对非对称无小区大规模MIMO视频通信系统,提出了一种基于深度学习的跨层功率分配方法。该跨层方法考虑了物理层信道状态信息(CSI)和应用层速率失真(RD)函数,旨在从峰值信噪比(PSNR)方面提高视频质量。我们的研究开发了一个分散的深度神经网络(DNN)模型来捕获复杂的系统模式,从而实现准确有效的功率分配决策。提出的跨层方法包括无监督和混合(监督/无监督)学习模型。数值结果表明,混合方法的收敛速度仅为无监督学习模型所需迭代次数的50%,并且比基线物理层方案的PSNR增益为1 dB。
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来源期刊
Symmetry-Basel
Symmetry-Basel MULTIDISCIPLINARY SCIENCES-
CiteScore
5.40
自引率
11.10%
发文量
2276
审稿时长
14.88 days
期刊介绍: Symmetry (ISSN 2073-8994), an international and interdisciplinary scientific journal, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided, so that results can be reproduced.
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