On proportional fairness of uplink spectral efficiency in cell‐free massive MIMO systems

Spectral efficiency (SE) is one of the eminent requirements in 5G mobile networks. Cell-free (CF) massive MIMO is deemed a key technology to provide substantial SE in 5G compared with the cellular and small cell approaches.Most of the prior studies have been focusing on the access points (APs) uniform distribution to assess the SE performance. However, 5G networks are typically dense, irregularly distributed, and mostly constrained by channel impairments. Therefore, considering a uniform distribution of APs is unrealistic. This paper considers a practical network distribution by taking into

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On the spectral efficiency of cell‐free massive MIMO system in irregular 5G mobile networks

Zbairi

Ez‐zazi

Yazid

et al. 2022

Int J Communication

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Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems

Khanh¹,

Pham²,

Kha³

et al. 2021

JAIT

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In this paper, we introduce a Deep Neural Network (DNN) to maximize the Proportional Fairness (PF) of the Spectral Efficiency (SE) of uplinks in Cell-Free (CF) massive Multiple-Input Multiple-Output (MIMO) systems. The problem of maximizing the PF of the SE is a nonconvex optimization problem in the design variables. We will develop a DNN which takes pilot sequences and largescale fading coefficients of the users as inputs and produces the outputs of optimal transmit powers. By consisting of densely residual connections between layers, the proposed DNN can efficiently exploit the hierarchical features of the input and motivates the feed-forward nature of DNN architecture. Experimental results showed that, compared to the conventional iterative optimization algorithm, the proposed DNN has excessively lower computational complexity with the trade-off approximately only 1% loss in the sum-rate and the fairness performance. This demonstrated that our proposed DNN is reasonably suitable for real-time signal processing in CF massive MIMO systems.Index Terms deep neural networks, proportional fairness, spectral efficiency, cell-free massive MIMO I. INTRODUCTIONIn recent years, Deep Neural Networks (DNNs) have obtained exceptional achievements in a variety of applications, especially in image processing. In [1], the authors proposed a deep network architecture named as Residual Network (ResNet) which consists of shortcut connections between layers to counteract the degradation problem in training significant DNNs. Numerical simulations demonstrated that in image classification and recognition, ResNets can achieve excellent results with significant depth, e.g. 1000 layers. In [2], the authors introduced a dense convolutional network, namely DenseNet. By feeding feature maps of previous layers to all subsequent layers, the DenseNets not only mitigate the gradient vanishing but also excessively deduct the trainable parameters. Numerical results shown that, compared to other state-of-the-art methods in the task of image classification, especially the ResNets [1], the DenseNets can obtained lower error rates with less computational efforts. In image restoration, deep

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On proportional fairness of uplink spectral efficiency in cell‐free massive MIMO systems

Cited by 2 publications

References 24 publications

On the spectral efficiency of cell‐free massive MIMO system in irregular 5G mobile networks

On the spectral efficiency of cell‐free massive MIMO system in irregular 5G mobile networks

Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems

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