Abstract
Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In a frequency division duplex (FDD) multiuser massive MIMO network, each user needs to compress and feedback its downlink CSI to the BS. The CSI overhead scales with the number of antennas, users and subcarriers, and becomes a major bottleneck for the overall spectral efficiency. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepCMC</i> , composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous DL-based CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural network (NN) architecture, with residual layers at the decoder, and incorporates quantization and entropy coding blocks into its design. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead. Simulation results demonstrate that DeepCMC outperforms the state of the art CSI compression schemes in terms of the reconstruction quality of CSI for the same compression rate. We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a single MIMO user for compression, but also benefits from the correlations among the channel matrices of nearby users to further improve the performance in comparison with DeepCMC. We also propose a reduced-complexity training method for distributed DeepCMC, allowing to scale it to multiple users, and suggest a cluster-based distributed DeepCMC approach for practical implementation.
Highlights
Massive multiple-input multiple-output (MIMO) systems are considered as the main enabler of 5G and future wireless networks thanks to their ability to serve a large number of users simultaneously, achieving impressive levels of energy and spectral efficiency
Our experiments reveal that adding the shortcut connections across two residual blocks at the decoder and choosing parametric rectified linear unit (PReLU) as the activation function improves the performance of DeepCMC
DeepCMC is composed of fully convolutional layers followed by quantization and entropy coding blocks, and outperforms state of the art deep learning (DL)-based channel state information (CSI) compression techniques, providing drastic improvements in CSI reconstruction quality at even extremely low feedback rates
Summary
Massive multiple-input multiple-output (MIMO) systems are considered as the main enabler of 5G and future wireless networks thanks to their ability to serve a large number of users simultaneously, achieving impressive levels of energy and spectral efficiency. In time division duplex (TDD) mode of operation, massive MIMO systems can exploit the uplink CSI for downlink transmission, thanks to channel reciprocity. The resulting feedback overhead becomes excessive due to the massive number of antennas and users being served, and has motivated various CSI reduction techniques based on vector quantization [4] and compressed sensing (CS) [5], [6]. In vector quantized CSI feedback, the overhead scales linearly with system dimensions, which becomes restrictive in many practical massive MIMO scenarios. CS-based approaches rely on sparsity of the CSI data in a certain transform domain, which may not represent the channel structure accurately for many practical MIMO scenarios. CS-based approaches are iterative, which introduces additional delay
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