Deep Convolutional Compression For Massive MIMO CSI Feedback

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 numbers 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 DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous deep learning DL-based CSI reduction structures, DeepCMC includes quantization and entropy coding blocks and minimizes 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.

Hardware-Limited Task-Based Quantization

“…1) Passing Gradient: In this approach the quantized values are modeled as the analog values corrupted by mutually independent i.i.d. noise [22], [23], [28], and thus quantization does not affect the back-propagation procedure. Since the quantization error is deterministically determined by the analog value [32], the resulting model is quite inaccurate.…”

ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

“…For feedback reduction, the DL-based CSI feedback developed in [7]- [17] could be classified into two categories. The first category is mainly based on a neural network called CsiNet [7], which achieved superior performance over various CS-based CSI feedback.…”

“…To alleviate this issue, compressive sensing (CS)-based CSI feedback methods have been proposed in [3]- [6], in which the temporal correlation [3], sparse enhancement basis [4], and spatial correlation [4]- [6] of CSI are developed. However, the downlink CSI is approximately sparse for a specific model rather than a general assumption, which may cause practical problems when the hypothesis is not valid [7]- [17].…”

CNN-Based Analog CSI Feedback in FDD MIMO-OFDM Systems

“…The received signal at BS then passes through demodulation, channel decoding, to give the bit stream input to the decoder. The encoder and the decoder are trained jointly according to the loss function presented in [14] with a parameter, λ, governing the tradeoff between reconstruction quality and the feedback rate. "Conv|256| 9×9| ↓ 4|BN|PRelu" represents a convolutional layer with 256 9×9 kernels followed by downsampling by a factor of 4, batch normalization and PRelu activation.…”

“…While the previous works on DL-based CSI feedback focus on the digital CSI feedback scheme [14]- [17], here we design a DL-based analog feedback scheme taking into account the uplink channel explicitly. We use a fully convolutional autoencoder model to efficiently map the downlink CSI at the UE to the uplink channel inputs, and to reconstruct them at the BS.…”