Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO

In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a feedback link. However, transmitting CSI to the BS is costly due to the bandwidth limitation of the feedback link. Deep learning (DL) has recently achieved remarkable success in CSI feedback. Realizing high-performance and low-complexity CSI feedback is a challenge in DL based communication. We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively. However, this network cannot be effectively applied to the mobile terminal because of the excessive numbers of parameters. Therefore, we further propose a new lightweight CSI feedback network based on the developed network. Simulation results show that the proposed CSI network exhibits better reconstruction performance than that of other CsiNet-related works. Moreover, the lightweight network maintains a few parameters and parameter complexity while ensuring satisfactory reconstruction performance. These findings suggest the feasibility and potential of the proposed techniques.Comment: 5 pages, 2 figures, 2 table

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

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral efficiency. Convolutional neural network (CNN)-based CSI feedback compression schemes has received a lot of attention recently due to significant improvements in compression efficiency; however, they still require reliable feedback links to convey the compressed CSI information to the BS. Instead, we propose here a CNN-based analog feedback scheme, called AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input. Corresponding noisy channel outputs are used by another CNN to reconstruct the DL channel estimate. Not only the proposed outperforms existing digital CSI feedback schemes in terms of the achievable downlink rate, but also simplifies the operation as it does not require explicit quantization, coding and modulation, and provides a low-latency alternative particularly in rapidly changing MIMO channels, where the CSI needs to be estimated and fed back periodically

Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems

Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the level of channel sparsity. To address the issue, a recent approach adopts deep learning (DL) to compress CSI into a codeword with low dimensionality, which has shown much better performance than the CS algorithms when feedback link is perfect. In practical scenario, however, there exists various interference and non-linear effect. In this article, we design a DL-based denoise network, called DNNet, to improve the performance of channel feedback. Numerical results show that the DL-based feedback algorithm with the proposed DNNet has superior performance over the existing algorithms, especially at low signal-to-noise ratio (SNR)

AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.Comment: accepted with minor revision for publication in IEEE Signal Processing Letter

Aggregated Network for Massive MIMO CSI Feedback

In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station. The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain. Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods. In this paper, a novel network named ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation. Moreover, valid approach to expand the network architecture in exchange of better performance is first discussed in CSI feedback task. Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.Comment: 5 pages, 8 figures, 3 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Learning the CSI Denoising and Feedback Without Supervision

In this work, we develop a joint denoising and feedback strategy for channel state information in frequency division duplex systems. In such systems, the biggest challenge is the overhead incurred when the mobile terminal has to send the downlink channel state information or corresponding partial information to the base station, where the complete estimates can subsequently be restored. To this end, we propose a novel learning-based framework for denoising and compression of channel estimates. Unlike existing studies, we extend a recently proposed approach and show that based solely on noisy uplink data available at the base station, it is possible to learn an autoencoder neural network that generalizes to downlink data. Subsequently, half of the autoencoder can be offloaded to the mobile terminals to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training data. Numerical simulations demonstrate the excellent performance of the proposed method.Comment: Final versio

Multi-resolution CSI Feedback with deep learning in Massive MIMO System

In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNetComment: 6 pages, 5 figures, 4 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems

A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and ultra low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.Comment: 7 pages, 6 figure

Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems

With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based scheme, joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division duplex (OFDM) systems. The proposed NN architecture exploits fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense neural network (NN) layers during training. Our novel pruning-based pilot reduction technique effectively reduces the overhead by allocating pilots across subcarriers non-uniformly; allowing less pilot transmissions on subcarriers that can be satisfactorily reconstructed by the subsequent convolutional layers successfully exploiting inter-frequency and inter-antenna correlations in the channel matrix.Comment: Submitted for publicatio