FDD Massive MIMO Channel Estimation with Arbitrary 2D-Array Geometry

This paper addresses the problem of downlink channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the cdownlink channel. However, there are at least two shortcomings of these DFT-based methods: 1) they are applicable to uniform linear arrays (ULAs) only, since the DFT basis requires a special structure of ULAs, and 2) they always suffer from a performance loss due to the leakage of energy over some DFT bins. To deal with the above shortcomings, we introduce an off-grid model for downlink channel sparse representation with arbitrary 2D-array antenna geometry, and propose an efficient sparse Bayesian learning (SBL) approach for the sparse channel recovery and off-grid refinement. The main idea of the proposed off-grid method is to consider the sampled grid points as adjustable parameters. Utilizing an in-exact block majorization-minimization (MM) algorithm, the grid points are refined iteratively to minimize the off-grid gap. Finally, we further extend the solution to uplink-aided channel estimation by exploiting the angular reciprocity between downlink and uplink channels, which brings enhanced recovery performance.Comment: 15 pages, 9 figures, IEEE Transactions on Signal Processing, 201

High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches

Massive MIMO has been regarded as one of the key technologies for 5G wireless networks, as it can significantly improve both the spectral efficiency and energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design trade-offs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified.Comment: 15 pages, 3 figures, 1 table, submitted to IEEE Systems Journal Special Issue on 5G Wireless Systems with Massive MIM

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.Comment: 30 pages, 11 figure

Directional Modulation: A Secure Solution to 5G and Beyond Mobile Networks

Directional modulation (DM), as an efficient secure transmission way, offers security through its directive property and is suitable for line-of-propagation (LoP) channels such as millimeter wave (mmWave) massive multiple-input multiple-output (MIMO), satellite communication, unmanned aerial vehicle (UAV), and smart transportation. If the direction angle of the desired received is known, the desired channel gain vector is obtainable. Thus, in advance, the DM transmitter knows the values of directional angles of desired user and eavesdropper, or their direction of arrival (DOAs) because the beamforming vector of confidential messages and artificial noise (AN) projection matrix is mainly determined by directional angles of desired user and eavesdropper. For a DM transceiver, working as a receiver, the first step is to measure the DOAs of desired user and eavesdropper. Then, in the second step, using the measured DOAs, the beamforming vector of confidential messages and AN projection matrix is designed. In this paper, we describe the DOA measurement methods, power allocation, and beamforming in DM networks. A machine learning-based DOA measurement method is proposed to make a substantial SR performance gain compared to single-snapshot measurement without machine learning for a given null-space projection beamforming scheme. However, for a conventional DM network, there still exists a serious secure issue: the eavesdropper moves inside the main beam of the desired user and may intercept the confidential messages intended to the desired users because the beamforming vector of confidential messages and AN projection matrix are only angle-dependence. To address this problem, we present a new concept of secure and precise transmission, where the transmit waveform has two-dimensional even three-dimensional dependence by using DM, random frequency selection, and phase alignment at DM transmitter

Compressed Sensing for Wireless Communications : Useful Tips and Tricks

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to come up with simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips that wireless communication researchers need to know when designing CS-based wireless systems. First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this article will be a useful guide for wireless communication researchers and even non-experts to grasp the gist of CS techniques

Beamspace Channel Estimation in mmWave Systems via Cosparse Image Reconstruction Technique

This paper considers the beamspace channel estimation problem in 3D lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a 2D natural image; that is, the channel is sparse, and the changes between adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse non-informative parameter estimator-based cosparse analysis AMP for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also propose a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%

Compressive Sensing with Prior Support Quality Information and Application to Massive MIMO Channel Estimation with Temporal Correlation

In this paper, we consider the problem of compressive sensing (CS) recovery with a prior support and the prior support quality information available. Different from classical works which exploit prior support blindly, we shall propose novel CS recovery algorithms to exploit the prior support adaptively based on the quality information. We analyze the distortion bound of the recovered signal from the proposed algorithm and we show that a better quality prior support can lead to better CS recovery performance. We also show that the proposed algorithm would converge in \mathcal{O}\left(\log\mbox{SNR}\right) steps. To tolerate possible model mismatch, we further propose some robustness designs to combat incorrect prior support quality information. Finally, we apply the proposed framework to sparse channel estimation in massive MIMO systems with temporal correlation to further reduce the required pilot training overhead.Comment: 14 double-column pages, accepted for publication in IEEE transactions on signal processing in May, 201

Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems

In this paper, we propose a channel tracking method for massive multi-input and multi-output systems under both time-varying and spatial-varying circumstance. Exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is designed to reduce the effective dimensions of up-link and down-link channel, which decomposes channel state information into the time-varying spatial information and gain information. We firstly model the users movements as a one-order unknown Markov process, which is blindly learned by the expectation and maximization (EM) approach. Then, the up-link time varying spatial information can be blindly tracked by Taylor series expansion of the steering vector, while the rest up-link channel gain information can be trained by only a few pilot symbols. Due to angle reciprocity (spatial reciprocity), the spatial information of the down-link channel can be immediately obtained from the up-link counterpart, which greatly reduces the complexity of down-link channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method

Location-Aided Coordinated Analog Precoding for Uplink Multi-User Millimeter Wave Systems

Millimeter wave (mmWave) communication is expected to play an important role in next generation cellular networks, aiming to cope with the bandwidth shortage affecting conventional wireless carriers. Using side-information has been proposed as a potential approach to accelerate beam selection in mmWave massive MIMO (m-MIMO) communications. However, in practice, such information is not error-free, leading to performance degradation. In the multi-user case, a wrong beam choice might result in irreducible inter-user interference at the base station (BS) side. In this paper, we consider location-aided precoder design in a mmWave uplink scenario with multiple users (UEs). Assuming the existence of direct device-to-device (D2D) links, we propose a decentralized coordination mechanism for robust fast beam selection. The algorithm allows for improved treatment of interference at the BS side and in turn leads to greater spectral efficiencies.Comment: 17 pages, 4 figure