Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

In multiuser massive MIMO systems, it is not clear whether users should feed back the channel or the precoder when they can exchange the channel state information (CSI). This paper compares the precoder feedback scheme with the channel feedback scheme. It is found that when there are sufficient number of bits for CSI exchange, the precoder feedback scheme can reduce the interference leakage to 1 /(K − 1) of the channel feedback scheme, where K is the number of users. Moreover, the interference leakage under the precoder feedback scheme decreases faster than the channel feedback scheme when the number of feedback bits increases.

Hybrid precoding has been recognized as a promising technology for millimeter wave-based massive MIMO systems. As an improved scheme for conventional hybrid precoding structures, the adaptively-connected structure requires low hardware cost and has attractive system performance. In this paper, we propose an adaptive hybrid precoding (AHP) scheme for multi-user massive MIMO systems. The hybrid precoder design is formulated as minimizing the difference between the hybrid precoder and the full digital precoder, and a rough design and revision based AHP (RDR-AHP) algorithm is developed to seek for a near-optimal solution. The proposed RDR-AHP algorithm is achieved by a three-step process including rough design of baseband precoder based on singular value unification, stepwise design of radio frequency precoder and baseband precoder revision. Simulation results demonstrate that the proposed scheme achieves higher spectral efficiency than existing schemes with same hardware cost in millimeter wave channel.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

In this paper, we develop the weighted sum-rate maximization (WSRM) problem for the cooperative multicell multiuser massive MIMO (C4M) system with the limited cooperative scheme where each base station (BS) has user data of all the users in a system. It is shown that by the simple domain change technique, the WSRM problem can be exploited for hidden mono-tonicity and turn out to be a monotonic optimization problem. We propose a robust algorithm for discretized approximation of the Pareto boundary of achievable rate region. Using this algorithm, the normal boundary intersection (NBI) problem is solved. We solve the WSRM problem by using the outer polyblock approximation (OPA) algorithm. Through simulation results, we show that our proposed method has high accuracy as well as a fast convergence rate.

Noncoherent multi-user detection schemes in combination with subspace projection were shown to be an attractive approach in multi-user massive MIMO systems. Up to now, the subspace projection has been performed via a singular value decomposition (SVD) of the receive matrix and nulling the non-signal dimensions. In this paper, we employ an alternative approach to the subspace estimation step, namely the signed URV (SURV) decomposition. We compare the two schemes in terms of complexity and performance in noncoherent massive MIMO systems. Numerical results cover the complexity reduction achieved by a suited choice of the parameters in the SURV strategy.

Multi-user technique is a method which allows a transmitter serves multiple users in the same time. In the Massive-MIMO, Multi-user technique could be implemented with the help of beamforming technique which allows the system to create several beams in the same time, where every beam serves a different user. In this paper, we introduce a method called Fair-Chordal Distance User Selection (Fair-CDUS) to equalize the user selection in Massive-MIMO System. The fairness level which is measured in this research is the frequency of user selection. In this paper, we also explain how to adjust the degree of the fairness with our proposed method.

A multiuser massive MIMO system with mutual coupling is investigated in finite-dimensional channel scenarios. The uplink ergodic achievable rate is analytically derived for a multiuser massive MIMO system equipped with a rectangular planar uniform antenna array at the base station (BS). Numerical results show that the mutual coupling effect reduces the uplink achievable rate of multiuser massive MIMO systems when the antenna distance decreases. But when the size of the antenna array is fixed, the uplink achievable rate increases with the growing of antenna number.

The design procedure of hybrid beamforming is investigated considering multi-user MIMO communication systems taking into account the number of independent data streams for each user. Hybrid precoding and combining are utilized at the transmitter and receiver sides of millimeter-wave multi-user massive MIMO. The obtained results indicate the trade-off in performance based on the ratio of the number of antennas to the number of data streams at the transmitter as well as the receiver. The optimization of the number of parallel data streams per user allows the increase of transmitted data rate while keeping the levels of error vector magnitude (EVM) at acceptable levels.

This paper characterizes the performance of massive multiuser spatial modulation MIMO systems, when a regularized form of the least-squares method is used for detection. For a generic distortion function and right unitarily invariant channel matrices, the per-antenna transmit rate and the asymptotic distortion achieved by this class of detectors are derived. Invoking an asymptotic characterization, we address two particular applications. Namely, we derive the error rate achieved by the computationally-intractable optimal Bayesian detector, and we propose an efficient approach to tune LASSO-type detectors. We further validate our derivations through various numerical experiments.

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

In massive MIMO, precoding has an interference cancellation issue because the channel size has a considerable dimension based on the number of antennas and receivers. This paper proposes complexity-efficient precoding for single-cell multiuser massive MIMO systems that iteratively subtract interference replica signals based on a maximal ration transmission (MRT) weight. Moreover, it additionally proposes zeroing minute replica components to avoid excessive interference cancellation. Its remaining power can be diverted to the desired signal, improving the signal-to-interference-plus-noise power ratio (SINR) ratio. In addition, processing related to zero-ed components can be omitted, contributing to reduced computation complexity. Simulation results show its effectiveness in a specified region.

This paper deals with antenna selection for multi-user massive MIMO systems with the aim of maximising energy efficiency. Massive MIMO technology, by employing a large number of antennas at base station, provides huge improvements in throughput and energy efficiency. The increased number of antennas leads to additional energy consumption due to RF chains and signal processing circuit. The main purpose of the paper is to determine the optimal subset of antennas at the base station that should be activated to serve a given number of active user equipment. This idea is implemented using artificial bee colony algorithm which has proven it efficiency by specifying the best control parameters.

The demand for higher data traffic is exponentially growing. Since last 2 years or so, new research movements have emerged towards LTE-B (Release 13 and beyond) and 5th generation (5G) networks. As the next generation of cellular networks are going to be an integration of different radio access technologies and denser in term of space and frequency, the problem of interference management becomes more vital and challenging for academic and industrial units. One of the main technical challenges of interference management in the frequency division duplex system is the issue of channel state information (CSI) feedback overhead. Accordingly, this article briefly overviews the key candidate technologies for LTE-B and 5G and as well as to addresses their features and problems from the interference management perspective. Then, the problem of limited feedback in the multi-user MIMO systems is formulated and discussed. Afterward, the performance of distributed antenna systems is investigated under imperfect CSI and different phases of limited feedback technique. It is shown that accuracy of CSI feedback plays a vital role towards the realization of next generation of cellular systems. Finally, the future research directions are addressed in this domain.

One major practical issue for the implementation of frequency division duplex (FDD) massive MIMO systems is that the acquisition of channel state information at the transmitter side (CSIT) requires overwhelming pilot training and feedback overhead. Recently, compressive sensing (CS) based CSIT estimation approaches have been proposed to reduce the pilot training overhead for massive MIMO systems. However, it is very difficult to compute the minimum required pilot training overhead at the base station because of loose restricted isometry property (RIP) bounds for successful CS recovery and unknown sparsity levels. In this paper, we consider a framework of closed-loop compressive CSIT estimation with 1 bit feedback to learn the minimum required pilot overhead to achieve a certain target CSIT MSE without explicit knowledge of channel sparsity. We analyze the convergence behaviors of the multi-loop pilot overhead adaptation based on the Lyapunov approach. Simulations show that the proposed closed-loop compressive CSIT estimation framework has substantial performance gain over conventional open-loop algorithms and is very robust to dynamic sparsity as well as model mismatch.

In this paper, a hybrid multi-user massive MIMO system operating on the mmWave frequency is considered. To reduce the heavy beam training overhead for such a massive MIMO system, we propose a 3D beam training algorithm, which estimates the azimuth and elevation of the angle of arrival (AoA) and departure (AoD) between the base station (BS) and the mobile stations (MSs). The proposed algorithm first executes a hierarchical search to acquire the range of azimuth and elevation, then applies an exhaustive search within this range for a more precise matching. Through this two-stage approach, the beam training overhead can be substantially reduced.