Efficient Channel Estimation in mm Wave Massive MIMO Using Hybrid Beamforming

Hybrid beamformer design plays very crucial role in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. Previous works assume the perfect channel state information (CSI) which results heavy feedback overhead. To lower complexity, channel statistics can be utilized such that only infrequent update of the channel information is needed. To reduce the complexity and provide robustness, in this work, we propose a deep learning (DL) framework to deal with both hybrid beamforming and channel estimation. For this purpose, we introduce three deep convolutional neural network (CNN) architectures. We assume that the base station (BS) has the channel statistics only and feeds the channel covariance matrix into a CNN to obtain the hybrid precoders. At the receiver, two CNNs are employed. The first one is used for channel estimation purposes and the another is employed to design the hybrid combiners. The proposed DL framework does not require the instantaneous feedback of the CSI at the BS. We have shown that the proposed approach has higher spectral efficiency with comparison to the conventional techniques. The trained CNN structures do not need to be re-trained due to the changes in the propagation environment such as the deviations in the number of received paths and the fluctuations in the received path angles up to 4 degrees. Also, the proposed DL framework exhibits at least 10 times lower computational complexity as compared to the conventional optimization-based approaches.

Multiple Input Multiple Output (MIMO) systems with limited Radio Frequency (RF) chains play a pivotal role in the Fifth Generation (5G) of wireless networks. However, the transmitter of Generalized Spatial Modulation MIMO (GSM-MIMO) systems that characterized by a single RF chain and multiple active antennas is capable of reducing both the energy consumption and the transmitter cost. In this paper, combining GSM-MIMO systems with the fully digital Geometric Mean Decomposition (GMD)-based precoding scheme and Analog Beamforming (ABF) into Hybrid Beamforming (HBF) regime is presented for Millimeter-wave (mmWave) massive MIMO systems which is a key enabler for 5G networks. In addition, applying the norm-based user selection algorithm in GSM-MIMO scheme with GMD-based hybrid precoding is proposed, and referred to as Multiuser Steered GSM-MIMO (MUS-GSM-MIMO) scheme. Simulation results demonstrate that the proposed schemes are capable of achieving considerable performance gains over the conventional GSM-MIMO schemes, while avoiding the overwhelming costs of multiple RF chains. Therefore, the proposed schemes are very efficient and highly suitable for large-scale and 5G wireless networks.

Accurate channel state information (CSI) at base station (BS) is the key to achieve full multiplexing and diversity gain in traditional multiple-input multiple-output (MIMO) systems. In frequency-division-duplex (FDD) systems, it is obtained through downlink training and uplink feedback. However, the training and feedback overhead in massive MIMO systems is unacceptable for the large number of antennas equipped at BS. To reduce the CSI feedback overhead and improve CSI quantization accuracy, we propose a limited feedback scheme for spatio-temporal correlated massive MIMO channel. The proposed scheme jointly performs Karhunen-Loève transform (KLT) and polar-cap quantization on CSI. For the compactly deployed antennas at BS, there exists spatial correlation in massive MIMO channel. When the user equipment (UE) moves slowly, adjacent channels feature temporal correlation. The high dimensional and spatial correlated CSI is compressed through KLT. Then the compressed CSI is normalized to be isotropic to perform polar-cap quantization. Simulation results show that the proposed scheme is capable of improving the sum-rate performance with limited feedback bits for massive MIMO systems.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

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.

The fifth generation cellular communication is all about high speed internet access with devices consuming less amount of power. Millimeter wave Multiple Input Multiple Output (MIMO) systems is a promising technology that offers high data rates due to a large bandwidth. These systems are compact with minimal antenna heights which allows us to deploy these systems more widely. The mm-Wave Massive MIMO systems enhances the data rates to a large extent. But due to the increasing number of antennas to support high data rates, the power consumption of the system goes high. In this paper, a limited channel state information (CSI) based precoding technique has been used to optimize the power consumption and to reduce the computational complexity of the system. The use of Hybrid Precoder architecture with sub array of precoding blocks helps in reducing the computational complexity by many folds. The conditional switching between two stages minimizes the number of computations required. The estimation of Angle of arrival (AoA) enables the precoder to steer the beam at the transmitter for higher throughput. The smart antenna selection algorithm optimizes the power consumption of the system. The two – staged precoding algorithm proposed employs all these techniques to optimize the performance of mm-Wave MIMO systems.

As an important part of the channel fading, large scale fading should be considered in downlink massive multiple-input multiple-output (MIMO) systems. This paper investigates the downlink massive MIMO system over a large scale fading channel, when the base station takes advantage of maximum-radio transmission (MRT) precoding. The cases when the base station has perfect and imperfect channel state information (CSI) are considered respectively. Specially, through the analysis of single user's ergodic achievable rate, some novel and approximate expressions for the spectral efficiency (SE) are derived. Based on the approximate SE, the effect of the channel estimation error is analyzed intuitively. In addition, the average SE of all the users with different large-scale fading parameters is carefully investigated. Simulations validate that all the theoretical results coincide with numerical results and the large scale fading factors have little influence on SE reduction resulted from channel estimation.

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

The promise of future 5G networks is to provide very high capacity and ultra-reliable communication. Performance analysis of wireless systems is a key towards insightful design of current and future wireless systems. In this paper, we investigate the uplink performance of a single cell multi-user massive multiple-input multiple-output (MIMO) system over inverse-Gamma shadowing channels. Assuming perfect knowledge of the channel state information (CSI), we derive closed-form expressions for outage probability, average achievable rate and bit error rate (BER). Additionally, we obtain an approximate expression of the signal-to-interference-plus-noise ratio with imperfect CSI, and we derive the corresponding outage probability expression. The obtained expressions are assessed through numerous simulations and numerical examples that confirm the accuracy of our analysis. Furthermore, the results give a clear view on the impact of various parameter settings (including fading severity, transmit power, and the number of base station antennas) on the system’s performance.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

The multiple-input multiple-output (MIMO) wireless system can either be massive or conventional “small-scale” ones. It has been verified that the massive MIMO technology has a great potential to improve transmission range, spectral efficiency, and coverage compared to conventional MIMO. For realistic antenna configurations systems, there is an increase in the computational complexity associated with signal detection in massive MIMO systems. Several theoretical iterative techniques have been proposed to balance the computational complexity and performance in massive MIMO systems. However, most of these methods have very slow convergence rate. Therefore, we employ the multiple search direction conjugate gradient (MSD-CG) method in massive MIMO uplink systems to reduce the data detection complexity and enhance the performance. The efficiency of the proposed method has been improved by a projection technique that requires a search direction in each sub-domain rather than forming all search directions conjugate to each other. In this paper, we compare the proposed approach to the approximate and exact methods. Our results indicate that the proposed algorithm with realistic antenna configurations is superior to the existing methods for large-scale MIMO systems.

기존 MIMO 시스템 대비 수십 또는 수백 배 많은 송신 안테나들이 설치되어 있는 주파수 분할(frequency division duplex, FDD) 방식의 거대 다중 안테나(massive multiple-input mulitple-output, Massive MIMO)에선 송신 안테나 별 CSI-RS(channel state information reference signal) 전송을 위해 기존 MIMO 시스템 대비 너무 많은 하향 및 상향링크 자원 요소가 필요하다는 한계를 갖는다. 이러한 단점들을 극복하기 위해 본 논문에선 주파수 분할 방식의 거대 다중 안테나 시스템에서 많은 송신 안테나를 사용하더라도 자원요소(resource element, RE)를 재사용함으로써 송신 안테나 수에 비례하지 않는 제한된 양의 하향링크 자원만을 사용하여 CSI-RS를 효과적으로 전송하는 기술 및 자원격자 구조를 제안하였다. Since FDD massive MIMO (multiple-input multiple-output) system deploys hundreds of transmit antennas at base station (BS) compared to conventional MIMO system, the overhead of transmitting channel state information reference signal (CSI-RS) increases proportionally to the number of transmit-antennas. To overcome these disadvantages, we proposed beamforming based CSI-RS transmission technique for FDD massive MIMO system which transmit CSI-RS by limited amount of downlink resources.

Massive multiple-input multiple-output (MIMO) technology for wireless communication is considered a key technology for the future fifth-generation (5G) communication networks. To reduce the propagation loss of channel, MIMO systems with analog or digital hybrid precoders and combiners (HPC) architecture have been used. This review paper presents the various types of analog and digital HPC designs for the massive MIMO communication systems that were investigated within the last decade. The reviewed systems are useful for both single-user (SU) and multi-user MIMO (MU-MIMO) systems. The key parameter of the study was spectral efficiency (SE), signal-to-noise ratio (SNR), signal-to-interference pulse noise ratio (SINR), system sum-rate (SR) performance, computational complexity (CC), co-channel interference (CCI), inter-primary base station interference (IPBSI), multiuser interference (MUI), effective desired channel gain (EDCG), and minimum mean square error (MMSE).

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.