Joint User Selection and GMD-Based Hybrid Beamforming for Generalized Spatial Modulation Aided Millimeter-wave Massive MIMO Systems

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.

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.

Beamforming with MIMO (Multiple-Input Multiple-Output) system is only solution to maximize high data rate and extended cell coverage with satisfying quality of Service (QoS) for fifth generation (5G) cellular networks. In hybrid millimeter-wave (mmWave) massive MIMO systems, estimation of information about Channel State is difficult due to the large channel and small number of RF chains. The present paper accomplishes millimeter-wave-based massive MIMO for hybrid beamforming based on Sparse Estimation. The proposed iterative hybrid algorithms accomplish the low rank and beamforming sparsity properties in massive MIMO to gain full data recovery with minimal error for small time duration. The proposed work model is an mmWave-based beamforming system with imperfect channel state information (CSI) to minimize channel estimation errors. Experimental section highlights the efficiency of our proposed method over traditional methods for the sake of simulation with accounting its improved temporal efficiency, fast convergence, and tolerance to abnormality channel information.KeywordsBeamforming5GMIMOHybrid beamforming

Millimeter wave (mmWave) communication with advanced lens antenna arrays is a promising technology for achieving cost-effective 5G wireless systems. This paper studies a multi-user mmWave single-sided lens multiple-input multiple-output (MIMO) system in the uplink communication, where the base station (BS) is equipped with a full-dimensional (FD) lens antenna array with both elevation and azimuth angle resolution capabilities, and each mobile station (MS) has the conventional uniform planar array (UPA). With limited radio frequency (RF) chains at the BS and one single RF chain at each MS, we propose a low-complexity path division multiple access (PDMA) scheme to enable virtually interference-free multiuser communications, by exploiting the angle-dependent energy focusing property of the lens antenna array at the BS as well as the multi-path sparsity of mmWave channels. Besides, a new technique called path delay compensation is proposed at the BS to effectively transform the frequency-selective MIMO channel to parallel frequency-flat small-size MIMO channels, for each of which the low-complexity single-carrier (SC) transmission can be applied. Numerical results show significant sum-rate gain with the proposed design over benchmark systems.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

The singular value decomposition (SVD) is a popular technique used in multiple-input multiple-output (MIMO) systems to remove inter-antennas interferences in order to achieve the best performance. As a result, the MIMO channel is decomposed into a number of independent singular-input singular-output (SISO) channels with different weightings. In order to improve the performance, bit- and power-allocation strategies are required due to the unequal weighting coefficients. In contrast, the geometric mean decomposition (GMD) decomposes the MIMO channel into a number of equally weighted SISO channels with remaining inter-antenna interference which can be removed by using dirty paper precoding at the transmit side. Having equally weighted layers, the computational complexity required to implement bit- and power-allocation strategies decreases and GMD-based MIMO systems seem to be an appropriate solution. This paper analyses and compares the performance of SVD- and GMD-based MIMO systems affected by antennas correlation where QAM constellations are transmitted along the transmit antennas, demonstrating that the GMD-based one is more robust against antennas correlation. Furthermore, optimal and suboptimal bit- and power-allocation strategies are compared. This investigation demonstrates that the suboptimal solution provides a performance close to that offered by the optimal one but with a reduced computational cost.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

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.

Space shift keying (SSK) is a single radio frequency (RF) chain multiple-input multiple-output (MIMO) scheme which uses the indices of transmit antennas for information transmission. Time successive SSK-M-ary modulation (TSSM) is a second order transmit diversity scheme based on single RF chain multiple antenna transmitter. In this letter, TSSM based on selection combining (TSSM-SC) is conceived to realize a single RF chain receiver for TSSM without compromise on the diversity order. The metric of receiver antenna selection is proposed for TSSM-SC. A novel approach is given to find a tight upper bound on the bit error rate (BER) of TSSM-SC in simple closed form. Further, the diversity order of TSSM-SC is analyzed with asymptotic expression. Simulation results that validate the analyses are given to show the diversity gain at reduced receiver complexity. The performance of TSSM-SC is compared with other schemes with selection combining to highlight its performance advantage.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have recently been proposed to meet the needs of the future wireless communication standards. The efficient use of low resolution digital-to-analog converters (DACs) and hybrid architecture could significantly reduce the high power consumption associated with the mmWave MIMO system components. This paper designs an energy efficient transmitter with low resolution DACs for mmWave massive MIMO systems. An optimization problem is formulated and solved to find the optimal number of radio-frequency (RF) chains to be used at the transmitter to minimize the power consumption. This problem is constrained by the information loss which introduces the reduction of the number of the RF chains, expressed in terms of the system capacity. Rate and energy performance are compared with different beamforming techniques and architectures for various DAC resolutions.

Conventional MIMO systems typically use full digital processing for optimal performance while it requires an independent radio frequency (RF) chain for each antenna, which is not feasible for large-scale antenna arrays due to the high cost, high-power consumption and high complexity of RF chain components in high frequencies. Fortunately, in recent years, the Hybrid beamforming has been proposed as an efficient and auspicious technique for the practical implementation of millimeter-Wave (mmWave) multiple-input multiple-output (MIMO) wireless systems. In this paper we propose a novel HBF (Hybrid beamforming) algorithm and simulate the sum rate of the proposed approach, then compare it with other algorithms in the sum rates. Specifically, the HBF algorithm as well as the maximizing minimum phase difference algorithm are based on the MMSE (Minimum Mean Square Error). For the full-digital beamforming and the beam steering, the ZF (zero forcing) criterion is employed. Simulations demonstrate that the performance of the proposed algorithm is similar to the full digital algorithm, and its robustness is confirmed by comparisons with other existing algorithms under the same channel model.

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

In this paper, we consider a millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication system with hybrid beamforming architecture. To search the optimal beam pair, the beam sweeping algorithm is complex and time-consuming in the case of initial access and beam switching. With the aim of improving the robustness, we propose a novel beam management scheme to quickly determine the optimal beam and switch to the suboptimal beam. To implement the beam management scheme, we design a simple 2-dimensional (2-D) direction-of-arrival (DOA) estimation strategy using limited radio frequency (RF) chains in the hybrid array. Numerical results show that the proposed scheme can obtain an excellent achievable rate which is the same as that of the MIMO system employed optimal design. Further, the proposed beam management scheme greatly reduces the time cost of the beam selection.

In large-scale multiple-input multiple-output (MIMO) systems, high cost and high power consumption of RF chains typically prohibit the use of traditional baseband beamforming which requires one distinct radio-frequency (RF) chain per antenna. One possible architecture to reduce the number of RF chains is hybrid beamforming in which the overall beamformer consists of a concatenation of an analog RF beamformer implemented using phase shifters (PSs) and a low-dimensional baseband digital beamformer. However, conventional hybrid beamforming designs require high-resolution PSs, which are expensive. In this paper, we consider transceiver design for maximizing the spectral efficiency of a large-scale MIMO system with hybrid beamforming architecture where only finite-resolution PSs are available at both ends. We propose a heuristic transceiver design for the critical case where the number of RF chains is equal to the number of data streams. We show that the proposed hybrid beamforming design can achieve a rate close to that of optimal exhaustive search. We also suggest how to generalize the algorithm for the setting where the number of RF chains exceeds the number of data streams. We show that the generalized algorithm can use the extra RF chains to significantly improve the system performance in the case of low-resolution PSs.

Massive multiple-input and multiple-output systems combined with hybrid beamforming technique is a key approach to achieve high data rate and extended cell coverage in the fifth generation (5G) cellular networks . Specifically, the precoding for analog/digital hybrid beamforming technique is absolutely essential for millimeter-wave (mm-wave) frequency based transmission for 5G, simply because the technique reduces hardware complexity and energy consumption for high-frequency design. In order to prove the aforementioned advantages, the present paper will investigate the energy efficiency (EE) and optimization of low complexity hybrid precoding (EELCHP) algorithm to enable the reduction of radio frequency (RF) chains within the base station (BS). The proposed solution will also incorporate high-resolution phase shifters (HRPS) specifically for downlink multi-user mm-wave system. The intelligence within the EELCHP algorithm for hybrid beamforming is based on the fully complex zero-forcing (ZF) precoding mechanism that allowed the maximization of energy efficiency by reducing the RF chains, without affecting the potentially increasing number of antennas. In addition, maximum EE is possible by eliminating the nonlinear concave fractional issue based on the Lagrange duality method. During the simulation process of the proposed method, we further realized that hardware complexity and power consumption can be reduced by allowing the RF chains to select only one beam per switch within the beamforming solution. Furthermore, with the implementation of EELCHP algorithm, more energy is saved due to low complexity system by reducing the required RF chain.