Design Of MIMO Systems Research Articles

Machine Learning Based Beam Selection With Low Complexity Hybrid Beamforming Design for 5G Massive MIMO Systems

In this paper, we present an energy-efficient joint machine learning based beam-user selection and low complexity hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. Massive MIMO system is a key enabler of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation (5G) technology which is being used in vehicle-to-everything (V2X) communications and other Internet-of-Things (IoT). The large number of antennas at the base-station side causes high power consumption in the radio frequency (RF) chain. Hybrid beamforming techniques are used to reduce the number of RF chains with minimal performance loss. This paper proposes a low complexity orthogonal hybrid beamforming design with the aid of a machine learning based beam-user selection scheme. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Householder</i> (HH) reflectors are used to generate the orthogonal analog beamforming (ABF) matrix. We use a feedforward neural network (FFNN) beam-user selection scheme. The proposed HH ABF + FFNN scheme provides better energy-efficiency (EE) performance in the ill-conditioned massive MIMO channel. It has been shown that the green SE-EE point (41.35 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/s/Hz</i> , 0.8 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/Hz/J</i> ) can be obtained by the proposed HH ABF + FFNN scheme as compared to the (33.77 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/s/Hz</i> , 0.63 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b/Hz/J</i> ) from the state-of-the-art Discrete Fourier transform based techniques.

Hardware Phase Shift Hybrid Precoding Designs For Multi-User Massive MIMO Systems

Hybrid precoding is a challenging design for massive MIMO systems that involves a combination of analog and digital processing, aiming to maximize the spectral efficiency (SE). Most works on hybrid precoding focus on the single phase shifter (SPS) implementation to adapt the phase from RF chains to antennas. In this paper we propose to develop the double phase shifter (DPS) and the fixed phase shifter (FPS) in both single-path and multi-path configuration. Simulation results certify a significant improvement for both proposed implementations with high hardware efficiency (HE) and high spectral efficiency especially in multi-path environment.

Distributed MIMO System Design and Channel Capacity Analysis Based on Polar Orbit LEO Constellation

Distributed MIMO System Design and Channel Capacity Analysis Based on Polar Orbit LEO Constellation

Tripolarizated MIMO: Experimental Analysis and Modeling of Channel Properties in Both Indoor and Outdoor Scenarios

The tripolarized MIMO system can provide one more degree of freedom and have a more compacted size over a dual-polarized MIMO system, which is attractive for high-capacity wireless communication systems. In this paper, we analyze and model channel properties for tripolarized MIMO systems based on experimental channel measurements in typical indoor and outdoor scenarios. Firstly, channel measurement campaigns in the laboratory and the Urban Micro (UMi) scenarios on sub-6 GHz bands are presented. Then, based on measured data, path loss, delay spread (DS), and cross-polarization discrimination (XPD) for 9 polarization combinations are analyzed and modeled in a statistical way. Statistical results of these channel properties are also given. It is observed that channel properties of both large-scale fading and small-scale fading depend strongly on the polarization direction. Furthermore, we evaluate the performance of tripolarized MIMO systems by analyzing the Demmel condition number and channel capacity gain (CG). For both the indoor and the outdoor scenarios, it is found that colocated tripolarized antenna can bring a nearly threefold CG with respect to the unipolarized one. These results can give good insights into the design and evaluation of tripolarized MIMO systems.

Iterative geometric mean decomposition based secure hybrid precoder design for mmWave massive MIMO communication systems

Physical layer security (PLS) is one of the major concerns in modern mobile communication systems and has got special attention in millimeter-wave (mmWave) communication systems. The principal objective of this study is to construct a hybrid precoder that can secure the mmWave communication systems by incorporating the finest techniques to obtain a secure precoder and combiner for the legitimate receiver and to ensure minimum information leakage towards illegal-user. In this context, firstly, a traditionally coordinated precoder and combiner design algorithm is exploited in the analog domain to protect the legitimate receiver, and then, iterative geometric mean decompositions (IGMD) scheme is applied to obtain a digital precoder and combiner. Moreover, artificial noise (AN) is also transmitted towards illegal-user that further alleviates the privacy of the system. Finally, we compared our results with our latest proposed algorithm for a secure hybrid precoder system based on the generalized triangular decomposition (GTD) method and a recent algorithm presented in the literature. The simulation studies confirmed that the IGMD based approach considerably degrades the eavesdropping capabilities of the undesired user and provides significantly better secrecy rate performance as much as 3.5 bps/Hz than its counterparts.

Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise

Massive multiple-input multiple-output (MaMi) systems have attracted much research attention during the last few years. This is because MaMi systems are able to achieve a remarkable improvement in data rate and thus meet the immensely ongoing traffic demands required by the future wireless networks. To date, the downlink training sequence (DTS) for the frequency division duplex (FDD) MaMi communications systems have been designed based on the idealistic assumption of white noise environments. However, it is essential and more practical to consider the colored noise environments when designing an efficient DTS for channel estimation. To this end, this paper proposes a new DTS design by exploring the joint use of spatial channel and noise covariance matrices, when the channel is not reciprocal but the coherence block length remains limited. We derive an analytical solution for the mean square error (MSE) based on the proposed training design with colored noise. In addition, this paper exploits the method of random matrix theory to provide an analytical solution for the downlink (DL) achievable sum rate of the regularized zero forcing beamforming (RZFBF) precoder. Numerical results demonstrate that using the proposed DTS design, the MSE of the channel estimate is significantly reduced compared with the conventional training designs with white noise. Furthermore, the results show that the proposed pilot design markedly improves the DL achievable SR over the conventional training designs, especially at relatively low signal-to-noise-ratio (SNR) levels. This enables FDD MaMi systems to operate under more practical scenarios of colored noise and limited coherence time environments.

Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

Pareto-Optimal Pilot Design for Cellular Massive MIMO Systems

We introduce a non-orthogonal pilot design scheme that simultaneously minimizes two contradicting targets of channel estimation errors of all base stations (BSs) and the total pilot power consumption of all users in a multi-cell massive MIMO system, subject to the transmit power constraints of the users in the network. We formulate a multi-objective optimization problem (MOP) with two objective functions capturing the contradicting targets and find the Pareto optimal solutions for the pilot signals. Using weighted-sum-scalarization technique, we first convert the MOP to an equivalent single-objective optimization problem (SOP), which is not convex. Assuming that each BS is provided with the most recent knowledge of the pilot signals of the other BSs, we then decompose the SOP into a set of distributed non-convex optimization problems to be solved at individual BSs. Finally, we introduce an alternating optimization approach to cast each one of the resulting distributed optimization problems into a convex linear matrix inequality (LMI) form. We provide a mathematical proof for the convergence of the proposed alternating approach and a complexity analysis for the LMI optimization problem. Simulation results confirm that the proposed approach can significantly reduce pilot power, whilst maintaining the same level of channel estimation error as in a baseline.

Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen's inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

Performance Analysis and Joint Statistical Beamformer Design for Multi-User MIMO Systems

In this letter, we propose techniques for joint transmit and receive beamforming in downlink multi-user MIMO systems under Rayleigh fading channels using knowledge of the second-order statistics. The contribution of our work is twofold. First, we derive a closed-form expression for the outage probability for a general channel model using a recent covariance shaping method and for the conventional Kronecker structured model. Second, we present an outage minimization technique obtained by designing the transmit and receive beamformers. The transmit beamformers maximize the statistics of the signal-to-leakage-plus-noise ratio, while the receive beamformers minimize the cross-covariance of all users. Our techniques are “blind” in the sense that they do not require the transmission of pilot symbols, nor the estimation of the instantaneous state of the channel, making them bandwidth efficient. The derived theoretical results are validated via Monte Carlo simulations and show significant reduction in the outage probability by employing the proposed statistical beamforming techniques.

Pilot Design and Allocation for Multi-Cell Massive MIMO Systems With Two-Tier Receiving

This paper investigates pilot design and allocation in multi-cell massive multiple-input multiple-output systems with two-tier receiving. As the length of pilot is limited, the pilots cannot be orthogonal and cause interference. The sum rate as well as the signal-to-interference-plus-noise ratio (SINR) of the system are firstly analyzed. The sum rate upper bound and the asymptotic SINR are derived. The analysis shows that the pilot correlation and the spatial correlation jointly affect the sum rate. Based on this observation, the discrete Fourier transform vectors are employed in designing pilots with various correlation values. Then, with the channel correlation information and the designed pilots, the pilots are allocated to the cells in a greedy way with the object of maximizing the sum rate upper bound. Moreover, the sum rate analysis, the pilot design, and the pilot allocation are extended to the scenario with grouped correlations. Simulation results show that the proposed pilot design and allocation performs better than other pilots and the derived upper bound is effective.

A Design of MIMO System Based on Y-Shaped with QSCS for UWB Applications

A Design of MIMO System Based on Y-Shaped with QSCS for UWB Applications

Design of dual ports coplanar waveguide MIMO system for UWB applications

In this research, a compact of four elements of dual ports coplanar wave guide (CPW) ending with T-shape stub are installed each one on a corner of 85×134 mm2 printed circuit board (PCB). Each element is connected through the SMA connector “panel mount jack receptacle-2-hole flange-tap terminal”. A twin of opened circular parasitic rings are installed in front of each T-stub. A large number of try and error are applied to select the optimum geometry dimensions of the proposed paradigm. The result of paradigm show that each element operated with a resonance frequency 6.35 GHz, 7.1 GHz is the operating frequency overall system. Also, by utilizing the twin of opened circular parasitic ring, a good isolation is achieved with -48 dB. The obtained band frequency 1.74 (6.64-8.38) GHz are covered an important part of UWB which may be used at a sub band of 5G. In addition, the size of the prototype is suitable for future smartphone applications

Hybrid combiner design for downlink massive MIMO systems

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

Constrained Channel Decomposition-Based Hybrid Beamforming for mmWave Massive MIMO Systems

In this article, we propose a hybrid beamforming design for multiuser mmWave massive MIMO systems. We adopt a two-stage approach for designing the analog and digital beamforming separately. The analog beamforming design is based on a constrained low-rank channel decomposition and aims to simultaneously harvest the array gain and reduce the intra- and inter-user interference. The digital beamforming design is conducted by using the regularized channel diagonalization method, which provides a better trade-off between multiuser interference suppression and transmit diversity, thus attaining a better performance in low-SNR scenarios or when communicating to many users or through many data streams. We validate the effectiveness of the proposed design through numerical simulations, which have shown that our design outperforms several other hybrid beamforming designs in the literature.

Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems With Quantized Phase Shifters

In this paper, a two-step codeword design approach for millimeter wave (mmWave) massive MIMO systems is presented. Ideal codewords are first designed, which ignores the hardware constraints in terms of phase shifter resolution and the number of RF chains. Based on the ideal codewords, practical codewords are then obtained taking the hardware constraints into consideration. For the ideal codeword design in the first step, additional phase is introduced to the beam gain to provide extra degree of freedom. We develop a phase-shifted ideal codeword design (PS-ICD) method, which is based on alternative minimization with each iteration having a closed-form solution and can be extended to design more general beamforming vectors with different beam patterns. Once the ideal codewords are obtained in the first step, the practical codeword design problem in the second step is to approach the ideal codewords by considering the hardware constraints of the hybrid precoding structure in terms of phase shifter resolution and the number of RF chains. We propose a fast search based alternative minimization (FS-AltMin) algorithm that alternatively designs the analog precoder and digital precoder. Simulation results verify the effectiveness of the proposed methods and show that the codewords designed based on the two-step approach outperform those designed by the existing approaches.

Design of Massive Multiuser MIMO System to Mitigate Inter Antenna Interference and Multiuser Interference in 5G Wireless Networks

Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput and spectral efficiency in 5G communication networks. Inter-antenna Interference (IAI) and Multi-user Interference (MUI) are two major factors that influence the performance of MU–MIMO system. IAI arises due to closely spaced multiple antennas at each User Terminal (UT), whereas MUI is generated when one UT comes in the vicinity of another UT of the same cellular network. IAI can be mitigated by the use of a pre-coding scheme such as Singular Value Decomposition (SVD) and MUI can be cancelled through efficient Multi-user Detection (MUD) schemes. The highly complex and optimal Maximum Likelihood (ML) detector involves a large number of computations, especially when in massive structures. Therefore, the local search-based algorithm such as Likelihood Ascent Search (LAS) has been found to be a better alternative for mitigation of MUI, as it results in near optimal performance using lesser number of matrix computations. Most of the literature have been aimed at mitigating either IAI or MUI, whereas the proposed work presents SVD pre-coding and LAS MUD to mitigate both IAI and MUI. Simulation results indicate that the proposed scheme can attain near-optimal bit error rate (BER) performance with fewer computations.

New Equivalent Model of a Quantizer With Noisy Input and Its Applications for MIMO System Analysis and Design

We propose a novel equivalent model for a quantizer with noisy input (the desired signal corrupted by measurement noise). It presents the quantizer output as a sum of the desired signal after it passes through a nonlinear element with a known equivalent transfer function and an equivalent additive white noise. The equivalent transfer function takes the form of a conditional expectation of the quantizer output given the desired signal portion of its input. The proposed model proves to be effective for the analysis and design of MIMO systems employing low-resolution quantizers (analog to digital and digital to analog converters, ADCs and DACs, respectively). We also demonstrate the efficacy of the model through several example applications for 1) the design of digital dither that mitigates the effect of DAC quantization error in a MIMO transmitter and significantly reduces the DAC resolution requirement; 2) the determination of the minimal ADC resolution required for operation of conventional MIMO receivers designed for infinite-resolution ADC arrays, without incurring significant performance degradation; and 3) the design of simple MIMO receivers (ML and MMSE) that mitigate the effect of insufficient ADC resolution, thereby extending the receiver SNR operating range without an undue complexity increase.

Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems With Few-Bit ADCs

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a nonlinear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal designed specifically for the MIMO channel model under consideration.

Partially connected hybrid precoding design for millimeter wave MIMO systems

To solve the problem of spectral efficiency and energy efficiency of hybrid precoding in millimeter-wave communication system. This paper considers alternately using the minimization framework to increase beamforming gain. However, such framework because the radio frequency domain phase modulation network are using phase shifters, which result in non-convex constraints, and known solutions suffer from high computational complexity. Before using the alternate minimization architecture, it is proved that the phase angle of the ordered right singular vector of the channel matrix can be used to initialize the analog precoder, and hence, the complicated optimization procedures used to search the near-optimum analog precoding matrix can be avoided. In addition, the antenna array response vectors at the transmitter are not required. The simulation results show that the performance of the proposed algorithm is better than the traditional partial-connected algorithm and the complexity is lower, especially in the case of high signal-to-noise ratio.