Wave Massive MIMO Systems Research Articles

Low-complexity hybrid precoding for sub-connected millimeter wave massive MIMO systems

Hybrid analog-digital precoding architectures facilitate practical implementation of millimeter wave (mmW) massive multiple-input multiple-output (mmW-mMIMO) systems by reducing the number of radio frequency chains required. The spectral efficiency (SE) and energy efficiency (EE) optimization problems of these systems are typically non-convex and NP-hard, as the joint optimization that involves the analog and digital precoding functions along with the constant modulus constraints required by analog phase shifters, results in highly non-linear problems. To address such problems, we propose a decoupled two-stage design. More specifically, in the first stage, a closed-form approximation of the effective channel is proposed, and thus the SE maximization problem can be transformed to an effective channel gain maximization problem. Then, the analog precoding matrices can be generated to facilitate the second stage, where the digital precoding matrix is devised for maximizing the system’s EE. In addition, the circuit power consumption statistics of the system modules are exploited and a sub-connected mmW-mMIMO architecture is adopted during EE optimization. Finally, simulation results are provided to validate the effectiveness of our proposed mmW-mMIMO scheme with hybrid precoding.

Capacity Achieving Design for Hybrid Beamforming in Millimeter Wave Massive MIMO Systems

Capacity Achieving Design for Hybrid Beamforming in Millimeter Wave Massive MIMO Systems

Adaptive Jamming Attack Detection Under Noise Uncertainty in mmWave Massive MIMO Systems

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

Intelligent based hybrid precoder for millimetre wave massive MIMO system

Intelligent based hybrid precoder for millimetre wave massive MIMO system

Integrated Deep Implicit CSI Feedback and Beamforming Design for FDD mmWave Massive MIMO Systems

The heavy overhead of channel state information (CSI) feedback and the non-convexity of the hybrid beamforming (HYB) design pose great challenges for frequency-division-duplex (FDD) millimeter wave (mmWave) massive MIMO systems. In this letter, a deep HYB approach is proposed with limited and implicit CSI feedback. Specifically, we develop an autoencoder-based integrated deep implicit CSI feedback and beamforming neural network (CsiBFNet), which jointly conducts limited CSI feedback in the encoder(s) distributedly deployed on the user side and designs low-complexity beamformer in the decoder at the BS without explicit CSI reconstruction. The CsiBFNet aims to improve the achievable rate of the system rather than the CSI recovery accuracy. Simulations show that the proposed scheme outperforms the benchmark methods at the same feedback dimension and achieves 80% of the sum-rate of the existing state-of-the-art traditional HYB methods with perfect CSI even under severe compression rates (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu =32$ </tex-math></inline-formula> ).

Robust Gridless Estimation of Angles and Delays for Full-Dimensional Wideband mmWave Channels

In this paper, we consider robust channel estimation for a millimeter wave (mmWave) massive MIMO system with uniform planar arrays (UPA). For many gridless angle estimation methods of mmWave channels, the channel gains needs to be time-invariant during training. We propose a gridless method that is applicable to time-invariant and time-varying channels, and the proposed method is robust to channel variations. This is done by employing time-varying precoders and combiners that are banded block Toeplitz (BBT) matrices. The advantage of time-varying BBT precoders is two-fold. First, with time-varying BBT precoding we can fabricate time-varying channels. Thus the channel can be estimated as if it is a time-varying channel whether the actual channel is time-varying or not. We will show that, thanks to fabricated time variation in the channel, a small training overhead can be used. Secondly, BBT precoding and combining preserve the shift invariance property of the received data matrix. Due to the preservation of shift invariance property, we can employ most existing direction finding methods for estimating the angles and delays, including low complexity gridless subspace based algorithms. Simulation results are given to show that the proposed time-varying BBT precoding provides robust and accurate channel estimates with a low complexity.

An Effective Kalman Based Hybrid Beamforming for Millimeter Wave Massive MIMO System by Using 2D Overlapped Partially Connected Sub-Array Structure

In 5G mobile communication networks, millimeter-wave (mmWave) technology plays a key role. To deal with the problem of path loss that happens in the mmWave band, competent beamforming employing a large antenna array is required. Fully digital beamforming approaches currently require separate radio frequency chains (RF chains) for each antenna, which adds to the computational complexity and hardware expense. In a multi-user environment, fully digital beamforming suffers from a larger antenna array layout, whereas analog beamforming solutions are prone to numerous performance concerns. Hybrid beamforming, on the other hand, offers a promising solution for multi-user mmWave communication. This work proposes a two-dimensional overlapping partially connected (2D-OPC) sub-array structure to reduce the antenna structure's hardware complexity and cost. The suggested technique comprises several sub-arrays that are overlapped in 2D space in the form of a Uniform Planar Array (UPA). The suggested scheme's performance is assessed using Kalman-based hybrid beamforming, which exhibits a considerable increase in spectral efficiency when compared to existing hybrid beamforming techniques. The suggested technique, which uses Kalman based hybrid beamforming and 2D-OPC provides 3.14% and 4.96% improvement over the MMSE hybrid beamforming and ZF pre-coding technique respectively.

Compressive sensing-based channel estimation schemes for wideband millimeter wave wireless communication systems

The introduction of both hybrid precoder-combiner structure and low-bit resolution analog-to-digital converters (ADCs) makes channel estimation in millimeter (mm) Wave Massive MIMO systems to be challenging. In the literature, several channel estimation schemes presented focus on narrowband channels. However, few works are found to be presented for the case of wideband channels for this system. In this paper, two new channel estimation techniques are proposed with the first scheme based on the Projection-based Orthogonal Matching Pursuit (POMP) algorithm. The second estimation scheme is based on the Predictive Orthogonal Least Squares (POLS) algorithm. In all the simulation results, POLS shows the best performance but with the highest computational complexity cost, with POMB exhibiting the next best performance with a lower computational complexity cost. In terms of compromise between performance and computational complexity cost, the proposed POLS-based channel estimator is the best estimator for the wideband mmWave Massive MIMO systems.

Bayesian Matching Pursuit Based Estimation of Off-Grid Channel for Millimeter Wave Massive MIMO System

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity.

Low-Complexity Hybrid Precoding Based on PAST for Millimeter Wave Massive MIMO System

Low-Complexity Hybrid Precoding Based on PAST for Millimeter Wave Massive MIMO System

Spectral efficiency analysis of distributed millimeter wave massive MIMO system using spatial point processes

This paper considers a downlink distributed millimeter wave massive multi-input multi-output (D-mmMIMO) system, consisting of multiple randomly distributed radio access units (RAUs), the distribution of which is modeled as either a Poisson point process (PPP) or a Matérn hard-core point process (MHCPP). Compared with the PPP model where RAUs are randomly distributed without restriction, the MHCPP model constructed from a PPP by dependently thinning is more realistic, since it imposes a minimum distance between two RAUs so that they are not too close to each other. When a hybrid precoding algorithm based on antenna array response vectors is adopted, the upper bounds on average spectral efficiencies of the UEs for both the PPP D-mmMIMO system and MHCPP D-mmMIMO system are derived. Finally, simulation results are provided to demonstrate the validity of the developed analysis. The presented results also show that, when the MHCPP is generated from the PPP used in the PPP D-mmMIMO system, the MHCPP D-mmMIMO system with a low hard-core distance achieves a quite close performance to the PPP D-mmMIMO system, and increasing hard-core distance has a negative impact on spectral efficiency. In contrast, when the MHCPP is constructed from a different PPP, which system performs better depends on the intensities of two PPPs as well as hard core distance.

Hybrid precoding algorithm for massive MIMO system with imperfect channel knowledge

Millimeter wave MIMO system offers orders of magnitude high data rate due to the excess bandwidth available then conventional MIMO system. But, at the cost of excessive free space losses that highly influence the transmission at very high millimeter wave frequencies. The resulting small Wavelength at the millimeter wave frequency can overcome the path losses using beam forming gain offered by the massive antennas which is known as pre-coding. In millimeter wave massive MIMO system, hybrid pre-coding is the technique that combines analog pre-coding and digital pre-coding to reduce the requirement of high number of RF chains and thus energy consumption. Hybrid beam forming uses number of RF chains equal to the number of simulations users being served rather than using separate RF chains in the case of digital pre-coding. In designing analog and digital pre-coders, large number of calculations that uses complex mathematical process such as block diagonalization, SVD for channel matrix inversion required for the computation of pre-coder weight vector computation. In this paper, we propose low complexity hybrid pre-coding algorithm using simple linear approximation algorithms for matrix inversion operations. We first used the simple inversion algorithm in the computation of analog beam forming matrix without any SVD or direct inversion of matrices. Then, the same is being used in digital pre-coding matrix. In the proposed algorithm, there is no need for the complicated operations as SVD as in traditional pre coding algorithms.

Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL.

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

On the Complexity Reduction of Beam Selection Algorithms for Beamspace MIMO Systems

Beamspace multiple-input multiple-output (B-MIMO) provides a promising solution for reducing the number of required expensive radio frequency (RF) chains without obvious performance loss. However, the high computational complexities of beam selection algorithms prohibits their practical applications in 5G millimeter wave (mmWave) massive MIMO communication systems. Reviewing existing beam selection procedures and using the matrix perturbation theory, the complexity reduction on beam selection for beamspace MIMO is considered in this letter. Two beam selection algorithms, one for B-MIMO systems with classic zero-forcing (ZF) precoder and the other for the systems with QR precoder are proposed. Theoretical analyses and numerical simulations all show that the proposed beam selection algorithms possess a much lower complexity than conventional ones.

Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

Design Simulation and Performance Assessment of Improved Channel Estimation for Millimeter Wave Massive MIMO Systems

In this paper, we have optimize specificities with the use of massive MIMO in 5 G systems. Massive MIMO uses a large number, low cost and low power antennas at the base stations. These antennas provide benefit such as improved spectrum performance, which allows the base station to serve more users, reduced latency due to reduced fading power consumption and much more. By employing the lens antenna array, beam space MIMO can utilize beam selection to reduce the number of required RF chains in mm Wave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beam space channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. In this work we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beam space channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beam space channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beam space channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beam space channel can be exploited.

Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system

Further enhancement of 4G (or 4G-LTE advanced) spectral efficiency is possible only by allocating more bandwidth i.e., broadcasting on millimeter wave (mmWave) spectrum, by using the concept of small cells and also by increasing the number of spatial channels which is referred to as MIMO systems. However, recognizing the mmWave in MIMO remains a complex task that faces the issues like increased propagation loss. A new optimization based estimation algorithm is proposed to estimate the mm wave channel and to improve the performance of hybrid precoding. Azimuth angle is considered as objective function and is optimized without deviating from the actual characteristics. In this paper, Dragonfly Evaluated Grey Wolf Optimization (DE-GWO) model is introduced that hybridizes the concepts of DA and GWO respectively. The proposed algorithm is validated by comparing with Deer Hunting Optimization Algorithm (DHOA), Lion Algorithm (LA) and LDHOA (combination of LA and DHOA). Finally, the performance of proposed work is compared and validated over other state-of-the-art models with respect to CSI and error measures.

Low-Complexity Hybrid beamforming with Low Resolution phase shifter in Millimeter Wave Massive MIMO Systems

Low-Complexity Hybrid beamforming with Low Resolution phase shifter in Millimeter Wave Massive MIMO Systems

Channel estimation by reduced dimension decomposition for millimeter wave massive MIMO system

In this paper, a channel estimation method based on reduced dimension decomposition for millimeter-wave massive MIMO systems is proposed. For the sake of obtaining the high accuracy of the estimation, we decompose the channel matrix estimation into angle information and channel gain information estimation. The received signals are decomposed by dimensionality reduction so that the angles of the receiving end and transmitting end are separated. The sparse signal recover (SSR) scheme is used to acquire the initial sparse support set. Then, the off-grid error is regarded as the adjustment parameter. Using the orthogonal relationship between the signal subspace and the noise subspace, we gradually approximate the true discrete grid using Taylor’s formula. Finally, the path gain is estimated by the least squares estimation (LSE) algorithm. The benefit of the proposed method is the reduction of the training resources and costs. More importantly, to verify the estimated performance, we obtain the normalized mean square error (NMSE) performance of the channel matrix estimation and the achievable spectral efficiency (ASE) performance under the estimation scheme. Simulation results indicate that the proposed method has the effectiveness and superiority.

BD-UCD-Based Nonlinear Hybrid Precoding for Millimeter Wave Massive Multiuser MIMO Systems

In this letter, we investigate the downlink hybrid precoding design for time-division duplexing (TDD) millimeter wave (mmWave) massive multiuser MIMO (MU-MIMO) systems, aiming to enhance both the sum spectral efficiency (SSE) and the bit error rate (BER) performance. First, we propose a joint analog precoding and combining design via Gram-Schmidt orthogonalization to maximize the beamforming gain while mitigating the inter-user interference. Then, by exploiting the uplink-downlink duality, we propose a block diagonal uniform channel decomposition (BD-UCD) based digital precoding and combining scheme to further improve the SSE and BER performance. Furthermore, Tomlinson–Harashima precoding (THP) is utilized to eliminate both the inter-user interference and the inter-stream interference. Simulation results demonstrate that the proposed BD-UCD-based nonlinear hybrid precoding scheme performs close to the fully-digital BD-UCD scheme and outperforms the other existing hybrid precoding schemes in terms of SSE and BER performance.