mmWave Massive Massive Multiple-input Multiple-output Systems Research Articles

Beam Tracking for Distributed Millimeter-Wave Massive MIMO Systems Based on the Unscented Kalman Filter

In this letter, a beam tracking problem is studied in distributed millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) systems. Firstly, a monopulse beam direction estimation method for a distributed mm-wave massive MIMO system is introduced. However, this method is very sensitive to noise and must be performed in every time slot in a single-user moving scene. To improve its accuracy, a monopulse beam tracking method based on the unscented Kalman filter (UKF) for a distributed mm-wave massive MIMO system is proposed. The simulation results show that the proposed beam tracking method has better tracking performance under different SNRs and different numbers of antennas of remote antenna units (RAUs), and its robustness and generalizability are quite well.

An Efficient Machine Learning Based Precoding Algorithm for Millimeter-Wave Massive MIMO

Millimeter wave communication works in the 30–300 GHz frequency range, and can obtain a very high bandwidth, which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation (5G). The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture. The resulting array gain can compensate for the path loss of the millimeter wave. Utilizing this feature, the millimeter wave massive multiple-input multiple-output (MIMO) system uses a large antenna array at the base station. It enables the transmission of multiple data streams, making the system have a higher data transmission rate. In the millimeter wave massive MIMO system, the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end, and the receiving end performs equalization, so that users can better obtain the antenna multiplexing gain and improve the system capacity. This paper proposes an efficient algorithm based on machine learning (ML) for effective system performance in mmwave massive MIMO systems. The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna. Simulation results show that, the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.

Performance and User Association Optimization for UAV Relay-Assisted mm-Wave Massive MIMO Systems

Unmanned aerial vehicle (UAV) relaying is deemed as a promising solution to enhance the achievable rate and widespread connectivity in millimeter-Wave (mm-Wave) systems for tomorrow’s 6G wireless networks. In this paper, we study both the performance and user association optimization for the UAV relay-assisted mm-Wave massive multiple-input multiple-output (MIMO) communication system, where multiple base stations (BSs) serve their respective users with the help of one beamforming UAV relay. Both the beamforming and the UAV relay have essential impact on the achievable sum-rate of the system. Thus, a multi-user hybrid beamforming scheme is designed to mitigate the inter-user interference issues and achieve a better trade-off between performance and complexity in UAV-enabled communications. Also, to exploit UAV relay based architecture in serving different ground BS-user pairs, we propose a UAV relay-assisted multi-BS mm-Wave massive MIMO system with hybrid beamforming architecture, which prevent sudden link disconnections caused by high path loss and line-of-sight (LoS) blockage in mm-Wave frequency band. Then, we formulate a user association problem with multiple constraints so that the sumrate of the overall UAV relay-assisted mm-Wave massive MIMO system is maximized. Simulation results are provided to show the effectiveness of the proposed UAV relay-enabled architecture.

Tensor Dictionary Manifold Learning for Channel Estimation and Interference Elimination of Multi-User Millimeter-Wave Massive MIMO Systems

Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.

Spectrum and energy efficient kalman‐based hybrid combiner for mmWave massive MIMO systems

The hybrid beamforming can significantly improve the spectral and energy efficiencies of the millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems without obvious performance loss. However, the fundamental limitation of existing hybrid beamforming is that they include high-resolution analog-to-digital converters (ADCs) which consume high power. To overcome this fundamental limitation, in this paper, spectrum and energy efficient mmWave massive MIMO transmission scheme is proposed that jointly optimizes the hybrid combiner and the number of ADC bits. Particularly, the hybrid combiner is presented as a combination of three parts: the analog combiner matrix, the quantization matrix, and the digital baseband matrix which is based on the Kalman filter. Unlike most existing works that rely on sumrate optimization to design the hybrid combiner, in this work, the mean square error (MSE) is chosen as the main performance metric to describe the reliability of the system. Simulations results confirm that the proposed Kalman based hybrid combiner exhibits the best performance in terms of spectral and energy efficiencies compared to minimum mean square error (MMSE) and zero forcing (ZF) receivers.

PARAFAC-Based Multiuser Channel Parameter Estimation for MmWave Massive MIMO Systems over Frequency Selective Fading Channels

Channel estimation is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially with a few training sequences. To solve the problem of uplink channel estimation in mmWave massive MIMO systems, a PARAFAC-based algorithm is proposed for joint estimation of multiuser channels. The orthogonal frequency divisional multiplexing (OFDM) technique is exploited to combat the frequency selective fading channels. In this paper, the received signal at the base station (BS) is formulated as a third-order parallel factor (PARAFAC) tensor, and then a low-complexity algorithm is designed for fast estimation of the factor matrices related to channel parameters, thus leading to joint estimation of multiuser channel parameters via one-dimensional search. Moreover, the Cramér–Rao Bound (CRB) results for multiuser channel parameters are derived for evaluation. Theorical analysis and numerical results reveal that the algorithm performs well with a few training sequences. Compared with existing algorithms, the proposed algorithm has clear advantages both in estimation accuracy and computational complexity.

Iteratively reweighted super-resolution channel estimation in hardware-impaired hybrid-precoded MM-wave massive MIMO systems using dual SVD and Marquardt's global search

In this paper, a three-dimensional geometric channel model characterized by channel gain, angle of arrival, and departure (AoA/AoD) is used to develop a non-ideal and more realistic system representation that accounts for additional signal perturbations other than additive white Gaussian noise (AWGN). Hinged on this, compressive sensing (CS) based channel estimation technique is proposed for millimeter-wave (MM-Wave) massive multiple-input multiple-output (MIMO) systems with inherent challenges due to hardware impairment (HI). The proposed estimator named dual singular value decomposition (SVD) and Marquardt, abbreviated as DSM estimator aims at tolerating complexity and improving channel estimation accuracy at reasonable trade-offs in the system. The normalized mean square error (NMSE) performance of the proposed channel estimation scheme, with modified Marquardt's global minimum search, achieves higher accuracy at below 0 dB (LoS)/-3 dB (NLoS) signal-to-noise ratio (SNR) for uniform linear array (ULA) configuration and performs better for uniform planar array (UPA) configuration. A low-rank range of unit difference is adopted for matrix decomposition twice (dual SVD) per SNR in ULA/UPA setting. The proposed estimator's result shows early convergence during the simulation and a linearly scaled complexity in comparison with one of the earlier proposed schemes in literature in particular the super-resolution channel estimation with gradient descent optimization.

Multi-user hybrid precoding for mmWave massive MIMO systems with sub-connected structure

Hybrid precoding achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior works on multi-user hybrid precoding only consider the full-connected structure. In this paper, a novel multi-user hybrid precoding algorithm is proposed for the sub-connected structure. Based on the improved successive interference cancellation (SIC), the analog precoding matrix optimization problem is decomposed into multiple analog precoding sub-matrix optimization problems. Further, a near-optimal analog precoder is designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, digital precoding is designed according to the block diagonalization (BD) technology. Finally, the water-filling power allocation method is used to further improve the communication quality. The extensive simulation results demonstrate that the sum rate of the proposed algorithm is higher than the existing hybrid precoding methods with the sub-connected structure, and has higher energy efficiency compared with existing approaches. Moreover, the proposed algorithm is closer to the state-of-the-art optimization approach with the full-connected structure. In addition, the simulation results also verify the effectiveness of the proposed hybrid precoding design of the uniform planar array (UPA).

An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix

<p>The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.</p>

SMUL-FFT: A Streaming Multiplierless Fast Fourier Transform

Beamspace processing is an emerging paradigm to reduce hardware complexity in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. This approach exploits sparsity of mmWave channels but requires spatial discrete Fourier transforms (DFTs) across the antenna array, which must be performed at the baseband sampling rate. To mitigate the resulting DFT hardware implementation bottleneck, we propose a fully-unrolled Streaming MUltiplierLess (SMUL) fast Fourier Transform (FFT) engine that performs one transform per clock cycle. The proposed SMUL-FFT architecture avoids hardware multipliers by restricting the twiddle factors to a sum-of-powers-of-two, resulting in substantial power and area savings. Compared to state-of-the-art FFTs, our SMUL-FFT ASIC designs in 65nm CMOS demonstrate more than 45% and 17% improvements in energy-efficiency and area-efficiency, respectively, without noticeably increasing the error-rate in mmWave massive MIMO systems.

Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Spatial Wideband Channel Estimation for mmWave Massive MIMO Systems With Hybrid Architectures and Low-Resolution ADCs

In this article, a channel estimator for wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid architectures and low-resolution analog-to-digital converters (ADCs) is proposed. To account for the propagation delay across the antenna array, which cannot be neglected in wideband mmWave massive MIMO systems, the discrete time channel that models the spatial wideband effect is developed. Also, the training signal design that addresses inter-frame, inter-user, and inter-symbol interferences is investigated when the spatial wideband effect is not negligible. To estimate the channel parameters over the continuum based on the maximum a posteriori (MAP) criterion, the Newtonized fully corrective forward greedy selection-cross validation-based (NFCFGS-CV-based) channel estimator is proposed. NFCFGS-CV is a gridless compressed sensing (CS) algorithm, whose termination condition is determined by the CV technique. The CV-based termination condition is proved to achieve the minimum squared error (SE). The simulation results show that NFCFGS-CV outperforms state-of-the-art on-grid CS-based channel estimators.

Principle of Computation Power Optimization in Millimeter Wave Massive MIMO Systems

The computation power of baseband units (BBUs) is a major source of power consumption in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with a large number of users due to complex signal processing. The effective reduction of computation power is critical for improving system energy efficiency. In this paper, the principle of reducing the computation power of BBUs is first investigated in mmWave massive MIMO systems with a hybrid precoding structure. A recursive constraint in decomposing the baseband precoding matrix is derived for reducing the computation power of hybrid precoding systems. Furthermore, the optimal number of sub-matrices minimizing the maximum error in decomposing the baseband precoding matrix is obtained. Based on the proposed principle, consisting of the recursive constraint and the optimal number of sub-matrices, a fast Monte Carlo baseband precoding (FMCBP) algorithm is developed to reduce the computation power of BBUs and improve system energy efficiency. Simulation results show that the total transmission rate and energy efficiency of mmWave systems are coupled with the computation power of BBUs, based on the FMCBP algorithm. Moreover, the FMCBP algorithm maximally improves the energy efficiency of multi-user mmWave massive MIMO communication systems by 124 percent, compared with the conventional equivalent zero-forcing algorithm.

Full-Duplex mmWave Massive MIMO Systems: A Joint Hybrid Precoding/Combining and Self-Interference Cancellation Design

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as one of the primary candidates for the fifth generation (5G) and beyond 5G wireless communication networks to satisfy the ever-increasing capacity demands. Full-duplex technology can further enhance the advantages of mmWave massive MIMO systems. However, strong self-interference (SI) is the major limiting factor in full-duplex technology. Hence, this paper proposes a novel angular-based joint hybrid precoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO systems. Our primary goals are listed as: (i) improving the self-interference cancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing the channel estimation overhead, (iv) designing the massive MIMO systems with a low number of RF chains. First, the RF-stage of AB-JHPC is developed via slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A joint transmit/receive RF beamformer design is proposed for covering (excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage of AB-JHPC is constructed via the reduced-size effective intended channel. After using the well-known singular value decomposition(SVD) approach at the BB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE) technique to further suppress the residual SI power by using AoD/AoA parameters. The numerical results demonstrate that the SI signal is remarkably canceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves 85.7 dB SIC and the total amount of SIC almost linearly increases via antenna isolation techniques. We observe that the proposed full-duplex mmWave massive MIMO systems double the achievable rate capacity compared to its half-duplex counterpart as the antenna array size increases and the transmit/receive antenna isolation improves.

Dynamic ADC Resolution-Mapping for Millimeter-Wave Massive MIMO Systems

The radio-frequency (RF) chains, phase shifters (PSs), and analog-to-digital converters (ADCs) play the dominant role of power consumption in the uplink hybrid millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) networks. To mitigate power consumption, an energy-efficient switch and inverter (SI) based partially-connected (PC) architecture with the Gram-Schmidt (GS) antenna selection strategy is addressed. The design of a variable-resolution ADC configuration is addressed under an independent upper bound of power consumption for each ADC in this paper. However, the resulting ADC resolution mapping becomes more complicated due to variant ADC power bounds. A simple ADC bit-allocation algorithm, namely, the sum-resolution (SR) ADC, is proposed. By replacing the total power constraint on ADCs to improve both the achievable sum-rate (ASR) and energy efficiency (EE) performance of the hybrid mmWave massive MIMO system. The SR-ADC solutions in closed form reveal that the optimal ADC resolution is proportional to the square power of the signal-to-noise ratio (SNR) in RF chains. Simulation results demonstrate that the proposed SR-ADC approach offers enhanced improvements on the ASR and EE, and exhibits prominent advantages on the number of activated RF chains compared with the fixed total power system.

Dynamically Subarray‐Connected Hybrid Precoding Scheme for Multiuser Millimeter‐Wave Massive MIMO Systems

Hybrid precoding is widely used in millimeter wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems. However, most prior work on hybrid precoding focused on the fully connected hybrid architectures and the subconnected but fixed architectures in which each radio frequency (RF) chain is connected to a specific subset of the antennas. The limited work shows that dynamic subarray architectures address the tradeoff between achievable spectral efficiency and energy efficiency of mmWave massive MIMO systems. Nevertheless, in the multiuser hybrid precoding systems, the existing dynamic subarray schemes ignore the fairness of users and the problem of user selection. In this paper, we propose a novel multiuser hybrid precoding scheme for dynamic subarray architectures. Firstly, we select a multiuser set among all users according to the analog effective channel information of the base station (BS) and then design the subset of the antennas to each RF by the fairness antenna‐partitioning algorithm. Finally, the optimal analog precoding vector is designed according to each subarray, and the digital precoding is designed by the minimum mean‐squared error (MMSE) criterion. The simulation results show that the performance advantages of the proposed multiuser hybrid precoding scheme for dynamic subarray architectures.

Tensor-Based Joint Channel Estimation and Symbol Detection for Time-Varying mmWave Massive MIMO Systems

In this paper, a tensor-based joint channel parameter estimation and information symbol detection scheme is developed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems. At the base station (BS), the information symbols are encoded according to the Khatri-Rao space-time (KRST) method and transmitted through time-varying channels. The received signals at the mobile station (MS) are constructed into a nested complex-valued parallel factor (PARAFAC) model, which contains an outer model and an inner model, respectively. With outer model, we estimate the compound channel matrix and detect the information symbols considering the sparse scattering nature of mmWave channels. With inner model, we extract physical parameters, including angles of arrival/departure (AoAs/AoDs), Doppler shifts and complex path gains from the estimated compound channel matrix. These physical parameters can be used to significantly reduce feedback overhead. A tricky way here is that we convert complex inner-submodel into a real one such that the computational complexity is reduced. Compared with existing schemes, the proposed one improves the estimation accuracy with low computational complexity, and is applicable for both uniform linear arrays (ULAs) and uniform planar arrays (UPAs). Simulation results demonstrate the effectiveness of the proposed joint channel estimation and information symbol detection scheme.

Dual-Iterative Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Systems With Sub-Connected Structure

The performance of the hybrid beamforming system based on the full-connected structure is close to that of the full-digital one in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, due to the high complexity of circuit wiring in practical applications, it has been greatly restricted. This paper investigates the hybrid (analog/digital) beamforming design of a multiuser mmWave massive MIMO system with sub-connected structure. For this system, we focus on a sum-rate maximization probelm. Considering the joint design of the receiver, and transmitter, a two-stage design method is adopted. In the analog stage, the analog beamformer, and combiner are designed by the proposed piecewise dual joint iterative approximation (PDJIA) method, which not only can obtain closed-form solutions but also has linear property. Based on the criterion of avoiding the loss of information, the problem of digital beamforming is solved by exploiting the baseband piecewise successive approximation (BPSA) method, which can effectively increase the number of users served. Simulation results show that the proposed scheme outperforms the state-of-the art MIMO hybrid beamforming design schemes, and more accurately approximates to that of the full-digital system.

Near‐optimal hybrid precoding for millimeter wave massive MIMO systems via cost‐efficient Sub‐connected structure

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is a promising technology for next generation wireless communications. The utilisation of traditional full digital precoding techniques for mmWave massive MIMO systems is too costly. Fortunately, the hybrid (analogue/digital) precoding is a better choice. In this study, the authors investigate a near-optimal iterative approximation hybrid precoding algorithm via cost-efficient sub-connected structure for mmWave massive MIMO systems. Based on the minimum mean square error (MSE) criterion, the optimal design of the proposed hybrid precoders is non-convex. The original problem is reformulated as two optimisation sub-problems, where one of the sub-problems is convex and another is non-convex. First, the authors convert the non-convex sub-problem into a convex problem by relaxing the constant modulus constraint from beampattern with interference control method, and eliminating the block diagonal constraint from vector operation with the properties of Kronecker product. Then the two convex sub–problems are solved iteratively. The proposed algorithm can achieve a highly approximate optimal solution, which can be theoretically demonstrated to converge to a Karush-Kuhn-Tucker point of the original problem. Simulation results show that the proposed algorithm via both sub–connected and fully connected structure gets favourable performance in terms of MSE and achievable rate.

Low Complexity Angular-Domain Detection for the Uplink of Multi-User mmWave Massive MIMO Systems

To compromise between the system performance and hardware cost, millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been regarded as an enabling technology for the fifth generation of mobile communication systems (5G). This paper considers a low-complexity angular-domain compressing based detection (ACD) for uplink multi-user mmWave massive MIMO systems, which involves hybrid analog and digital processing. In analog processing, we perform angular-domain compression on the received signal by exploiting the sparsity of the mmWave channel to reduce the dimension of the signal space. In digital processing, the proposed ACD scheme works well with zero forcing (ZF)/maximum ratio combining (MRC)/minimum mean square error (MMSE) detection schemes. The performance analysis of the proposed ACD scheme is provided in terms of achievable rates, energy efficiency and computational complexity. Simulations are carried out and it shows that compared with existing works, the proposed ACD scheme not only reduces the computational complexity by more than 50 % , but also improves the system’s achievable rates and energy efficiency.