Multi-user Massive Multiple-input Multiple-output Systems Research Articles

Interference Cancellation Aided Hybrid Beamforming for mmWave Multi-User Massive MIMO Systems

In large scale multiple-input multiple-output (MIMO) systems (or massive MIMO systems), hybrid beamforming is a promising technique due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. In this paper, we investigate the downlink millimeter wave (mmWave) multi-user massive MIMO system and propose an interference cancellation (IC) framework on hybrid beamforming design. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Specifically, for the first proposed algorithm, we use zero-forcing (ZF) to cancel inter-user interference and use SIC to cancel intra-user interference. For the second one, SIC is used to cancel inter-user interference and ZF is used to cancel intra-user interference. Both inter-user interference and intra-user interference are suppressed by SIC in the third algorithm. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed SIC-aided hybrid beamforming algorithms outperforms the existing approaches in terms of spectral efficiency (SE) at the cost of computational complexity for the SIC procedure. Moreover, the results indicate that the proposed algorithms can achieve good SE performance with 2-bit finite resolution phase shifters and channel estimation error.

Performance analysis of uplink massive spatial modulation MIMO systems in transmit-correlated Rayleigh channels

In this paper, the performance of uplink multiuser massive multiple-input multiple-output (MIMO) system with spatial modulation over transmit-correlated Rayleigh fading channel is investigated, where a large number of antennas are deployed at the base station and linear zero-forcing (ZF) receiveris employed for detection. By taking the transmit correlation and the randomness of shadow fading in to account, the bit error rate (BER) performance of the system is analyzed. According to the performance analysis, an approximated expression of overall average BER of the system is attained. Besides, asymptotic performance is studied and the corresponding BER expression at high signal-to-noise ratio is derived. On this basis, the diversity gain of the system can be obtained for performance evaluation. Simulation results show that the derived theoretical expressions matchthe simulated values well, which verifies the correctness of our analysis.

Coverage Analysis of Physical Layer Network Coding in Massive MIMO Systems

Wireless networks are prone to interference due to their broadcast nature. In the design of most of the traditional networks, this broadcast nature is perceived as a performance-degrading factor. However, Physical Layer Network Coding (PNC) exploits this broadcast nature by enabling simultaneous transmissions from different sources and thereby enhances the performance of the wireless networks with respect to improvement in spectral efficiency, coverage, latency and security of the system. For fifth generation (5G) networks and beyond, massive multiple input multiple output (MIMO) is considered as a key physical layer technology. Thus, its combination with PNC can significantly enhance the performance of the network, facilitating capacity-coverage improvement, among other benefits. While the bit error rate performance of multiuser massive MIMO-PNC systems through linear detection has been investigated extensively, their coverage probability for a given target signal-to-noise ratio has not been explored yet. In this paper, we derive a closed form expression for coverage probability in PNC based multiuser massive MIMO systems employing zero-forcing equalization. Both theoretical and simulation results are provided for different number of users and antennas in the multiuser massive MIMO-PNC communications systems.

User grouping based tilt optimization for single-cell multi-user massive MIMO systems

In this paper, we study the tilt optimization method in a single-cell multi-user massive multiple-input multiple-output (MIMO) system. We use the asymptotic channel orthogonality to derive the asymptotic sum rates in the uplink and the downlink, respectively. Based on the correlation property of the array steering vectors, the users are grouped based on the elevation direction-of-arrivals. Then, the optimal tilt is obtained by comparing the sum rates of the groups. Simulation results show that the proposed optimization method is effective for improving cell sum rates.

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.

Low-Latency Lattice-Reduction-Aided One-Bit Precoding Processor for 64-QAM 4×64 MU–MIMO Systems

Massive multi-user multiple-input multiple-output (MU-MIMO) communication is a crucial technique for next-generation wireless systems because it enables high-throughput and ultra-reliable data transmission. However, high power consumption is a challenging problem in conventional transceiver designs, where each RF chain at the transmitter requires a pair of high-resolution digital-to-analog converters (DACs), which are the primary power sources in front-end circuits. Therefore, quantized precoding algorithms were recently proposed to address this issue. They used low-resolution DACs at the transmitter and compensated for the distortion caused by the quantization effect at the baseband. This paper proposes a constellation-range gain-controlled lattice-reduction-aided (CR-GCLRA) one-bit precoding algorithm for massive MU-MIMO systems with high-order quadrature amplitude modulation (QAM) signaling. Lattice reduction (LR) preprocessing is adopted for performance enhancement. A novel gain-control mechanism is proposed to address the constellation range expansion problem in conventional LR preprocessing. We designed and implemented a CR-GCLRA one-bit precoding processor by using TSMC 40-nm CMOS technology to accelerate the one-bit precoding processing for 64-QAM 4 ×64 MU-MIMO system. The proposed one-bit precoding processor can achieve a throughput of 43.92 Mbits per second at a clock speed of 269 MHz with 271 mW power consumption and 0.51μs latency.

Constructive Interference-Based Symbol-Level Precoding Design for Millimeter-Wave Massive Multiuser MIMO Systems With Hardware-Efficient Hybrid Precoding Architecture

This study considers the constructive interference-based symbol-level precoding design problem in millimeter-wave massive multiuser multiple-input multiple-output systems with hybrid analog-and-digital precoding architecture. In contrast to traditional hybrid precoding architecture in which the analog part is implemented by infinite-resolution phase shifters, analog precoders are achieved in this study using low-cost and energy-efficient switches and inverters. Our objective is to design a hybrid precoder that minimizes the Euclidean distance between the hybrid precoder and the optimal fully digital precoder. However, such a design problem is difficult due to the intricate coupling of digital and analog precoder variables. Thus, we apply the alternating minimization (AltMin) framework to decouple the considered problem into digital and analog precoder design problems and then solve for digital and analog precoders in an alternating manner. By adopting the AltMin framework, we obtain a closed-form solution for digital precoder. Nevertheless, solving the analog precoder design problem incurs combinatorial complexity, which we address by proposing a coordinate descent algorithm to sequentially update each element of the analog precoder with a closed-form expression. Simulation results reveal that the application of the proposed algorithm for the proposed hardware-efficient hybrid precoding architecture provides the desired balance among symbol error rate performance, power consumption, and hardware cost.

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing.

Effect of Imperfect Artificial Noise on the Performance of Physical Layer Security for MU-Massive MIMO System

Physical Layer Security (PLS) in Multiple Input Multiple Output (MIMO) systems is crucial to achieving reliable communication. Artificial Noise (AN) is a commonly employed technique to provide PLS-based secure communication. This requires perfect knowledge of the channel between transmitter (Alice) and legitimate user (Bob). However, this channel information may be imperfect due to estimation errors. Thus, the claimed performance of AN-based PLS is questionable and requires thorough investigation. In this work, the effect of imperfect channel estimation on the performance of PLS for multiuser Massive (MU-Massive) MIMO system is investigated. The channel estimation error is modeled as a complex Gaussian variable. The performance measure in terms of Bit-error-rate (BER) is evaluated for different levels of channel estimation error. This investigation provides a good illustration of how the performance of AN-based PLS is affected by the channel estimation error in the MU-Massive MIMO system. This work shows the trend in BER increase with the increase in AN mismatch.

Improved Hybrid Beamforming for mmWave Multi-User Massive MIMO

Massive multiple input multiple output (MIMO) has become essential for the increase of capacity as the millimeter-wave (mmWave) communication is considered. Also, hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency (RF) chains. This paper proposes a hybrid beamforming scheme to improve the spectral efficiency for multi-user MIMO (MU-MIMO) systems. In a frequency selective fading environment, hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers. To mitigate performance degradation, this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques. The analog precoder is iteratively updated for each column until it converges. The proposed scheme can reduce errors in the approximating process of the overall spectral efficiency. This scheme can be applied to fully-connected and partially-connected structures. The simulation results show that spectral efficiency performance for the proposed scheme is better than the conventional schemes. The proposed scheme can achieve similar performance with full-digital beamforming by using a sufficiently large number of RF chains. Also, this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment. This performance improvement can be achieved in both structures.

On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems

In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent multiple-symbol detection for differential phase-shift keying (DPSK) multi-user massive MIMO systems is proposed. The proposed deep-learning implementation reduces the high computational complexity required in conventional DPSK detection techniques. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with decision-feedback differential detection (DFDD), and conventional multiple-symbol differential detection (MSDD) for the same system parameters. Multiple-symbol differential sphere detection (MSDSD) is used to implement conventional MSDD. The results show that the proposed deep-learning-based multiple-symbol differential detection implementation outperforms decision-feedback differential detection and achieves an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection for single-user and multi-user scenarios.

Heuristic Search Inspired Beam Selection Algorithms for mmWave MU-MIMO System With Discrete Lens Array

By employing beam selection in beamspace, the costs of hardware and power consumption in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system can be significantly reduced without obvious performance loss. In most existing schemes, there is a limitation that the number of beams (radio frequency (RF) chains) and users must be equal, which leads to degradation of system performance and flexibility. To overcome this limitation, we formulate beam selection as a discrete combinational optimization problem of binary selection vector to maximize the spectral efficiency of multi-user MIMO (MU-MIMO) system. In order to solve this problem, we propose enhanced CE (ECE)-based and GA-based algorithms inspired by cross entropy (CE) method and genetic algorithm (GA) in heuristic search. Moreover, we analyze the complexity of proposed algorithms and provide a graphical representation of convergence property for ECE-based algorithm. Simulation results demonstrate that the proposed algorithms are able to achieve good performance with variable number of beams, and verify the validity of graphical representation of convergence property for ECE-based algorithm.

Fast Beam Training for FDD Multi-User Massive MIMO Systems With Finite Phase Shifter Resolution

A fast beam training scheme for the massive antenna arrays is a key to frequency division duplex (FDD) mmWave systems, as channel reciprocity between the down-link (DL) and up-link (UL) channels does not hold in general, requiring feedback mechanism for DL beam selection. Due to the large number of antennas in a massive array, it is not very practical to conduct an exhaustive search, especially when considering the small angular coverage of one directional narrow beam (hence the number of candidate beams). To address such a challenge, we consider the 3D beam matching problem for a multi-user massive multiple-input multiple-output (MIMO) system, and propose a two-stage hierachical codebook along with the corresponding fast beam training scheme. The proposed codebook contains a primary codebook for hierarchical beams and an auxiliary codebook for narrow beams following the limited resolution of the phase shifters (PSs). The fast beam training scheme based on the codebook not only reduces beam training overhead, but also is applicable to the scenario where there exist multiple propagation paths for one mobile station (MS). Numerical results show that the proposed scheme not only enjoys a lower beam matching complexity (i.e. the training overhead), but also achieves a comparable performance to the exhaustive search scheme.

Sparse Vector Coding Aided Ultra-Reliable and Low-Latency Communications in Multi-User Massive MIMO Systems

Ultra-reliable and low-latency communication (URLLC) has been recognized as a key service to support delay-sensitive applications for next generation wireless systems. A critical challenge is how to support multiple users with ultra reliability in the short packet transmission. In this paper, we propose a sparse vector coding (SVC)-aided URLLC multi-user transmission scheme by employing the massive multiple-input multiple-output (MIMO) technique. To reliably acquire the support information, we formulate the SVC decoding problem in multi-user massive MIMO systems as the problem to find out nonzero positions of a sparse vector. Then, the support recovery problem is transformed into a series of sub-problems by decoupling the support identification of each user, where the successive interference cancellation based matching pursuit (SIC-MP) algorithm is proposed to estimate the support sequentially. In addition, the user sorting scheme is proposed to alleviate the inter-user interference in the SVC decoding. The complexity of the proposed SIC-MP algorithm is only linear with the system parameters.

Investigation of low-density parity check codes concatenated multi-user massive multiple-input multiple-output systems with imperfect channel state information

In this era of communication technology, it is desirable to increase the data rate while minimizing the error to improve the system’s reliability. One of these techniques is massive multiple-input multiple-output (mMIMO), which increases the spectral efficiency by providing the data to multiple users simultaneously through spatial multiplexing. The mMIMO system processes the received signal by prior estimation of the channel, which has a finite variance leading to imperfect channel state information (ICSI) at the receiver. In the fifth-generation technology, spectral efficiency using mMIMO may decrease as the number of subscribers increases, resulting in more interference and affecting system capacity. The ICSI provides another challenge, as the processed data at the receiver’s output may now be more erroneous. Thus, this article provides an insight into the impact of an increase in the number of users on the variation in bit error rate with the signal-to-noise ratio in multi-user mMIMO (MU-mMIMO) and low-density parity check (LDPC) codes concatenated MU-mMIMO systems having ICSI at the receiver for quadrature amplitude modulation (QAM) and 16-QAM as modulation techniques. It has been shown that the performance of the concatenated scheme outperforms the conventional mMIMO system.

Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO

Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in an RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.

Low-complexity soft-output signal detector for massive MIMO with higher order QAM constellations

As the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems is dramatically increased, linear detection methods are able to achieve the near optimal performance at the expense of performing the complicated matrix inversion of a high dimensional matrix, which makes hardware implementation infeasible. Meanwhile, the straightforward application of the hard symbol-to-bit decision algorithm yields a very poor performance for the higher order quadrature amplitude modulation (QAM) that is widely adopted as an efficient tool to accommodate high demand of data throughput in modern wireless systems. In this work, we propose a low-complexity soft-output signal detector for multiuser massive MIMO communication systems employing Gray-coded higher order QAM with square constellations. In the proposed detector, the two-dimensional double successive projection (2D-DSP) algorithm is utilized to iteratively realize zero-forcing (ZF) algorithm for multiuser signal recovery, which circumvents the matrix inverse operation. Moreover, a simplified implementation of Max-Log-MAP algorithm is developed to calculate log likelihood ratios (LLRs) for soft-input channel decoding by fully exploiting the bit-flipping property of Gray-coded modulation scheme along with the utilization of the channel hardening property of the massive MIMO systems. Numerical results show that the proposed soft-output detector provides a relatively good tradeoff between the complexity and performance compared with several existing detectors, and approaches the performance of the ZF algorithm with only 3 iterations.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station (BS) is equipped with a very large number of antennas (also referred to as “massive multiple-input multiple-output (MIMO)”). We consider a time-division duplexing (TDD) scheme, in which reciprocity between the uplink and downlink channels can be assumed. Channel estimation is essential for downlink beamforming in massive MIMO, nevertheless, the pilot contamination effect hinders accurate channel estimation, which leads to overall performance degradation. Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals (AOAs) in the large-scale antenna system, we propose a multiple signals classification (MUSIC) based channel estimation algorithm during the uplink transmission. Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

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.

User Grouping and Hybrid RF/Baseband Precoding for Multi-User Massive MIMO Systems

In this paper, we investigate hybrid precoders for massive MIMO systems with the objective to reduce the number of radio-frequency (RF) chains. The users are first partitioned into groups that share similar angles of departure (AoDs) to design an angular-based RF-beamforming stage. Then a multi-user-Multiple Input Multiple Output (MIMO) baseband precoding stage is developed with reduced channel state information (CSI) dimension in massive MIMO systems. We develop a systematic approach to simultaneously determine the number of groups and to cluster the users accordingly. The partition is done by minimizing the inter-group interference using a similarity graph. We also incorporate a simple transfer block between the digital precoding and RF-beamforming stages to reduce the number of RF chains in massive MIMO systems. For a sufficiently large number of antennas and over a wide range of signal-to-noise ratio (SNR), the proposed hybrid RF/baseband precoding schemes can offer a sum-rate performance approaching that of the single-stage full digital optimal precoder, while keeping the number of RF chains equal to the number of users. Next, we propose a simple way to design the RF and baseband digital precoding stages in the case of multiple scatterings to fully exploit the multiple reflections that occur in dense urban environments. The proposed hybrid 2-stage RF/baseband schemes significantly outperform other hybrid RF/baseband precoding schemes using orthogonal multiplexing.