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

Adaptable Hybrid Beamforming with Subset Optimization Algorithm for Multi-User Massive MIMO Systems.

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system's sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS-users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.

3D Antenna array for multi-user massive MIMO system to minimize unfavorable propagation

Unfavorable propagation in massive multiple input multiple output (MIMO) systems is an underlying channel condition that reduces the gains offered by the system in terms of parallel channels. This paper presents a study on different types of antenna array configurations at base station (BS) to minimize unfavorable propagation conditions. Unfavorable propagation results in increased correlation between channel vectors of two or more mobile terminals (MTs) which arises when the signals from two or more MTs have small angular separation and small angular spread. The study starts with the investigation of the conventional uniform linear array (ULA) under different propagation conditions using a geometrical channel model based on a single cluster of scatterers per MT to quantify the performance degradation in unfavorable propagation. Subsequently, the performance of other types of array geometries is investigated. A custom cluster-based geometrical channel model is used in this study which includes practical propagation aspects like multipath fading, shadow fading, and the effect of BS height. Further, the study considers two cases: micro-cell and macro-cell. Under space constraint settings in terms of the maximum dimension of the array, the proposed technique uses randomized antenna locations in the array to minimize the effect of unfavorable propagation. Based on the results on performance parameters like user correlation and capacity, the study reveals that an antenna array configuration comprising of elements distributed random-homogeneously in a spherical shell outperforms other configurations.

Joint CFO and Channel Estimation for RIS-Aided Multi-User Massive MIMO Systems

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as the RIS phases are adjusted based on the channel estimates and even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure makes it possible to accurately estimate the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a more computationally demanding grid search technique while having a significantly lower computational complexity. Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel es-timation as well as in the bit error rate (BER) performance. Moreover, analyzing the computational complexity and the pilot resource efficiency of the proposed method, we demonstrate that the proposed estimation approach requires low computational load and no extra cost in pilot overhead.

Semi-Probabilistic Repetition Schemes for Sporadic URLLC Traffic in Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) assisted grant-free access is a compelling approach to support uplink ultra-reliable low-latency communications (URLLCs). It is a natural idea to combine massive MIMO and repetition transmission to further improve the reliability. However, excessive repetitions may increase the collision probability in multiuser scenarios and thus limit the reliability improvement. In this paper, we propose a novel semi-probabilistic repetition (SPRe) scheme for the massive multiuser MIMO (MU-MIMO) systems with grant-free access. By introducing a transmission probability for the replicas, the SPRe is more flexible than the popular repetition scheme, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition, and covers the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition as a special case. Considering practical MU-MIMO systems with finite block-length coding, channel estimation errors, pilot collisions and the sporadic arrivals of URLLC traffic, the reliability of the proposed SPRe is thoroughly analyzed and derived in closed form. Through further asymptotic analysis, the optimal transmission probability to maximize the reliability of MU-MIMO is derived for the high-SNR regime and low-load scenarios. Moreover, the diversity gain of the proposed SPRe is also analyzed and it is proved that the optimal SPRe is superior to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition in terms of reliability. The analysis and superiority of the SPRe are finally verified through computer simulations.

Hybrid Beamforming for Multi User Massive MIMO Systems

The increasing demand in wireless communications for enhanced spectral efficiency (SE) and throughput makes massive multiple input multiple output (MIMO) a great choice for meeting those demands by using a vast set of antennas. Despite the advantages of massive MIMO, to implement such systems comes with a huge price tag and consumes a lot of power. Hybrid beamforming (HBF) architecture has drawn considerable attention in the past few years, by significantly decreasing the amount of employed radio frequency (RF) chains and combining high dimensional analog beamforming (ABF) using phase shifters (PS) together with low dimensional digital beamforming (DBF). However, because of the extreme energy consumption and hardware complexity, traditional precoding designs are difficult to implement. In this paper, two HBF techniques are proposed to address the above issue. (i) low complexity precoding known as phased zero forcing (PZF) precoding, which controls phase only in the RF domain (ii) singular value decomposition (SVD) based optimal unconstrained precoding, that can be implemented on inexpensive RF components.

Secure Communication for Spatially Correlated RIS-Aided Multiuser Massive MIMO Systems: Analysis and Optimization

This letter investigates the secure communication in a reconfigurable intelligent surface (RIS)-aided multiuser massive multiple-input multiple-output (MIMO) system exploiting artificial noise (AN). We first derive a closed-form expression of the ergodic secrecy rate under spatially correlated MIMO channels. By using this derived result, we further optimize the power fraction of AN in closed form and the RIS phase shifts by developing a gradient-based algorithm, which requires only statistical channel state information (CSI). Our analysis shows that spatial correlation at the RIS provides an additional dimension for optimizing the RIS phase shifts. Numerical simulations validate the analytical results which show the insightful interplay among the system parameters and the degradation of secrecy performance due to high spatial correlation at the RIS.

Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?

Precoding design for maximizing weighted sum-rate (WSR) is a fundamental problem for downlink of massive multi-user multiple-input multiple-output (MU-MIMO) systems. It is well-known that this problem is generally NP-hard due to the presence of multi-user interference. The weighted minimum mean-square error (WMMSE) algorithm is a popular approach for WSR maximization. However, its computational complexity is cubic in the number of base station (BS) antennas, which is unaffordable when the BS is equipped with a large antenna array. In this paper, we consider the WSR maximization problem with either a sum-power constraint (SPC) or per-antenna power constraints (PAPCs). For the former, we prove that any nontrivial stationary point must have a low-dimensional subspace structure, and then propose a reduced-WMMSE (R-WMMSE) with linear complexity by exploiting the solution structure. For the latter, we propose a linear-complexity WMMSE approach, named PAPC-WMMSE, by using a novel recursive design of the algorithm. Both R-WMMSE and PAPC-WMMSE have simple closed-form updates and guaranteed convergence to stationary points. Simulation results verify the efficacy of the proposed designs, especially the much lower complexity as compared to the state-of-the-art approaches for massive MU-MIMO systems.

A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output (MIMO) systems, a large amount of antennas are deployed at the Base Station (BS). In this case, the Minimum Mean Square Error (MMSE) detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation. The optimization algorithms such as Gradient Descent (GD) method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation. However, they converge slowly when the condition number of the MMSE filtering matrix (the coefficient matrix) increases, which can compromise the efficiency of their implementation. Moreover, their soft information computation also involves a large-scale matrix-matrix multiplication operation. In this paper, a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent (APGD-SOD) method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems. In the proposed detector, an Adaptive Pre-conditioner (AP) matrix obtained through the Quasi-Newton Symmetric Rank One (QN-SR1) update in each iteration is used to accelerate the convergence of the GD method. The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix, since then the condition number would be close to unity and the convergence would be rapid. By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems, the computational complexity of the soft information is simplified. The proposed AP matrix is applied to the GD method as a showcase. However, it also can be used by Conjugate Gradient (CG) method due to its generality. It is demonstrated that the proposed detector is robust and its convergence rate is superlinear. Simulation results show that the proposed detector converges at most four iterations. Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation, which entails a big challenge for the efficient implementation.

One-bit DACs precoding for massive MU-MIMO systems based on antenna selection architecture

In massive multi-user multiple-input multiple-output (MU-MIMO) communication systems, the utilization of one-bit digital-to-analog converters (DACs) in the base station (BS) radio frequency (RF) chains can significantly reduce the power consumption of the system. This study proposes a low-cost precoding architecture for massive MU-MIMO systems to alleviate multi-user interference (MUI). By including a selection structure, the architecture decreases the power consumption of typical one-bit precoding architecture. Based on this structure, we investigate the antenna selection-aided one-bit DACs precoding problem. However, jointly identifying the precoding factor and the precoding vector of the proposed precoding architectures is intractable because they are coupled together. We address this issue by utilizing an alternating direction multiplication method (ADMM) framework. The antenna selection strategy and the implementation of the non-convex approximate projection operator are given in detail. In addition, the power consumption of the proposed system architecture is analyzed. Simulation results reveal that the proposed precoding algorithm can successfully activate the antenna subset and ensure the performance of the system. Moreover, the hardware complexity and power consumption of the proposed precoding architectures are considerably lower than those of previous one-bit DACs precoding architectures.

Channel Estimation for Hybrid Massive MIMO Systems With Adaptive-Resolution ADCs

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes.

Decentralized Baseband Processing With Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems

Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. Based on the message passing rule, each antenna cluster iteratively calculated the local means and variances which are fused to generate the global symbol beliefs for demodulation. The state evolution framework of the decentralized GMP algorithm is presented under the assumptions of large-system limit and Gaussian sources. Analytical results corroborated by simulations demonstrate that the nonuniform antenna cluster partition scheme exhibits a higher convergence rate than the uniform counterpart. Simulation results illustrate that the proposed decentralized GMP detection outperforms the recently proposed decentralized algorithms.

LDPC Coded Multi-User Massive MIMO Systems With Low-Complexity Detection

We design a low-density parity-check (LDPC) coded multi-user (MU) massive multiple-input multiple-output (MIMO) system with an iterative joint detection and decoding (JDD) algorithm. As a low-complexity MU detection scheme, we consider a factor graph based belief propagation detection with Gaussian approximation of interference, called a FG-GAI BP detection. We introduce a factor graph representation of LDPC coded MU massive MIMO system and define the message updating rule in the JDD process. We devise a design tool for analyzing extrinsic information transfer (EXIT) characteristics of messages exchanged in JDD, based on which degree distribution of LDPC codes and a JDD strategy are efficiently designed for coded MU massive MIMO systems. A JDD strategy and LDPC codes are designed such that a fast convergence of JDD and a low bit error probability are attained. It is observed that the coded MU massive MIMO system equipped with LDPC codes and the JDD strategy designed by the proposed method shows a lower bit error rate than conventional ones with a given number of iterations.

Low-Complexity Constant Envelope Precoding With One-Bit DAC for Massive MU-MIMO Systems

Constant envelope (CE) precoding design problem with one-bit digital-to-analog-converters (DACs) in massive multiuser multiple-input multiple-output (MU-MIMO) has been one of core issues in fifth-generation (5G) communication technology, because CE precoding with one-bit DACs can avoid high hardware costs and high power consumption caused by a lot of radio frequency chains. In order to design the CE precoding with one-bit DACs, we need to solve a non-convex combinatorial optimization problem by jointly optimizing the precoding vector and the precoding factor. However, the corresponding optimization problem is NP-hard and difficult to solve. In this paper, we develop a linearized alternating direction method of multipliers (LADMM) algorithm to tackle the problem. Numerical experiments show that the presented LADMM algorithm gets the comparable bit error ratio (BER), faster convergence and lower complexity than the existing methods.

Channel Estimation Based on Compressive Sensing for Multi-User Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future wireless communications. Channel feedback for massive MIMO is a challenging task due to the increased dimension of MIMO channel matrix. By exploiting the channel sparsity, channel estimation based on compressive sensing (CS) aims to reduce the feedback overhead in massive MIMO systems. In this paper, various CS algorithms for channel estimation in massive MIMO systems are summarized, and a novel CS algorithm, i.e. modified sparsity adaptive matching pursuit (MSAMP), is proposed hereupon. Moreover, various measurement matrices are introduced in the CS scheme. The performances of channel estimation and recovery are simulated and compared. It is inferred from simulation that, SAMP is very appropriate for reconstructing sparse channel information in massive MIMO system, especially the modified SAMP can give higher reconstruction, and some measurement matrix is suitable for certain optimal CS algorithms.

A pilot allocation method for multi-cell multi-user massive MIMO system

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple out-put (MIMO) systems. This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency. The pilots of the terminals are allocated one-bit orthogonal identifier to diminish the cell categories by the operation of exclusive OR (XOR). At the same time, the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm, and different pilot sequences are assigned to different groups. The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Hybrid precoding for mmWave massive MU-MIMO systems with overlapped subarray: A modified GLRAM approach

Abstract Mmillimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) system usually utilizes hybrid precoding to reduce complex hardware and high energy consumption. However, most hybrid precoding scheme consider the fully connected architecture, which may result in a significant loss of energy efficiency. In this paper, we propose a hybrid precoding scheme based on the overlapped subarray architecture (OSA), where the analog part of the MU-MIMO system is realized by a OSA network. Based on the OSA architecture, a modified generalized low rank approximation of matrices (GLRAM) based hybrid precoding scheme is designed to maximize the achievable sum-rate, where the analog precoding algorithm apply the phase alignment technology and GLRAM approach to obtain RF precoder/combiner with high array gain provided by massive MIMO system. Then, the block diagonalization (BD) technology is performed to obtain the digital precoding matrix for the multiplexing gain. Simulation results show that the modified GLRAM based hybrid precoding algorithm for OSA architecture can reduce the number of iterations. Compared with the existing hybrid precoding algorithm, it achieves higher spectral efficiency and lower complexity.

A novel committee machine hybrid precoding and combining algorithm for mmWave massive MU-MIMO systems

Millimeter-wave (mmWave) massive multi-user multiple input multiple output (MU-MIMO) systems employ hybrid analog-digital precoding/combining design, to reduce the number of radio-frequency (RF) chains without large sum-rate performance loss. Committee machines combine several expert algorithms to yield better reliable estimates to those of its constituent experts. However, several committee machines approach mainly rely on the ‘quality’ of the joint support-set of expert algorithms combine by committee machine. In this paper, we present hybrid analog-digital precoder and combiner designs for mmWave massive MU-MIMO systems by exploiting the intersection of the estimated support-sets, namely, common support-set of ‘expert’ algorithms in a committee machine methodology. With S denoting the sparsity level, the proposed algorithms perform two essential tasks to identify in which subspace, generated by not more than S columns of the matrix of array response vectors, the optimal hybrid analog-digital precoders or combiners lie: Firstly, the selection of the committee machine common support set, which accepts the section where both the ‘experts’ agree. Secondly, if some column indices of the optimal hybrid analog-digital precoders or combiners do not lie in the current common support-set for the correct spanning space, −the algorithm searches over the matrix of array response vectors and selects column indices that exhibit the highest correlation with the signal residual, to acquire the remaining support. We derive the theoretical analysis for performance enhancement of the proposed algorithm. Simulation results demonstrate that the proposed design allows mmWave massive MU-MIMO systems to approach their unconstrained performance limits, and exhibits substantial sum-rate performance improvement over existing regularized channel diagonalization based schemes and the beam steering solution.

Is Multipath Channel Beneficial for Wideband Massive MIMO With Low-Resolution ADCs?

Coarse quantization by using low-resolution analog-to-digital converters (ADCs) is an attractive approach to relieve the burden of power consumption and hardware cost of implementing massive multiple-input multiple-output (MIMO) systems. In this article, we analyze the uplink spectral efficiency of a multiuser massive MIMO system with low-resolution ADCs in the context of orthogonal frequency division multiplexing (OFDM) under multipath channels. Firstly, we develop an efficient pilot scheme which results in a constant average power of the quantization noise for different channel delay power spectrums and also minimizes the mean squared error of channel estimation. Then a tight approximation of the uplink achievable rate is derived in a closed form considering both perfect channel state information (CSI) and estimated CSI. Then we analyze the impact of multipath channels on the system performance. Under perfect CSI, we discover that an increment of multipath taps has a positive impact on compensating the performance degradation due to the quantization noise. Under imperfect CSI, the most beneficial channel is uniformly distributed over a specific number of taps. Simulations are conducted to verify our analytical results.

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.