Multiuser Multiple-input Multiple-output Systems Research Articles

Recent Advances in Acquiring Channel State Information in Cellular MIMO Systems

In cellular multi-user multiple input multiple output (MU-MIMO) systems the quality of the available channel state information (CSI) has a large impact on the system performance. Specifically, reliable CSI at the transmitter is required to determine the appropriate modulation and coding scheme, transmit power and the precoder vector, while CSI at the receiver is needed to decode the received data symbols. Therefore, cellular MUMIMO systems employ predefined pilot sequences and configure associated time, frequency, code and power resources to facilitate the acquisition of high quality CSI for data transmission and reception. Although the trade-off between the resources used user data transmission has been known for long, the near-optimal configuration of the vailable system resources for pilot and data transmission is a topic of current research efforts. Indeed, since the fifth generation of cellular systems utilizes heterogeneous networks in which base stations are equipped with a large number of transmit and receive antennas, the appropriate configuration of pilot-data resources becomes a critical design aspect. In this article, we review recent advances in system design approaches that are designed for the acquisition of CSI and discuss some of the recent results that help to dimension the pilot and data resources specifically in cellular MU-MIMO systems.

Controlling High PAPR in Vehicular OFDM-MIMO using Downlink Optimization Model under DCT Transform

The persisting challenges of the radio channel in vehicular networks entail the use of multi-antennas which is known as Multiple-Input Multiple-Output (MIMO). In order to obtain an efficient multi-user MIMO system, the power of the radio frequency (RF) components should be optimized. Necessarily practical solutions are essential to lower the vehicular nodes' complexity and support a robust Orthogonal Frequency Division Multiplexing (OFDM) discipline with a simplified equalization at the receiver. In this paper, the pre-coding Zadoff-Chu Sequence (ZCS) is employed along with the Discrete Cosine Transform (DCT) to control and optimize the high peak power. It intends the transmission over multi-user MIMO downlink vehicular channels. At last, the convex optimization is utilized to guarantee the peak-to-average power ratio (PAPR) minimization. Simulation results have shown that the proposed model can lessen the high PAPR compared to the least-square pre-coding. At the same time, it proved its effectiveness and accuracy as it enhances the transmission quality over multi-user MIMO-OFDM downlink vehicular channel.

Combining Alamouti Scheme with Block Diagonalization Beamforming precoding for 5G Technology over Rician Channel

Wireless communication faces a number of adversities and obstacles as a result of fading and co-channel interference (CCI). Diversity with beamformer techniques may be used to mitigate degradation in the system performance. Alamouti space-time-block-code (STBC) is a strong scheme focused on accomplishing spatial diversity at the transmitter, which needs a straightforward linear processing in the receiver. Also, high bit-error-rate (BER) performance can be achieved by using the multiple-input multiple-output (MIMO) system with beamforming technology. This approach is particularly useful for CCI suppression. Exploiting the channel state information (CSI) at the transmitter can improve the STBC through the use of a beamforming precoding. In this paper, we propose the combination between Alamouti STBC and block diagonalization (BD) for downlink multi-user MIMO system. Also, this paper evaluates the system performance improvement of the extended Alamouti scheme, with the implementation of BD precoding over a Rayleigh and Rician channel. Simulation results show that the combined system has performance better than the performance of beamforming system. Also, it shows that the combined system performance of extended Alamouti outperforms the combined system performance without extended Alamouti. Furthermore, numerical results confirm that the Rician channel can significantly improve the combined system performance.

Joint Quantized Constant Envelope Precoding and Antenna Selection for Massive MU-MIMO Downlink Systems Using Higher-Order QAM

This paper introduces the antenna selection (AS) mechanism into the quantized constant envelope (CE) precoding in massive multiuser multiple-input multiple-output systems to simultaneously reduce multiuser interference and hardware power consumption. However, this joint design problem requires optimizing over the precoding vector, the precoding factor, and the selected antenna subset. Unfortunately, these design parameters are connected. To address this problem, we propose an iterative algorithm developed from the alternating minimization (AltMin) framework to decouple the considered problem into two subproblems: the precoding factor design problem and the joint AS and the precoding vector design problem. The former can be easily solved approximately, whereas the latter incurs combinatorial complexity. We address this issue by using a simple yet effective coordinate update algorithm (CUA). We also develop a reduced-complexity version of CUA without performance loss. Moreover, the proposed CUA-based AltMin algorithm can be applied to the quantized CE precoding design problems without modification. The simulation results show that the proposed CUA-based AltMin algorithm considerably outperforms the existing algorithms for the quantized CE precoding design problems. In addition, when the AS mechanism is included in the quantized CE precoding design, CUA-AltMin with AS achieves the same or higher performance than CUA-AltMin without AS while providing further power savings.

A Game Theoretic Approach to Uplink Pilot and Data Power Control in Multi-Cell Multi-User MIMO Systems

In multi-user multiple-input–multiple-output (MU-MIMO) systems that employ pilot-symbol aided channel estimation, the pilot-to-data power ratio (PDPR) has a large impact on the system performance. In this paper, we consider the problem of setting the PDPR in multi-cell MU-MIMO systems in the presence of channel estimation errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a model of the multi-cell MU-MIMO system and derive a closed-form expression for the mean squared error of the uplink received data symbols. Building on this result, we then propose two decentralized PDPR-setting algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that do not properly set the PDPR, while they maintain different levels of fairness.

Superimposed Pilot Optimization Design and Channel Estimation for Multiuser Massive MIMO Systems

For massive multiuser multiple input multiple output (MIMO) (MU-MIMO) systems, pilot contamination (PC) and data interference (DI) are two main factors that affect the accuracy of the superimposed pilot (SP) based channel estimation, provided that the channel coherence time is less than the total number of users. In this paper, to tackle the effect of the PC, we propose a block-diagonal Grassmannian line packing (GLP) approach, in which the specific sequences for different cells are first designed based on the GLP, and then are block-diagonally extended to form the SP matrices for the users in different cells. To also alleviate the effect of DI on the channel estimation, with the designed SP matrices, an iterative channel estimation (ICE) method based on Tikhonov regularization is presented. The impact that the proposed ICE method has on the signal-to-interference-plus-noise ratio with matched filtering receiver is theoretically analyzed. Moreover, the optimal power allocation factor to the SP by maximizing the spectral efficiency (SE) of the target cell is also derived. Numerical results show that the proposed ICE method with the designed SP matrices effectively mitigates the effects of the PC and DI, and hence, improves the accuracy of channel estimation and further the SE of the massive MU-MIMO systems.

Per-Antenna Power Distribution of a Zero-Forcing Beamformed ULA in Pure LOS MU-MIMO

An analytical model is presented to determine the per-antenna power distribution of a beamformed $M$ -element uniform linear array (ULA) as a base station antenna (BSA). The analysis assumes a single sector cell serving two pieces of user equipment (UE) in a downlink multi-user multiple-input multiple-output system employing zero-forcing transmission. It is shown that the power distribution across the ULA antenna aperture is a periodic function whose characteristics mainly depend on the angular separation of two UEs and the number of BSA antenna elements. A significant variation between ULA input powers is seen to occur if the number of elements in the ULA is smaller than one period of this power distribution function. In order to mitigate the dynamic range of the power variation across the array, an upper bound for the inter-element spacing, depending on the field-of-view of the BSA, is defined. It is shown that, in a 20-element ULA, increasing the inter-element spacing from $0.5\lambda $ to $1.4\lambda $ reduces the power variation from 19 to 10 dB for differentiating two close-by UEs with 1° angular separation.

Fundamental Limits of Training-Based Uplink Multiuser MIMO Systems

In this paper, we endeavor to seek a fundamental understanding of the throughput limit for a training-based uplink multiuser multiple-input multiple-output (MIMO) system. In a multiuser MIMO system, users are geographically separated and experience significantly different large-scale fading. As large-scale fading varies order of magnitude more slowly than small-scale fading, the system resource consumed for acquiring large-scale fading coefficients is usually marginal as compared with that for small-scale fading coefficients. Such a difference between large-scale and small-scale fadings, however, has not been appropriately handled in the existing approaches for training-based multiuser MIMO systems. In this paper, we ignore the overhead for large-scale fading acquisition by assuming that the large-scale fading coefficients are known a priori , whereas the small-scale fading coefficients are estimated via training. Our target is to maximize the mutual information lower bound (MILB) of a training-based uplink multiuser MIMO system over various system parameters, including the training length, the training signals, the number of users, and the power allocation between training and data transmission. As the exact solution is difficult, we establish upper and lower bounds of the MILB. Specifically, we use the majorization theory to establish an upper bound of MILB. We then propose a lower bound of the MILB by deactivating a portion of users with the smallest large-scale fading gains and assigning orthogonal training sequences to the rest of the users. We show that this simple lower bound is asymptotically optimal for MILB maximization in the high signal-to-noise ratio (SNR) regime. We also show that the upper and lower bounds are reasonably tight in the finite SNR regime under various system configurations. Furthermore, we derive the optimal training design for MILB maximization in uplink multiuser MIMO with uniform large-scale fading. Numerical results are presented to verify our analysis.

A Hardware-Scalable DSP Architecture for Beam Selection in mm-Wave MU-MIMO Systems

Millimeter-wave (mm-Wave) communication, operating from 30- to 300-GHz bands with wider available spectrum, has now emerged as a promising solution for future wireless systems. To reduce the hardware complexity of mm-Wave transceivers, beam-selection techniques combined with beamspace multiple-input multiple-output (MIMO) communications have been proposed in the literature. In such a system, beam selection is performed to exploit the near-sparse nature in the mm-Wave channel and, hence, is crucial to the system performance. This paper presents the world’s first digital signal processing (DSP) architecture design for beam selection operation in an mm-Wave multi-user MIMO system. A computationally efficient beam-selection algorithm is first presented. Based on the proposed algorithm, a hardware-scalable beam selection processor is implemented using coordinate rotation digital computers arithmetic. To establish the validity of the concept, the proposed processor is implemented in a 40-nm CMOS technology, integrating 360k logic gates in an area of 0.5625 mm2, while dissipating 44 mW at 266 MHz in an MU-MIMO system with 16 beams and 8 users.

Joint Transmit Precoding and Receive Antenna Selection for Uplink Multiuser Massive MIMO Systems

This paper considers the uplink of multiuser multiple-input multiple-output systems, where several mobile stations (MSs) cooperatively transmit hybrid messages, including common messages and private messages, to a single base station (BS). We aim to jointly design transmit precoding at the MSs' side and antenna selection at the BS side to maximize the achievable system throughput while reducing implementation complexity. The problem at hand is nonconvex and difficult to solve due to the antenna selection constraint. By exploiting the problem structure and linear relaxation, we propose using the Frank-Wolfe method and the well-known weighted mean-square error minimization approach to tackle the problem, leading to an efficient iterative algorithm. Moreover, due to the large number of antennas, the sparsity of antenna selection is also taken into account by introducing an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm penalty function into the objective function. To tackle this nonconvex and discontinuous problem, we resort to quadratic approximation with smooth optimization and extend our proposed algorithm to the sparse optimization problem. The convergence of the proposed algorithms is analyzed and its effectiveness is verified by numerical examples in terms of the achieved system throughput.

Performance improvement of multi-user MIMO system in the presence of nonlinear distortion

In the presence of nonlinear distortion, the performance degradation of multiuser multiple-input multiple-output (MU-MIMO) systems becomes significant. The performance loss comes not only from the in-band nonlinear distortion, but also from the imperfect cancellation of multiuser interference (MUI) due to the nonlinear distortion. In this regard, we analyze characteristics of nonlinear distortion in an MU-MIMO system. Numerical results show that even tiny nonlinear distortion severely degrades the performance of MU-MIMO system. A nonlinear minimum mean square error (NMMSE) detection algorithm is proposed to improve the system performance. The proposed detection algorithm improves the system performance at low additional cost.

Solving Fractional Polynomial Problems by Polynomial Optimization Theory

This work aims to introduce the framework of polynomial optimization theory to solve fractional polynomial problems (FPPs). Unlike other widely used optimization frameworks, the proposed one applies to a larger class of FPPs, not necessarily defined by concave and convex functions. An iterative algorithm that is provably convergent and enjoys asymptotic optimality properties is proposed. Numerical results are used to validate its accuracy in the non-asymptotic regime when applied to the energy efficiency maximization in multiuser multiple-input multiple-output communication systems.

Low‐complexity user selection method in 3D MU‐MIMO systems

The user selection method based on chordal distance is widely used in multiuser multiple-input multiple-output (MU-MIMO) systems with block diagonalisation. It has throughput close to the capacity based method with low complexity. In this Letter, a user selection method with higher throughput and much lower complexity is proposed. To investigate the performance in a more practical channel scenario, 3D MIMO channels are used in simulations. Compared with existing user selection algorithms, the proposed method achieves good throughput performance and the lowest complexity order.

Notice of Violation of IEEE Publication Principles: A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems

Notice of Violation of IEEE Publication Principles A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems by Imran Khan in IEEE Transactions on Vehicular Technology, July 2018, 9597-9604 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copied portions of text from the following paper with no credit to the original work (including appropriate references to the original author(s) and/or paper title) and without permission. Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems by Lei Liu, Chau Yuen, Yong Liang Guan, Ying Li, and Yuping Su in IEEE Transactions on Wireless Communications, June 2016, pp.6487-6501 Aiming at the problem of high detection complexity at the base station in massive multiuser multiple-input multiple-output system, this paper proposes a low complexity variable node full information Gaussian message passing iterative detection based on forced convergence (VFI-GMPID-FC) algorithm. First, the traditional GMPID algorithm is improved to get the VFI-GMPID algorithm. The detection performance of the VFI-GMPID algorithm approaches the minimum mean square error (MMSE) detection algorithm, but the complexity is much lower than that of the MMSE algorithm. Combined with the idea of forced convergence and VFI-GMPID, the VFI-GMPID-FC algorithm is proposed to further reduce the complexity of the algorithm and improve the detection efficiency. Finally, the simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm while ensuring improved detection performance.

Exploiting high rate differential algebraic space‐time block code in downlink multiuser MIMO systems

This paper considers a multiuser multiple-input multiple-output (MIMO) space-time block coded system that operates at a high data rate with full diversity. In particular, they propose to use a full rate downlink algebraic transmission scheme combined with a differential space-time scheme for multiuser MIMO systems. To achieve this, perfect algebraic space-time codes and Cayley differential transforms are employed. Since channel state information (CSI) is not needed at the differential receiver, differential schemes are ideal for multiuser systems to shift the complexity from the receivers to the transmitter, thus simplifying user equipment. Furthermore, orthogonal spreading matrices are employed at the transmitter to separate the data streams of different users and enable simple single user decoding. In the orthogonal spreading scheme, the transmitter does not require any knowledge of the CSI to separate the data streams of multiple users; this results in a system which does not need CSI at either end. With this system, to limit the number of possible codewords, a sphere decoder is used to decode the signals at the receiving end. The proposed scheme yields low complexity transceivers while providing full rate full diversity with good performance. Simulation results demonstrate the effectiveness of the proposed scheme.

Maximizing Energy Efficiency in Downlink Massive MIMO Systems by Full-complexity Reduced Zero-forcing Beamforming

Energy efficiency (EE) is one of the key design goals for fifth-generation (5G) cellular networks due to the intermittent properties of renewable energy sources and limited battery capacity. In this paper, we analyze the EE of downlink (DL) massive multi-user multiple-input multiple-output (MIMO) system based on circuit power consumption for the transmit antenna configuration. We designed full complexity reduced zero-forcing (ZF) beamforming to cancel out interbeam interference when the number of antennas and minimized the power consumption model, when formulating the power allocation optimization problem with the Lagrange duality method, in order to maximize EE. Simulation results revealed that the EE in the base station (BS) could be improved when the number of radio frequency (RF) chains was proportional to the optimal transmit power allocation and when the consumption circuit power was comparable to the transmit power.

Hybrid Beamforming for Multi-User Massive MIMO Systems

The large scale multiple-input multiple-output (MIMO) system with hybrid beamforming (HBF) is a promising communications technology due to its excellent tradeoff between hardware complexity and system performance. Assuming perfect channel state information is acquired, we consider a single cell downlink multi-user massive MIMO system working in a generic channel model with a hybrid structure that supports multiple streams per UE. We aim to find an analog and digital precoder/combiner that maximizes the sum-rate of the communication system. Unlike the traditional two-stage design criterion, which separately designs the analog and digital stages, our proposed criterion jointly designs two stages by trying to avoid the loss of information at each stage. When double the least number of radio frequency (RF) chains are available, we provide an asymptotically optimal solution in a massive MIMO regimen, i.e., the sum-rate of such an HBF solution could approach the channel capacity under large base station (BS) antenna arrays. A corresponding solution using the fewest RF chains is then derived. Finally, the simulation results are shown to validate the proposed schemes. Specifically, the solution with the fewest RF chains is shown to outperform the state of the art for HBF systems, even when the number of BS antennas is not very large. It should be noted that the schemes proposed in this paper have low complexity owing to their closed-form solutions.

All Cognitive MIMO: New Multiuser Detection Approach With Different Priorities

A new detection scheme for multiuser multiple-input multiple-output (MIMO) systems is analytically presented. In particular, the transmitting users are being categorized in two distinct priority service groups, while they communicate directly with a multi-antenna receiver. The linear zero-forcing scheme is applied in two consecutive detection stages upon the signal reception. In the first stage, the signals of one service group are detected, followed by the second stage, including the corresponding detection of the remaining signals. An appropriate switching scheme based on specific transmission quality requirements is utilized prior to the detection so as to allocate the signals of a given service group to the suitable detection stage. The objective is the provision of the reception quality for both service groups. The proposed approach can be implemented directly in cognitive radio communication assigning the secondary users to the appropriate service group. The exact outage probability of the considered system is derived in closed form. The special case of massive MIMO is further studied yielding some useful engineering outcomes; the effective channel coherence time and a certain optimality condition defining both the transmission quality and effective number of independent transmissions.

Hybrid Precoding Architecture for Massive Multiuser MIMO With Dissipation: Sub-Connected or Fully Connected Structures?

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully-connected structure. It is revealed that increasing signal power can compensate the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization (RVQ) codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully-connected structure in either massive MIMO or low signal-to-noise ratio (SNR) scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mmWave) channels verify the conclusions above.

Low-Complexity Constant Envelope Precoding Using Finite Resolution Phase Shifters for Multiuser MIMO Systems With Large Antenna Arrays

The present study considers a constant envelope (CE) precoding design problem in massive multiuser multiple-input multiple-output systems, where CE precoding is implemented with a network of finite resolution phase shifters (PSs). Mathematically, such CE precoding design problem can be formulated as a discrete optimization problem whose optimal solution can be obtained through an exhaustive search. Unfortunately, an exhaustive search involves exponential complexity with numerous base station antennas ( $M$ ) and bits in PS resolution. We resolve the aforementioned concern by using a low-complexity algorithm developed from the iterative gradient descent (IGD) based algorithmic framework. However, unlike conventional IGD-based solvers, finite resolution PSs are considered directly in the optimization procedure of the proposed algorithm to improve performance. In addition, the proposed algorithm is roughly $M/2$ times less complex than the conventional IGD algorithm at each iteration. The simulation results reveal that the proposed algorithm has nearly the same performance as that of the unquantized version of the state-of-the-art IGD algorithm.