Multiuser Multiple-input Multiple-output Research Articles

Codesign for Hybrid MU-MIMO Communication and MIMO Radar Systems Based on Mutual Information

To enhance the coexistence performance of multiple-input multiple-output (MIMO) radar system and multiuser MIMO communication system with hybrid beamforming architecture, this work proposes a codesign problem regarding the digital and analog beamformers of communication base station, the receive beamformers of downlink users, and the transmit waveform of radar. An information-theoretic criterion is adopted to improve the radar detection performance, while the signal-to-interference-plus-noise ratio requirement is imposed on each downlink user to ensure communication quality. In addition, similarity restriction is also involved to manage some significant radar waveform characteristics. An iterative algorithm is introduced to tackle the resulted nonconvex and NP-hard problem. In each iteration, several subproblems are developed to decompose the original problem and are solved by the lower bound maximization and the alternating direction method of multiplier algorithm. Numerical simulations show that the proposed algorithm allows the objective function to increase monotonically and finally converge to a finite value. It also shows that the codesign scheme can effectively optimize the radar detection performance while ensuring the achievable sum rate and the bit error rate of the communication system.

Kinerja Multi-user MIMO OFDM dengan Block-Diagonalization Precoding

Multiple-input multiple-output (MIMO) is a transmission technology that uses more than one antenna both on the transmitter side and on the receiver side. In the communication services, the system will involve more than one active user who uses one base station (BS) together so that it is called multi-user MIMO (MU-MIMO). This paper discusses the performances of MU-MIMO when it uses block diagonalization (BD) precoding and applies minimum mean squared error (MMSE) detector. As a comparison we also apply zero-forcing (ZF) detector. From the simulations that have been done, it is found that the MMSE had better performances than the ZF detector in terms of BER. We also consider the performance when users are moving. The simulation results show that BER performances are decrease slowly when users are moving.

Multiuser-MIMO Systems Using Comparator Network-Aided Receivers With 1-Bit Quantization

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.

Phase Rotation Assisted Interference Exploitation Precoding With Finite Block Length for MU-MIMO Systems

The interference exploitation precoding (IEP) leverages the harmful interference to align at the constructive interference (CI) region via symbol level precoding (SLP), which greatly improves the conventional zero forcing precoding with QPSK modulation or even higher modulations. This letter firstly develops a joint phase rotation structure, considering both the outer phase rotation for multiple layers and the inner phase alignment for IEP with finite block length in multi-user multiple-input multiple-output (MU-MIMO) systems, termed as JPR-IEP. To tackle the problem, a decoupling scheme is presented, which breaks JPR-IEP into two independent steps as DPR-IEP, thus converting the non-convex JPR-IEP to a solvable problem with low complexity. At last, a codebook-assisted DPR-IEP, namely CB-DPR-IEP with less computational complexity and feed-forward overhead is further investigated, which is approved to be the optimal choice considering the performance, the feed-forward and the complexity for practical MU-MIMO systems.

Auxiliary Variable-Aided Hybrid Message Passing for Joint Channel, Phase Noise Estimation and Detection for Multi-User MIMO Systems

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system with independent phase noise (PN) at user terminals (UTs) due to non-synchronous noisy local oscillators. The conventional factor graph of MU-MIMO involves an observation factor with multiple high-dimensional variables in the form of multiplication and summation. In this work, the MU-MIMO factor graph is represented by introducing auxiliary variables, which enables the decomposition of the observation factor into several simpler sub-factors and then use different message passing techniques to obtain approximate marginals. Specifically, belief propagation and expectation propagation (BP-EP) are used for the linear factors for random walk process, modulation and coding, while mean field (MF) is used for non-linear factor in exponential form. To reduce the computational complexity, the non-Gaussian MF messages are approximated to be Gaussian by using the second order Taylor expansion at its belief obtained in the previous iteration. The proposed receiver has a quadratic computational complexity per symbol per iteration. Simulation results show that the performance of the proposed receiver is better than existing methods and is close to the matched filter bound (MFB) in terms of the bit error rate (BER).

Diversity Order and Measure of MIMO Antennas in Single-User, Multiuser, and Massive MIMO Wireless Communications

In this letter, the diversity order (DO) and the diversity measure (DM) of multiple-input multiple-output (MIMO) antennas in generic single-user, multiuser, and massive MIMO wireless communications are calculated for several practical case studies. These performance metrics are calculated from the antenna correlation coefficient (ACC) matrix. The DO is calculated from the rank of the ACC matrix, and the DM is obtained from the trace and Frobenius norm of the ACC matrix. Numerical results are provided. In particular, it is shown that though the DM is always less than the DO, it incorporates the effect of antenna correlation on the diversity gain in a better way.

Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO NOMA Systems

In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.

Hybrid Precoding and Combining Based on Rate Balancing for Wideband mmWave Multiuser MIMO Systems

Considering wideband millimeter wave (mmWave) multiuser multiple-input multiple-output (MIMO) systems, we propose a new hybrid precoding and combining method for rate balancing among users. The orthogonal frequency division multiple access (OFDMA) scheme is employed for multiuser transmission, and a greedy-based algorithm is developed for subband allocation under the max-min user rate criterion. A new algorithm is derived that optimizes the radio frequency (RF) precoder and combiners under the constant-modulus constraints as well as the baseband precoders and combiners under the rate balancing power allocation criterion. The proposed algorithm iteratively updates the RF precoder and combiners using the conjugate gradient method, and baseband precoders and combiners through the singular value decomposition and numerical gradient-based power allocation among users and subcarriers. The complexity of the proposed method is compared to existing wideband hybrid processing schemes through analysis and numerical runtime measurement. The convergence of the proposed algorithm is verified via numerical simulations in mmWave MIMO-OFDMA systems. It is also shown through numerical simulations that the proposed wideband hybrid processing method outperforms the existing wideband hybrid techniques in terms of the minimum user rate, regardless of the signal-to-noise ratio, the number of antennas, and the number of users. Moreover, simulation results present that the proposed scheme is more advantageous than the conventional hybrid processing methods under channel uncertainty.

Spectral Efficiency of Precoded 5G-NR in Single and Multi-User Scenarios under Imperfect Channel Knowledge: A Comprehensive Guide for Implementation

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices.

A QoS-Adaptive Interference Alignment Technique for In-Band Full-Duplex Multi-Antenna Cellular Networks

In this paper, we propose a novel interference alignment (IA) technique for an in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) cellular network where a base station (BS) and user equipment (UE) are equipped with multiple antennas, and the local channel state information (CSI) is available at all nodes. Considering a practical IBFD MIMO cellular network, it is assumed that only the BS operates with full-duplex (FD) communication while UE operate in half-duplex (HD) mode. These IBFD networks introduce a new type of interference called cross-link interference (CLI), in which uplink UE affects downlink UE. The proposed IA technique consists of two symmetric IA schemes according to the number of antennas in the uplink and downlink UE, and both schemes effectively mitigate CLI in the IBFD MIMO network. It is worth noting that both IA schemes are adaptively applicable according to the network's quality-of-service (QoS) requirements, such as uplink and downlink traffic demands. Furthermore, we theoretically characterize and prove the achievable sum-degrees-of-freedom (DoF) of the proposed IA technique. Simulation results show that the proposed IA technique significantly improves the sum rate performance compared to conventional HD communications (multi-user MIMO) while achieving the same achievable DoF as the interference-free IBFD MIMO network.

A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.

Multi-IRS-Aided Multi-User MIMO in mmWave/THz Communications: A Space-Orthogonal Scheme

Multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are two appealing technologies in millimeter-wave (mmWave) and terahertz (THz) communications. The challenge of combining these two technologies lies in joint design for active beamforming (at the base-station (BS)/users) and passive beamforming (at the IRSs). In this paper, we consider a multi-IRS-aided multi-user MIMO scenario and propose a novel space-orthogonal scheme by applying zero-forcing techniques. Specifically, we first propose a multi-IRS-based zero-interference criterion, under which multi-user interference can be eliminated regardless of the IRS’s phase shifts. Based on this criterion, we decompose the precoder/decoder matrix into a product of two matrices, with one of them devised for interference cancellation and the other one of them devised for achievable rate maximization. Next, an approximate space-orthogonal technique referred to as partial zero-forcing (IRS-PZF) is proposed for proposed for devising the former matrix whose objective is to cancel the multi-user interference; while two efficient phase-shift schemes are proposed for the IRS passive beamforming, namely, water-filling segment matching (WSM) and phase iterative evolution (PIE), which balance between performance and complexity. Finally, we calculate the latter matrix of the precoder/decoder by applying the singular value decomposition (SVD) for the effective BS-user channels, so as to maximize the users’ achievable rates. Numerical results demonstrate the effectiveness and superiority of our proposed scheme compared with the benchmarks.

Constrained Capacity Optimal Generalized Multi-User MIMO: A Theoretical and Practical Framework

Conventional multi-user multiple-input multiple-output (MU-MIMO) mainly focused on Gaussian signaling, independent and identically distributed (IID) channels, and a limited number of users. It will be laborious to cope with the heterogeneous requirements in next-generation wireless communications, such as various transmission data, complicated communication scenarios, and unprecedented massive user access. Therefore, this paper studies a generalized MU-MIMO (GMU-MIMO) system with more generalized and practical constraints, i.e., practical channel coding, non-Gaussian signaling, right-unitarily-invariant channels (covering Rayleigh fading channel matrices, certain ill-conditioned and correlated channel matrices, etc.), and massive users and antennas. These generalized assumptions bring new challenges in theory and practice. For example, there is no accurate constrained capacity region analysis for GMU-MIMO. In addition, it is unclear how to achieve constrained-capacity-optimal performance with practical complexity. To address these challenges, a unified framework is proposed to derive the constrained capacity region of GMU-MIMO and design a constrained-capacity-optimal transceiver, which jointly considers encoding, modulation, detection, and decoding. Group asymmetry is developed to group users according to their rates, which makes a tradeoff between user rate allocation and implementation complexity. Specifically, the constrained capacity region of group-asymmetric GMU-MIMO is characterized by using the minimum mean-square error (MMSE) optimality of orthogonal/vector approximate message passing (OAMP/VAMP) and the relationship between mutual information and MMSE. Furthermore, a theoretically optimal multi-user OAMP/VAMP receiver and practical multi-user low-density parity-check (MU-LDPC) codes are proposed to achieve the constrained capacity region of group-asymmetric GMU-MIMO. Numerical results demonstrate that the proposed MU-LDPC coded GMU-MIMO systems achieve asymptotic performance within 0.2 dB from the theoretical sum capacity. Moreover, their finite-length performances are about 1~2 dB away from the associated sum capacity of GMU-MIMO.

Structured light based multiuser indoor communications: Exploiting quantum encoding over classical data channel

By leveraging the characteristics of both quantum encoding and classical data channel, we present a unique and novel multiuser optical communication system that utilizes fascinating properties of the structured light for information transmission. Specifically, we propose an indoor orbital angular momentum (OAM) multiuser multiple-input multiple-output (MIMO) optical communication system, where the quantum encoding is performed and the multiuser transmission is then analyzed from the context of the classical data channel. In the proposed system, as the OAM beam propagates, its angular spread is analyzed. In addition, we show that the variance of the beam divergence increases with the increasing OAM index mode on the basis of the derived MIMO channel impulse response for OAM carrying Laguerre–Gaussian beams. Comparative studies reveal that the proposed system significantly outperforms the conventional non-orthogonal multiple access (NOMA) applied with MIMO.

Ergodic Capacity Analysis of Downlink Communication Systems under Covariance Shaping Equalizers

Advances in higher-end spectrum utilization has enabled user equipment to dock multiple antenna elements, and hence make use of selectivity via equalization in new generation of mobile networks. The equalization can exploit channel statistics to shape covariance matrices, and hence improve network performance at the physical layer of these networks by projecting segregated signals to non-overlapping subspaces. We propose to establish the promise of covariance shaping method by incorporating the equalizers in the modelling of a downlink multi-user multiple-input multiple-output (MU-MIMO) systems and thereby characterizing a key performance indicator, namely, the sum ergodic capacity. This is achieved by utilizing a residue theory approach which can account for indefinite eigenvalues. The system modelling is generic in a sense that it requires the base station (BS) to only have second order statistics of the channel rather than instantaneous knowledge. Furthermore, the BS incorporates a transmit beamformer design to enhance the ergodic capacity and feedforward the information of covariance shaping equalizers. Search method for transmit beamforming is also proposed which shows a promising three fold increase in sum ergodic capacity at signal-to-noise ratio of 10 dB for the considered MU-MIMO system. Proposed characterization of the system is authenticated using simulation means, and a comparative analysis of transmit beamformer designs on the sum ergodic rate is showcased.

Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems

Hybrid analog/digital multiple input multiple output (MIMO) system is proposed to mitigate the challenges of millimeter wave (mmWave) communication. This architecture enables utilizing the large array gain with reasonable power consumption. However, new methods are required for the channel estimation problem of hybrid architecture-based systems due to the fewer number of radio frequency (RF) chains than antenna elements. Leveraging the sparse nature of the mmWave channels, compressed sensing (CS)-based channel estimation methods are proposed. Recently, machine learning (ML)-aided methods have been investigated to improve the channel estimation performance. Additionally, the Doppler effect should be considered for the high mobility scenarios, and we deal with the time-varying channel model. Therefore, in this article, we consider the scenario of time-varying channels for a multi-user mmWave hybrid MIMO system. By proposing a Deep Neural Network (DNN) and defining the inputs and outputs, we introduce a novel algorithm called Deep Learning Assisted Angle Estimation (DLA-AE) for improving the estimation of the Angles of Departure/Arrival (AoDs/AoAs) of the channel paths. In addition, we suggest Linear Phase Interpolation (LPI) to acquire the path gains for the data transmission instants. Simulation results show that utilizing the proposed DLA-AE and LPI methods enhance the time-varying channel estimation accuracy with low computational complexity.

Downlink MIMO-RSMA With Successive Null-Space Precoding

In this paper, we consider the precoder design for an underloaded or critically loaded downlink multi-user multiple-input multiple-output (MIMO) communication system. We propose novel precoding and decoding schemes which enhance system performance based on rate splitting at the transmitter and single-stage successive interference cancellation at the receivers. The proposed successive null-space (SNS) precoding utilizes linear combinations of the null-space basis vectors of the successively augmented MIMO channel matrices of the users as precoding vectors to adjust the inter-user-interference experienced by the receivers. We formulate a non-convex weighted sum rate optimization problem for the precoding vectors and the associated power allocation for the proposed SNS-based MIMO-rate-splitting multiple access (RSMA) scheme. We obtain a suboptimal solution for this problem via successive convex approximation. Moreover, we study the robustness of the proposed precoding scheme to imperfect channel state information (CSI) at the base station via derivative-based sensitivity analysis. Our analysis and simulation results reveal the enhanced performance and robustness of the proposed SNS-based MIMO-RSMA scheme over several baseline multi-user MIMO schemes, especially for imperfect CSI.

Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Multibeam Antenna for Ka-Band CubeSat Connectivity Using 3-D Printed Lens and Antenna Array

In this letter, the design of a passive multibeam lens antenna is proposed for the CubeSat space communication system as an alternative application of a 2-D microstrip antenna array that has originally been designed for a 39-GHz 5G multi-user, multiple-input, multiple-output (MU-MIMO) system. The half-ellipsoid lens is 3-D printed using stereolithography technology. The antenna prototype is capable of selecting the main beam between 16 different directions with a gain ranging from 14 to 16 dBi and a half-power beamwidth of 14 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> –18 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> . The measurements carried out in an anechoic chamber show good agreement with numerical simulations. The presented prototype shows that by employing 3-D printing technologies existing antennas can be easily and inexpensively converted to switched-beam or multibeam solutions.

Weighted Sum-Rate and Energy Efficiency Maximization for Joint ITS and IRS Assisted Multiuser MIMO Networks

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.