Multiuser Multiple-input Multiple-output Downlink Research Articles

NOMA‐based precoded quadrature spatial modulation in multiuser MIMO downlink transmission over correlated channel

SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.

AP cooperation in Wi-Fi: Joint transmission with a novel precoding scheme, resilient to phase offsets between transmitters

Multi Access-Point (M-AP) cooperation is expected to play a key role in the next-generation Wi-Fi standard (namely the upcoming IEEE WLAN 802.11bn/UHR, dubbed Wi-Fi-8), particularly in dense deployments where inter-cell interference hinders further increase in network capacity. Among the various considered AP-cooperation techniques, coherent Joint Transmission (JT) is the most ambitious, aiming at deploying an M-AP cluster as a single ‘super-AP’ serving multiple stations in a downlink Multi-User Multiple-Input Multiple-Output (DL MU-MIMO) fashion. Ideally, in the absence of practical impairments – predominantly inter-AP phase misalignment – prior-art JT schemes exhibit superior performance relative to simpler M-AP precoding schemes such as Coordinated Beam-Forming (Co-BF), equivalent to ∼10 dB Signal-to-Interference-plus-Noise-Ratio gain in certain scenarios. However, the Wi-Fi standard – being based on low-cost implementation and deployment strategies as a major driver for success – cannot guarantee the required tight inter-AP synchronization levels, thus jeopardizing the JT advantage in practice (essentially wiping out the gain relative to Co-BF, in the above scenarios). As a cure, we develop a novel MIMO-precoding scheme, which preserves most of the JT gain (∼7 out of the ∼10 dB above) while being inherently resilient to phase offsets between the APs, thus paving the way to practical incorporation of JT into Wi-Fi-8.

Joint Beamforming and Power Optimization for Cooperative Downlink MIMO Systems

Herein, we propose a joint optimization algorithm to minimize the overall transmit power in multicell multiuser multiple-input multiple-output (MIMO) systems for B5G. The proposed algorithm is an iterative optimization algorithm that jointly computes the transmit/receive beamforming vectors and the transmit power. The superiority of the proposed method is demonstrated through simulations.

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.

Deep learning-based transceiver design for multi-user MIMO systems

Multi-user multiple-input multiple-output (MIMO) is a key technique to increase both the channel capacity and the number of users that can be served simultaneously. One of the main challenges related to the deployment of such systems is the complexity of the transceiver processing. Although the conventional optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In contrast to existing work, we study a DL-based transceiver design scheme for a downlink MIMO broadcasting channel (MIMO BC) system, which consists of a base station (BS) serving multi-users. The objective of this work is to maximize the sum-rate of all users by jointly optimizing the transmitter and receivers under the total power constraint, while suppressing interference as much as possible. Due to the inter-user interference in such system, the considered problem is nonconvex and NP-hard. Different from traditional optimization algorithms, we rely on the convolutional neural networks (CNNs) to optimize the transceivers in an adaptive way. In the proposed scheme, we develop an unsupervised learning strategy, where a loss function is constructed innovatively for reducing the inter-user interference. Simulation results show that the inter-user interference is reduced effectively by our proposed CNN-based transceiver optimization method.

Artificial-Noise-Aided Space–Time Line Code for Enhancing Physical Layer Security of Multiuser MIMO Downlink Transmission

We consider secure downlink multiple-input–multiple-output (MIMO) communications between a transmitter and multiple users when there exists a passive eavesdropper. A downlink main channel between the transmitter and a user is estimated through uplink pilot symbols in a time-division duplex mode. On the other hand, to hinder an eavesdropper from estimating the eavesdropper channel between the transmitter and the eavesdropper, the transmitter does not send any pilot symbols. The main channel is hence available at the transmitter only, while the eavesdropper channel is unavailable at any node. Assuming these channel state information (CSI) conditions, we propose a MIMO transmission method using space–time line code (STLC) with artificial noise (AN) for enhancing the physical layer security. The STLC enables noncoherent detection at the receiver, i.e., a user, without CSI and achieves full spatial diversity. We derive the lower bound of the secrecy sum rate for the proposed STLC transmission method without AN, with AN, and with AN and imperfect CSI. Moreover, numerical results verify that the proposed STLC scheme outperforms the conventional eigen-beamforming and the precoding space-time block code in terms of secrecy sum rate regardless of the signal-to-noise ratio, the number of transmit antennas, and the CSI uncertainty.

Performance Evaluation of MU-MIMO Transmissions with Joint Interference Constraint in HetNet

In this paper, we investigate the per-tier outage probability of multiuser multiple-input multiple-output (MU-MIMO) transmissions in heterogeneous networks (HetNets) with joint interference constraint. In the tier of cellular cell, user equipment (UE) is required to report measured channel information and the base station (BS) adopts ZF-based precoding MU-MIMO transmission to achieve multiuser diversity gain. With the constraint of cross-tier interference and unpredicted inter-beam interference, we derive the closed-form expression of outage probability of downlink MU-MIMO transmissions. Considering the capacity of nodes in the tier of ad hoc networks, a max-SINR scheduler and codebook-based MU-MIMO transmission are employed. The scheduler selects the best receiving nodes for each beam in predefined codebook according to measured signal to interference plus noise ratio (SINR), and the transmitting node performs data transmissions using orthogonal beams. In the presence of inter-node interference, inter-beam interference, and cross-tier interference, we obtain the closed-form expression of outage probability of MU-MIMO transmissions when downlink or uplink transmissions occur in cellular cell. Additionally, in case that the outage probability in ad hoc networks should satisfy quality of service (QoS) requirement, a restricted area in cellular cell in which the outage probability in ad hoc networks is not greater than a required threshold is explored. Numerical results show that the unpredictable inter-beam interference in cellular cell degrades the outage probability slightly. The restricted area increases with the outage probability threshold.

Coverage Analysis of Downlink MU-MIMO Cellular Networks

In this letter, we analyze the coverage performance of multi-user multiple-input multiple-output (MU-MIMO) downlink cellular networks. We model the locations of the base stations and mobile users using Poisson Point Processes and employ maximum ratio transmission precoding at the base station. Our analysis builds upon Prony’s method to approximately calculate the coverage probability using the sum of exponentials which facilitates the computation of Laplace transform of both intra-cell and inter-cell interference. We demonstrate the accuracy of derived approximate coverage probability expressions through numerical simulations for different MU-MIMO configurations.

Coalition Formation Games for Improved Cell-Edge User Service in Downlink NOMA and MU-MIMO Small Cell Systems

In today’s wireless communication systems, the exponentially growing needs of mobile users require the combination of new and existing techniques to meet the demands for reliable and high-quality service provision. This may not always be possible, as the resources in wireless telecommunication systems are limited and a significant number of users, usually located at the cell edges, can suffer from severe interference. For this purpose, a new Joint Transmission Coordinated Multipoint (JT-CoMP) scheme, in which the transmission points’ clustering is based on a coalition formation game, is deployed alongside with Non-Orthogonal Multiple Access (NOMA) in a Cloud Radio Access Network (C-RAN) consisting of small cells. To further enhance the network’s performance, Multiuser Multiple-Input Multiple-Output (MU-MIMO) with Zero-Forcing (ZF) beamforming is applied. The proposed scheme’s performance, in terms of user throughput, is then compared to that of a scheme where JT-CoMP with static clustering is selected, a JT-CoMP scheme with clustering based on a greedy algorithm and a scenario where JT-CoMP is not deployed. Simulation results confirm that the proposed scheme reliably improves the throughput of users with poor wireless channels while guaranteeing that the performance of the rest is not severely undermined.

A Downlink Multiuser MIMO–OFDM System With Nonideal Oscillators and Amplifiers: Characterization and Performance Analysis

In this article, we analytically investigate the performance of a downlink multiuser (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. Specifically, we focus on the joint impact of nonideal oscillators and nonlinear high-power amplifiers, which can be quantified in terms of multiplicative and additive nonlinear distortions as well as phase noise, which causes inter-channel interference (ICI). We first derive the signal-to-distortion-plus-interference-plus-noise ratio (SDINR) and then utilize the SDINR to derive the closed-form expressions for symbol-error-rate and the average capacity of the aforementioned system. Our analysis shows that the statistics of an additive nonlinear distortion and additive white Gaussian noise are not affected by phase noise. However, the phase noise itself significantly deteriorates the performance of the MU-MIMO-OFDM system even in the absence of the nonlinear distortions. We further show that nonlinear distortion and ICI cause an irreducible error floor which considerably degrades the system error performance and also adversely affects the total capacity of the MU-MIMO-OFDM system. Comparisons are drawn between the performance of an ideal MU-MIMO-OFDM system and a MU-MIMO-OFDM system in presence of above impairments. In the end, we present the simulation results to validate the efficacy of our theoretical analysis.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

Joint Data and Pilot Power Allocation for Massive MU-MIMO Downlink TDD Systems

This brief investigates the jointly optimal pilot and data power allocation among the users in a massive multiuser multiple-input multiple-output downlink system, in which the base station is equipped with a massive number of antennas and simultaneously serves single-antenna users in the same frequency band. First, a closed-form lower bound on the achievable rate is derived for each user where maximum-ratio transmission precoding is employed, and this closed form lower bound is used in defining the spectral efficiency (SE). Then, in order to maximize the SE, a method of power allocation among each of the pilot and data symbols of all the users is proposed, given the total power budget per coherence interval for all users. Numerical results show that the proposed power allocation scheme is superior to other existing methods in terms of higher SE.

Interference Cooperation via Distributed Game in 5G Networks

Nash noncooperative power game is an effective method to implement interference cooperation in downlink multiuser multiple-input multiple-output (MU-MIMO). Power equilibrium point of Nash noncooperative power game can achieve a satisfactory tradeoff between self-benefits of Internet of Things (IoT) users and interference between IoT users which largely enhance the edge IoT user throughput. However, either power strategy space, i.e., the enabled range of power allocation for IoT users, or overall BS transmit power in the existing Nash noncooperative power games is generally static. This limits the performance of systems, especially in IoT systems, etc., in 5G. As an effort to address these problems, we design a novel framework of Nash noncooperative game with iterative convergence for downlink MU-MIMO. We first decompose the MU-MIMO into multiple virtual single-antenna transmit-receive pairs with a stream analytical model. Afterwards, based on streams, we propose a noncooperative water-filling power game with pricing (WFPGP) where the power strategy space of each stream can be dynamically determined by iterative water-filling . We derive the sufficient condition for the existence and uniqueness of WFPGP game, in which the verification of the sufficient condition can be executed in a distributed manner. By simulations, we verify the performance of WFPGP compared to other Nash noncooperative games.

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.

NOMA-Aided Precoded Spatial Modulation for Downlink MIMO Transmissions

In this paper, a novel multi-user multiple-input multiple-output (MIMO) transmission scheme, called non-orthogonal multiple access (NOMA) aided precoded spatial modulation (PSM) (NOMA-PSM) is proposed for overloaded downlink transmissions. NOMA-PSM beneficially amalgamates the concept of index modulation (IM) and NOMA techniques, and therefore it inherits both the merits of IM with low-complexity transceiver and the advantages of NOMA with high bandwidth efficiency. For the proposed scheme, we develop a pair of low-complexity yet effective detection algorithms by combining the spatial index demodulation and successive interference cancelation. The spectral efficiency (SE), implementation cost, and multi-user interference of NOMA-PSM are evaluated and compared with conventional designs. Furthermore, we derive the mutual information (MI) of the proposed NOMA-PSM to characterize its achievable SE and also obtain a lower bound for simplifying the measurement of MI. Our simulation results show that the proposed NOMA-PSM scheme is capable of achieving considerable performance gains over conventional orthogonal multiple access aid PSM and antenna-grouping-based PSM in wireless MIMO fading channels.

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.

A New Approach to User Scheduling in Massive Multi-User MIMO Broadcast Channels

In this paper, a new two-phase-feedback-based user-scheduling-and-beamforming method is proposed for multi-user multiple-input multiple-output downlink in the context of two-stage beamforming. The key ideas of the proposed method are: 1) to use a set of orthogonal reference beams and construct a cone around each reference beam to select “nearly-optimal” semi-orthogonal users based only on channel quality indicator feedback; and 2) to apply post-user-selection beam design with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increase. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF that requires full CSI for all users, with a significantly reduced amount of required channel information which is even less than that required by random beamforming.

On modeling and performance analysis of non-cooperative multi-antenna multi-user MIMO systems

This work presents modeling and performance analysis of beamforming in non-cooperative multi-user multiple-input multiple-output (MU-MIMO) cellular systems using stochastic geometry based abstraction models. Particularly, a downlink MU-MIMO system is investigated in which an array of antenna elements at the base stations (BS) serves several user devices. We derive the closed-form expression of the complimentary cumulative distribution function (CCDF) of signal-to-interference-plus-noise ratio (SINR) representing the coverage probability for orthogonal pilot sequences and thereby characterize a number of related special cases. Moreover, closed-form expressions are also obtained for the CCDF of signal-to-interference ratio (SIR) for non-orthogonal pilot sequences and we also derive useful special cases of it and identify the support region of our analysis. Lastly, we define the numerical expression of ergodic capacity and utilize a partial fraction expansion to solve a simple form. Our contribution in this work is twofold, we not only provide a pedagogical tutorial-like exposition of stochastic geometry technique to model terrestrial networks, but also augment the existing literature in the field with aspects of frequency reuse factor, an indicator function for pilot usage, and the effect of pilot contamination. Simulation results are presented to support the theoretical part.

Iterative Hybrid Precoder and Combiner Design for mmWave Multiuser MIMO Systems

This letter investigates analog/digital hybrid precoder and combiner design for millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems. We propose a novel iterative algorithm for the joint hybrid precoder and combiner design by exploiting the duality of the uplink and downlink MU-MIMO channels. For the initialization stage, the proposed algorithm selects the analog precoder-combiner pair based on the orthogonal matching pursuit (OMP) algorithm to enhance the channel gain, while mitigating the multiple access interference, and obtains the digital combiner via minimum mean square error criterion. Then, the proposed algorithm enhances the performance gradually at each iteration via joint design of the uplink and downlink precoders/combiners. Simulation results demonstrate the significant performance advantages of the proposed precoder and combiner design compared with existing hybrid beamforming algorithms.