Multiuser Multiuser Multi-input Multi-output Research Articles

Closed-Form Capacity Reliability Analysis of Multiuser MIMO System in the Presence of Generalized Multipath Fading.

This research studies the problem of a joint capacity/capacity reliability analysis of the multiuser multi-input multioutput (MIMO) system functioning in the presence of generalized multipath fading. The study presents the derived results of the closed-form analytical statistical description of the ergodic sum-rate capacity, the capacity reliability and the capacity's higher-order statistics in the case of complex Nakagami-m distributed channel transmission coefficients. A numerical verification of the derived expressions was performed, and it demonstrated excellent correspondence with the simulation. The system performance was evaluated with the help of a numerical analysis of the joint first- and second-order statistics description, depending on the channel and system parameters. The results demonstrated several peculiarities, e.g., the existence of a specific extremum of the capacity reliability for small-sized MIMO systems, its opposite behavior (in terms of the varying number of antenna elements) for heavy and light fading, and the existing asymptotic regions of the system and the channel parameters.

Regularized artificial fast fading designs for secure transmission in multi-user MIMO wiretap channels

In this paper, we investigate a regularized artificial fast fading (AFF) design for multi-user multi-input multi-output (MIMO) wiretap channels. To solve the effective channel gain reduction issue in conventional zero-forcing (ZF) based approaches, we propose a regularized AFF beamforming method by applying the minimum mean squared error (MMSE) problem and Lagrangian dual analysis. Finally, simulation results show that the proposed MMSE based AFF scheme improves the error performance of the legitimate user compared to the conventional ZF AFF method while achieving the same low probability of interception at the eavesdropper for multi-user MIMO secure transmission systems.

A User Cooperation Approach for Interference Cancellation in FDD Massive MIMO Systems

The performance of a massive multi-user Multi-Input Multi-Output (MIMO) system, operating in Frequency Division Duplex (FDD) mode, severely degrades under imperfect Channel State Information (CSI). Among the main challenges toward the acquisition of sufficiently accurate CSI at the transmitter is the issue of enormous CSI feedback overhead. In this paper, a novel interference cancellation strategy is proposed to alleviate the overhead. The concept of a device-to-device based interference cancellation strategy was hinted in some prior works but has not been fully exploited in the multi-user MIMO systems, especially when the number of antennas becomes large. Hence, this paper aims to exploit the potential of User Equipment (UE) cooperation to reduce the dependency of precoder at the transmitter to the accuracy of CSI. To do so, adjacent pieces of UE that experience correlated CSI are clustered in a similar group, jointly adjusting their receive antenna combining the weight vector to maximize the channel vector orthogonality. Simulation results show that the proposed strategy reduces the dependency of system performance on the accuracy of CSI feedback; moreover, compared to the conventional limited feedback strategy, a larger number of antennas can be deployed at the transmitter.

A Machine Learning Approach to Blind Multi-Path Classification for Massive MIMO Systems

This paper concerns the problem of the multi-path classification in a multi-user multi-input multi-output (MIMO) system. We propose a machine learning approach to achieve a blind multi-path classification in the uplink (UL) scheme of a multi-user massive MIMO system. Note that the “blind” term in our approach represents the achievement of the multi-path classification without different pilot sequences on different users, without prior channel state information (CSI) at each user, and without any exploiting the special properties of the received signal. Specifically, our approach consists of two phases. In the first phase, multiple users transmit communication-requests to the base station (BS) for message transmission. The BS only estimates the scaled large-scale path loss of each user, which is determined by the distance between the transmitter and the receiver and is independent of the number of multi-path. Then, the BS compares the difference of the scaled large-scale path loss between any two users. If the difference is sufficiently large, the BS notifies all users to permit their simultaneous message transmissions. However, if the difference is small, the BS notifies each user to slightly adjust their transmission power and then permits their simultaneous message transmissions as well. In the second phase, all users simultaneously transmit their messages using the same radio resource. Then the BS selects one predetermined constellation point from the received pilot symbols as the input of clustering algorithms. According to the clustering results, the BS classifies each multi-path into a specific user. The key intuition of our approach is that the clustering algorithms can generate multiple cluster centroids and each cluster centroid represents the average reception power of each user. Moreover, we use a weighted ensemble clustering algorithm to further improve the performance of our approach. We implemented our approach and conducted extensive performance comparison. Our experimental results show that, when the received signal-to-noise ratio (SNR) is more than 13 dB, our approach with the weighted ensemble clustering algorithm can correctly classify all multi-path to the corresponding user and the output SNR can be improved by 3.2 dB, where we consider three users in an 8PSK system and each user possess 50 multi-path.

Spatial overlapping index based joint beam selection for millimeter-wave multiuser MIMO systems

In this paper, beam selection for millimeter-wave multiuser multi-input multi-output (MIMO) systems that employ zero-forcing based analog-digital hybrid processing is investigated. The existing approaches have tackled neither the joint beam selection at both sides of the communication system nor the strong interference imposed by the mobile stations (MSs) which share beams with some other MSs. Hence, by first assuming full channel state information, the problem of joint beam selection is formulated and the concept of spatial overlapping index (SOI) is proposed to deal with the strong interference imposed by the MSs which share beams with some other MSs. Furthermore, by considering the realistic scenario of having channel estimation, the joint beam selection is divided and integrated into the channel estimation process, and the SOI is redefined based on the channel estimates. Finally, our analysis and simulations show that the upper bound of the sum rate increases with the number of MSs and the number of base station antennas, the proposed approach outperforms the existing approaches, and the computational complexity of the proposed approach is close to or lower than that of the existing approaches.

Analytical Characterization of Dual-Band Multi-User MIMO-OFDM System with Nonlinear Transmitter Constraints

Recent studies have shown that carrier-aggregated multi-user (MU) multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can deliver unprecedented improvements in network throughput and capacity when compared with existing systems. However, most of these studies fail to take into account the nonlinear constraints that limit the performance of such systems. This paper proposes an analytical framework to evaluate the end-to-end performance of a carrier-aggregated dual-band (DB) MU-MIMO-OFDM system in the presence of transmitter nonlinearity. Specifically, we derive the closed-form expression for signal-to-interference-plus-noise ratio (SINR) and further utilize this expression to evaluate the average capacity and symbol error rate of a nonlinear DB MU-MIMO-OFDM system in a multi-path Rayleigh fading channel. We show that the transmit preprocessing technique conventionally used to mitigate inter-user interference in a linear DB MU-MIMO-OFDM system fails to eliminate inter-user interference in a nonlinear DB MU-MIMO-OFDM system. As a consequence, there is an irreducible error floor which is function of number of aggregated bands as well as number of users present in the system. The irreducible error floor has been shown to considerably deteriorate the performance of nonlinear DB MU-MIMO-OFDM system. Simulation results are also reported to validate the efficacy of the proposed analysis.

Interference suppression algorithm based R-TDD for multi-user MIMO heterogeneous network

Aiming at the problem of uplink co-channel interference in multi-user multi-input multi-output (MIMO) heterogeneous network, a reversed time division duplex (R-TDD)-based method is used to improve the performance of interference rejection combining (IRC) algorithm. This method overcomes the difficulty of IRC algorithm in obtaining the interference noise covariance matrix by utilizing the R-TDD transmission scheme. On the premise that the spectrum efficiency is guaranteed, through combining R-TDD with spatial modulation, the transmission complexity is significantly reduced and the uplink co-channel interference is effectively controlled, so as to improve the system performance. Simulation results demonstrate that the R-TDD-based scheme can improve the performance of IRC algorithm in terms of spectrum efficiency.

A Nonlinear Downlink Multiuser MIMO-OFDM Systems

This letter analyzes the impact of transmitter nonlinearity on a multiuser (MU) multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. A single value decomposition-based multiuser decomposition technique has been used to mitigate the interference between different users. It has been shown that the nonlinear distortion caused due to transmitter nonlinearity considerably degrades the performance of MU-MIMO-OFDM system and is a function of number of users. Further, this nonlinear distortion also introduces an irreducible error floor thus limiting the gains of MU-MIMO-OFDM system as compared to single user MIMO-OFDM system.

A simple block diagonal precoding for multi-user MIMO broadcast channels

Abstract The block diagonalization (BD) is a linear precoding technique for multi-user multi-input multi-output (MIMO) broadcast channels, which is able to completely eliminate the multi-user interference (MUI), but it is not computationally efficient. In this paper, we propose the block diagonal Jacket matrix decomposition, which is able not only to extend the conventional block diagonal channel decomposition but also to achieve the MIMO broadcast channel capacity. We also prove that the QR algorithm achieves the same sum rate as that of the conventional BD scheme. The complexity analysis shows that our proposal is more efficient than the conventional BD method in terms of the number of the required computation.

Max-Min Fairness Linear Transceiver Design for a Multi-User MIMO Interference Channel

Consider the max-min fairness linear transceiver design problem for a multi-user multi-input multi-output (MIMO) interference channel. When the channel knowledge is perfectly known, this problem can be formulated as the maximization of the minimum signal-to-interference-plus-noise ratio (SINR) utility, subject to individual power constraints at each transmitter. We prove in this paper that, if the number of antennas is at least two at each transmitter (receiver) and is at least three at each receiver (transmitter), the max-min fairness linear transceiver design problem is computationally intractable as the number of users becomes large. In fact, even the problem of checking the feasibility of a given set of target SINR levels is strongly NP-hard. We then propose two iterative algorithms to solve the max-min fairness linear transceiver design problem. The transceivers generated by these algorithms monotonically improve the min-rate utility and are guaranteed to converge to a stationary solution. The efficiency and performance of the proposed algorithms compare favorably with solutions obtained from the channel matched beamforming or the leakage interference minimization.

Multi-User Downlink System Design with Bright Transmitters and Blind Receivers

This paper considers a scenario for multi-user multi-input multi-output (MIMO) communication systems when perfect channel state information at the transmitter (CSIT) is given while the equivalent channel state information at the receiver (CSIR) is not available. Such an assumption is valid for the downlink multi-user MIMO systems with linear precoders that depend on channels to all receivers. We propose a concept called dual systems with zero-forcing design based on the duality principle, originally proposed to relate the Gaussian multi-access channel (MAC) and the Gaussian broadcast channel (BC). For the K-user N×2 MIMO BC with N antennas at the transmitter and two antennas at each of the K receivers, we design a downlink interference cancellation (IC) transmission scheme by obtaining the dual of uplink MAC systems employing IC methods. The transmitter simultaneously sends K precoded Alamouti codes, one for each user. Each receiver zero-forces the unintended user's Alamouti codes and decouples its own data streams using two simple linear operations independent of CSIR. Analysis shows that the proposed scheme achieves a diversity gain of 2(N - K + 1) for equal energy constellations with short-term power and rate constraints. Power allocation among K users can also be performed to improve the array gain but not the diversity gain. Numerical results demonstrate that the bit error rate of the downlink IC scheme has a diversity gain improvement compared to the block diagonalization (BD) method, which requires global channel information at each node.

A MMSE Vector Precoding with Block Diagonalization for Multiuser MIMO Downlink

Block diagonalization (BD) algorithm is a generalization of the channel inversion that converts multiuser multi-input multi-output (MIMO) broadcast channel into single-user MIMO channel without inter-user interference. In this paper, we combine the BD technique with a minimum mean square error vector precoding (MMSE-VP) for achieving further gain in performance with minimal computational overhead. Two key ingredients to make our approach effective are the QR decomposition based block diagonalization and joint optimization of transmitter and receiver parameters in the MMSE sense. In fact, by optimizing precoded signal vector and perturbation vector in the transmitter and receiver jointly, we pursue an optimal balance between the residual interference mitigation and the noise enhancement suppression. From the sum rate analysis as well as the bit error rate simulations (both uncoded and coded cases) in realistic multiuser MIMO downlink, we show that the proposed BD-MVP brings substantial performance gain over existing multiuser MIMO algorithms.

Uplink Capacity of OFDM Multi-User MIMO Using Near-ML Detection in a Cellular System

Multi-user multi-input multi-output (MIMO) system has been attracting much attention due to its high spectrum efficiency. Non-linear MIMO signal detection methods with less computational complexity have been widely studied for single-user MIMO systems. In this paper, we investigate how a lattice reduction (LR)-aided detection and a maximum likelihood detection (MLD) employing the QR decomposition and M-algorithm (QRM-MLD), which are commonly known as non-linear MIMO signal detection methods, improve the uplink capacity of a multi-user MIMO-OFDM cellular system, compared to simple linear detection methods such as zero-forcing detection (ZFD) and minimum mean square error detection (MMSED). We show that both LR-aided linear detection and QRM-MLD can achieve higher uplink capacity than simple linear detection at the cost of moderate increase of computational complexity. Furthermore, QRM-MLD can obtain the same uplink capacity as MLD.

Performance Analysis of Multi-user Multi Input Multi Output- Interleave-Division Multiple-Access System Employing Turbo Coding with Multi-User Detection over Frequency-Selective Wireless Communication Channel

Problem statement: This study presents the performance analysis of multi-user Multi Input Multi Output (MIMO) assisted interleave based multiple-access system. In IDMA, different interleavers are used to distinguish users as against different signature sequence in a conventional code-Division Multiple-Access (CDMA) scheme. Approach: The basic principle of IDMA is that the interleaver is unique for the users. Results: In this study, we consider that Interleavers are generated independently and randomly. Also the IDMA technique is extended to multi user MIMO IDMA with multi-user detection. At the receiver, OSIC detector is realized using ZF for frequency fading channel to combat MAI and MUI problem. The performance of the system is analyzed for different channel conditions using extensive simulation runs based on Monte Carlo simulation trials. Conclusion: It is shown that the IDMA scheme can achieve near single user performance in situations with very large numbers of users while maintaining very low receiver complexity. It is discerned from the computer simulation results that IDMA outperforms CDMA in frequency selective channel for high load conditions.

Performance of Block Diagonalization Scheme for Downlink Multiuser MIMO System with Estimated Channel State Information

In this paper, we study the performance of block diagonalization (BD) scheme for a downlink multiuser multi-input multi-output (MIMO) system with imperfect channel state information (CSI). At each mobile user, information about the channel is obtained by applying the minimum mean-square-error (MMSE) channel estimation method. The channel state information is fed back to the base station through error-free uplink channels. A theoretical analysis is performed showing that channel estimation errors contribute to co-channel interferences thus deteriorating sum rate capacity. Computer simulations are performed to evaluate the impact of channel estimation errors on the sum rate capacity. The results show that if the MSE of the channel estimation is not less than 10-2, the impact of channel estimation errors is significant and cannot be neglected. To combat this adverse effect, a proper transmit power level is required for the training signals.

Generalized Channel Inversion Methods for Multiuser MIMO Systems

Block diagonalization (BD) is a well-known precoding method in multiuser multi-input multi-output (MIMO) broadcast channels. This scheme can be considered as a extension of the zero-forcing (ZF) channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users and transmit antennas at low and medium signal-to-noise ratio regime, since the complete suppression of multi-user interference is achieved at the expense of noise enhancement. Also it performs poorly under imperfect channel state information. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. We first introduce a generalized ZF channel inversion algorithm as a new approach of the conventional BD. Applying this idea to the MMSE channel inversion for identifying orthonormal basis vectors of the precoder, and employing the MMSE criterion for finding its combining matrix, the proposed scheme increases the signal-to-interference-plus-noise ratio at each user's receiver. Simulation results confirm that the proposed scheme exhibits a linear growth of the sum rate, as opposed to the BD scheme. For block fading channels with four transmit antennas, the proposed scheme provides a 3 dB gain over the conventional BD scheme at 1% frame error rate. Also, we present a modified precoding method for systems with channel estimation errors and show that the proposed algorithm is robust to channel estimation errors.