Uplink Multiuser Multiple-input Multiple-output Systems Research Articles

Spectral decomposition-based optimization for MMSE receiver design in an uplink MU-MIMO cooperative communication system with imperfect CSI

The receiver’s architecture is always a critical issue in multiple-input multiple-output (MIMO) systems, as it has a great impact on the system’s performance and complexity. Accordingly, in this paper, a spectral decomposition-based minimum mean squared error (MMSE) receiver is proposed in an uplink multi-user MIMO (MU-MIMO) system with a full-duplex decode and forward relay. Imperfect channel state information is assumed at the relay and base station where, least-squares channel estimation technique is applied due to its simplicity and low complexity. An equivalent relay is applied for self-interference cancellation. While, multi-user interference is mitigated by using the block diagonalization concept. A closed-form expression for the mean squared error (MSE) of the estimated data symbol is derived in the presence of channel estimation error. Moreover, an optimization problem is solved to derive an expression for the proposed MMSE receiver’s matrix. Finally, the performance of the proposed receiver is compared to that of the conventional receiver and Zero-forcing (ZF) receiver. Results show the superiority of the proposed receiver over both receivers.

Graph Neural Network Aided MU-MIMO Detectors

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems.

Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.

Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

In this paper, we propose two deep learning (DL) based receiver schemes in uplink multiple-input multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO receiver using a data-driven full connected neural network. This data-driven receiver can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel. In the second scheme, we adopt a model-driven network which combines communication knowledge with DL. The model-driven scheme divides the MIMO receiver into channel estimation subnet and signal detection subnet, and each subnet is composed of a traditional solution as initialization and a DL network to further improve the accurate. The simulation results show that both of the two schemes achieve better bit error ratio (BER) performance than traditional methods. In particular, the data-driven scheme can achieve optimal BER performance in low-dimensional MIMO systems, while the model-driven scheme can be trained with fewer trainable parameters and outperforms the data-driven scheme in high-dimension MIMO systems.

Multi-User Millimeter Wave MIMO With Full-Dimensional Lens Antenna Array

Millimeter wave (mmWave) communication by utilizing lens antenna arrays is a promising technique for realizing cost-effective 5G wireless systems with large MIMO (multiple-input multiple-output) but only limited radio frequency (RF) chains. This paper studies an uplink multi-user mmWave single-sided lens MIMO system, where only the base station (BS) is equipped with a full-dimensional (FD) lens antenna array with both elevation and azimuth angle resolution capabilities, and each mobile station (MS) employs the conventional uniform planar array (UPA) without the lens. By exploiting the angle-dependent energy focusing property of the lens antenna array at the BS as well as the multi-path sparsity of mmWave channels, we propose a low-complexity path division multiple access (PDMA) scheme, which enables virtually interference-free multi-user communications when the angle of arrivals (AoAs) of all MS multi-path signals are sufficiently separable at the BS. To this end, a new technique called path delay compensation is proposed at the BS to effectively transform the multi-user frequency-selective MIMO channels to parallel frequency-flat small-size MIMO channels for different MSs, for each of which the low-complexity single-carrier(SC) transmission is applied. For general scenarios with insufficient AoA separations, analog beamforming at the MSs and digital combining at the BS are jointly designed to maximize the achievable sum-rate of the MSs based on their effective MIMO channels resulting from path delay compensation. In addition, we propose a new and efficient channel estimation scheme tailored for PDMA, which requires negligible training overhead in practical mmWave systems and yet leads to comparable performance as that based on perfect channel state information (CSI).

Low Complexity ZF and MMSE Detectors for the Uplink MU-MIMO Systems With a Time-Varying Number of Active Users

The classical multiuser detection algorithms such as zero forcing (ZF) and minimum mean square error (MMSE) receivers are designed with the assumption that the number of active users is constant and known. When the number of the active users changes, the receiver may exhibit a serious performance loss if it does not react quickly to such variations. In this paper, we address the problem of reducing the complexity of the reevaluation of the popular ZF and MMSE detectors for multiuser multiple-input multiple-output (MU-MIMO) systems with a time-varying number of users in the channel. For each technique, we propose a detection approach with low complexity and without performance loss. The proposed algorithms avoid the direct computation of matrix inverses required by the ZF and MMSE detectors. Moreover, the performance losses, due to the use of the ZF and MMSE detectors intended for the scenario with a fixed number of active users, are evaluated with Monte Carlo simulation results.

Design of Robust Transceiver for Precoded Uplink MU-MIMO Transmission in Limited Feedback System

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.

Multiuser MIMO Transceiver Design for Uplink and Downlink with Imperfect CSI

This paper considers a joint linear transmitter and receiver design for multi-user multiple-input multiple-output (MU-MIMO) systems using total mean square error (TMSE) criterion, subject to a total transmit power constraint assuming imperfect channel state information. Both the uplink and downlink MU-MIMO systems, which is employed with improper constellations such as binary phase shift-keying and $$M$$ M -ary amplitude shift-keying are considered. A minimum TMSE design is formulated as a nonconvex optimization problem under a total transmit power constraint and the closed-form optimum linear precoder and decoder for both the downlink and uplink MU-MIMO systems with improper modulation are determined by solving this nonconvex optimization problem. A novel contribution in this paper is to derive a closed-form optimum linear precoder and decoder for both the downlink and uplink MU-MIMO systems with improper modulation by solving the nonconvex optimization problem under total power constraint. The simulation results show that the performance of the proposed design is improved over the previous design.

Distributed user selection scheme for uplink multiuser MIMO systems in a multicell environment

Abstract We propose an interference-aware user selection scheme for uplink multiuser multiple-input multiple-output systems in a multicell environment. The proposed scheme works in a distributed manner. Each mobile station determines its transmit beamforming vector based on the locally available channel state information, and informs the associated base station (BS) of the amount of potential interference caused to adjacent cells along with the resulting beamforming vector. Then, the BS selects a set of users to be served simultaneously with consideration of intercell interference. The user selection scheme is devised either to maximize the sum rate or to achieve proportional fairness among users. For each case, we derive an optimal user selection criterion and propose a suboptimal distributed user selection algorithm with low complexity. Simulation results confirm that the proposed scheme offers significant throughput enhancement due to reduction of the intercell interference in a multicell environment.

Frequency-domain equalization for OFDMA-based multiuser MIMO systems with improper modulation schemes

In this paper, we propose a novel transceiver structure for orthogonal frequency division multiple access-based uplink multiuser multiple-input multiple-output systems. The numerical results show that the proposed frequency-domain equalization schemes significantly outperform conventional linear minimum mean square error-based equalizers in terms of bit error rate performance with moderate increase in computational complexity.

Joint Adaptive Modulation and Power Allocation for Multiuser MIMO Systems with Reduced-Feedback

This paper presents an adaptive modulation and power allocation method for uplink multiuser multiple-input multiple-output systems under the assumption that there is perfect channel information at the receiver but not at the transmitter. The receiver jointly optimizes the power level and modulation order for all users under constraints of transmit power and error requirements, and returns these information to each user via a low-rate feedback channel. Power and modulation optimization maximizes the total throughput under a tolerable bit error ratio for each user. The ellipsoid method is used to design efficient algorithms for optimal power and modulation level.

Joint linear precoder and power allocation design for uplink MIMO systems with limited feedback

In this article, the authors consider joint design of a linear precoder and power allocation for uplink multiuser multiple input multiple output (MIMO) communication systems with limited feedback to improve the bit error rate (BER) performance for all users. Precoder selection from the codebook set is directly based on the exact BER performance, instead of other suboptimal criteria, to achieve the optimal precoder matrix, but closed-form expressions may not exist in the view of power allocation based directly on the BER criterion. From this perspective, the authors propose the joint transmitter optimization algorithm for the consideration of precoder design, with total power constraint for asymptotic MBER (AMBER) criterion. In this AMBER criterion, a closed-form solution has been derived for power allocation with an optimal precoder. The simulation results show that the proposed joint design algorithm can achieve a much better performance than precoding with uniform power allocation and only consideration of power allocation.

A low complexity user scheduling algorithm for uplink multiuser MIMO systems

A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-input multiple-output (MIMO) multiuser system. Compared with the existing scheduling algorithms, our algorithm is not only more efficient but also converges to within 99% of the optimal capacity obtained by exhaustive search. We demonstrate the convergence of the proposed scheduling algorithm and study the tradeoff between its complexity and performance.

New Robust Beamforming Method for Frequency Offsets in Uplink Multiuser OFDM-MIMO

Multiuser multiple input multiple output (MU-MIMO) systems are attracting attention due to their frequency efficiency. However, in uplink MU-MIMO systems, different frequency offsets among multiple mobile stations (MSs) significantly degrade the transmission quality, especially when orthogonal frequency division multiplexing (OFDM) is used. In this paper, the influence of these frequency offsets is first analyzed in a frequency selective fading environment. Numerical analysis shows that an error floor occurs in the bit error rate and the influence of the frequency offset becomes larger in short delay spread environments. To overcome this problem, a new beamforming method is proposed to compensate for the frequency offset by introducing an auto frequency controller after frequency-space equalization in each data stream. The effect of the proposed method is evaluated in a frequency selective fading environment by computer simulations and measured results.