Uplink Multiuser Multiple-input Multiple-output Research Articles

A sum rate maximization problem in uplink MIMO with RSMA systems

This study explores the problem of maximizing the sum rate in uplink multi-user Multiple-Input Multiple-Output (MIMO) using Rate-Splitting Multiple Access (RSMA) systems. The investigation revolves around the scenario where the Users (UEs) are single-antenna nodes transmitting data to a multi-antenna Base Station (BS) through the RSMA technique. The optimization process encompasses determining parameters such as UEs’ transmit powers, decoding order, and detection vector at the BS. An approach based on Deep Reinforcement Learning (DRL) is introduced to address this challenge. This DRL framework involves an action-refined stage and applies a Deep Deterministic Policy Gradient (DDPG)-based strategy. Simulation outcomes effectively demonstrate the convergence of the proposed DRL framework, where it converges after approximately 1,800 episodes. Also, the results prove the superior performance of the proposed method when compared to established benchmark strategies, where it is up to 45% and 86% higher than the local search and random schemes, respectively.

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.

Spatial Multiplexing Superimposed Pilots for Multicell Multiuser MIMO Uplink Systems

For a multiple-input multiple-output (MIMO) system, or a multiuser MIMO system, it is important to acquire the accurate channel state information. In this article, a superimposed-pilot-based channel estimation scheme is proposed for multicell multiuser MIMO uplink systems. In this scheme, the pilot sequences and data sequences are superimposed, but the space generated by the pilot sequences and the space spanned by data sequences are separated far enough in order to reduce interference. Based on this scheme, the minimum mean-square error channel estimation is deduced, and the optimal power allocation between the pilots and the data signals is derived to maximize throughput. These results show that our proposed scheme can overcome the pilot contamination and achieve better performance than the traditional time-multiplexing scheme with mutually orthogonal pilot sequences for different users when the coherent time slot is limited.

Multiuser MIMO Uplink Transmission With Electromagnetic Exposure Constraints: Spectral Efficiency and Energy Efficiency Tradeoff

Along with the relentless development of wireless communications, the electromagnetic (EM) radiation emitted by wireless devices has gradually turned into a significant concern of the public. A growing number of government departments and communication regulatory agencies require that EM radiation be limited to a suitable level with the measurement of specific absorption rate (SAR). Meanwhile, both spectral efficiency (SE) and energy efficiency (EE) are key indicators of wireless transmission systems, so that they generally need to be considered at the same time. To this end, we investigate the resource efficiency (RE), which characterizes the weighted sum of SE and EE, in uplink multiuser multiple-input multiple-output (MIMO) transmission with EM constraints. We first simplify the RE calculation by applying its deterministic equivalents (DEs) and deal with the objective function by the quadratic transform algorithm. Then, a modified RE maximization water-filling algorithm is proposed for the precoder design at the users. Numerical results indicate the superior performance of the EM aware RE oriented transmission scheme.

Performance Evaluation of Uplink Multiuser MIMO-OFDM System With Single RF Chain Receiver

In response to the recent increase in wireless-traffic demand in various wireless communication networks, the number of spatial multiplexings, represented by multiple input multiple output (MIMO) systems, has been increasing. Assuming a realistic hardware configuration achieving an increase in the number of spatial multiplexings depends on increasing the amount of hardware, such as analog/digital (A/D) and D/A converters, and radio frequency (RF) chains. Simplifying the hardware configuration while maintaining large channel capacity was the challenging issue to further improve the channel capacity in Beyond-5G and 6G. This paper proposes a novel system configuration based on an uplink multiuser MIMO - orthogonal frequency division multiplexing (OFDM) system with a single RF chain receiver. By oversampling OFDM signals with a single RF chain receiver, our approach provides an additional spatial multiplexing by switching the antenna directivity pattern in synchronization with the received sampling frequency. Oversampled OFDM signals are typically used for noise reduction, in the proposed configuration, they are used for adding spatial multiplexings. We clarify the potential of the proposed configuration for improving the channel capacity by applying it to the uplink multiuser MIMO-OFDM system. To demonstrate its feasibility, we developed a testbed and analyzed its effectiveness in experiment in an actual indoor office environment.

Energy Efficiency and Spectral Efficiency Tradeoff in RIS-Aided Multiuser MIMO Uplink Transmission

The emergence of reconfigurable intelligent surfaces (RISs) enables us to establish programmable radio wave propagation that caters for wireless communications, via employing low-cost passive reflecting units. This work studies the non-trivial tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a RIS equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our transmission strategy design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a performance metric called resource efficiency (RE). For the design of UT's precoding, it is simplified into the design of UTs' transmit powers with the aid of the closed-form solutions of UTs' optimal transmit directions. To avoid the high complexity in computing the nested integrals involved in the expectations, we derive an asymptotic deterministic objective expression. For the design of the RIS phases, an iterative mean-square error minimization approach is proposed via capitalizing on the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness and rapid convergence rate of our proposed optimization framework.

A Novel Random Access Framework for Uplink Cellular IoT: Non-Orthogonal Preambles and Multi-Antennas

Traditionally, even though the base station (BS) is equipped with multiple antennas, it has not been able to apply antenna technique such as an uplink multi-user multiple-input multiple-output (MU-MIMO) during the random access (RA) procedure due to the contention feature of the RA. To overcome such limitation, this letter proposes a novel RA framework leveraging both non-orthogonal preambles and multi-antenna technique. In our proposed framework, the non-orthogonal preambles effectively increase the total amount of contention resources, i.e., RA preambles, and thus the occurrence of preamble collisions can be significantly reduced. Accordingly, the BS can take an opportunity to exploit an uplink MU-MIMO even in the contention-based RA procedure under the belief that the contention may be resolved. We mathematically analyze the proposed framework in terms of packet collision probability and resource efficiency. Analysis and simulation results reveal that our proposed framework is able to accommodate more internet-of-things (IoT) devices in a resource-efficient way, compared to the conventional one.

A Cooperative Transmission Scheme in Drone-Based Networks

In this paper, we propose an uplink multiuser-multiple input multiple output (MU-MIMO) cooperative retransmission control scheme for the reliable and efficient communication of autonomous unmanned aerial vehicles (i.e., flight communication and drone-to-drone communication). This scheme is a key element in the design of a drone-based ad-hoc network. Because drone relay transmission operates in the air, flexible wireless relay networks over large areas can be achieved without interference from multipath signals caused by buildings and obstacles in ground-based communication. However, two main challenges exist. First, the relay transmission system occupies bandwidth every time the drone relays information. Therefore, because the radio channel is shared by multiple relay stations, the throughput is greatly reduced. To improve throughput, it is better to transmit using the minimum number of relay stations. Ultimately, long-distance direct transmission is the best. The other issue is a direct result of the first issues solution: the propagation loss increase when relaying information directly over a long distance. Moreover, because the frequency of interference waves from the ground increases, transmission signal errors are an important issue. To solve this issue, a transmission scheme to reduce errors is required. The scheme proposed in this study achieves high transmission reliability and efficiency even under high transmission error conditions. The results of a computer simulation reveal that the proposed method achieves a direct transmission throughput that is 1.5 times better than that of a conventional scheme. It is also shown that the proposed scheme requires only about three stations to achieve acceptable results.

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation (MBM) is expected to be a prominent modulation scheme, which has access to the high data rate by using radio frequency (RF) mirrors and fewer transmit antennas. Associated with multiuser multiple input multiple output (MIMO), the MBM scheme achieves better performance than other conventional multiuser MIMO schemes. In this paper, the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed. This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots, which exploits the structured sparsity of these aggregate MBM signals. Under this kind of scenario, a multiuser detector with low complexity based on the compressive sensing (CS) theory to gain better detection performance is proposed. This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit (CoSaMP) and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals. By exploiting these sparsity, the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity. Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

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).

Network utility maximization in uplink multiuser wireless LANs

Multiuser Multiple-Input Multiple-Output (MU-MIMO) enables several simultaneous transmission from the stations toward a multiple antennas access point of a WLAN and thereby boosts the network throughput. However, this throughput enhancement greatly depends on which stations are transmitting concurrently, which in turn necessitates the design of an efficient scheduling algorithm. In this paper, we model uplink MU-MIMO scheduling as a network utility maximization problem that is a non-convex problem due to the wideband channel aware transmission rate function and scheduling constraints. Relying on the zero duality gap, a stochastic learning algorithm is designed to solve the dual of that problem. Determination of stochastic subgradient in the stochastic learning algorithm involves an NP- complete problem, hence imperialist competitive algorithm is exploited to solve this problem. The simulation results substantiate significant throughput improvement and airtime fairness superiority of this scheme compared to the existing ones. The results also show the proposed scheme is not able to guarantee the optimal throughput due to employing imperialist competitive algorithm.

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.

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.

Non‐cooperative interference alignment for multicell multiuser MIMO uplink channels

Linear interference alignment (IA) is considered a promising interference management technique for future cellular networks. However, its application to practical cellular networks still faces many challenges, two of which are the overhead of sharing global channel state information (CSI) and the cost of signalling dimensionality. These two challenges are resolved in this study via a new linear IA scheme that not only prevents global CSI sharing but also keeps the number of signalling dimensions manageable. While making linear IA more applicable, the proposed scheme provides comparable degrees of freedom to the existing IA schemes in symmetric multicell multiuser multiple-input multiple-output (MIMO) uplink channels.

Extending the user capacity of MU-MIMO systems with low detection complexity and receive diversity

Multiple-input multiple-output (MIMO) based technologies are considered as an integral part of the upcoming 5G communications to fulfil the ever-increasing demands of wireless applications with high spectral efficiency requirements. However, in uplink multiuser MIMO (MU-MIMO) channels, the number of allowed users is limited by the number of receive antennas associated with radio frequency (RF) chains at the base-station and the complexity burden of multiuser detection (MUD). In this paper, a novel group layer MU-MIMO scheme with low complexity MUD is proposed to increase the number of served users well beyond the available RF chains. By taking the advantage of power control and inherent path loss in cellular systems, the allowed users are divided into groups based on their received power. Efficient group power allocation and group layer MUD (GL-MUD) are utilized to provide a valuable tradeoff between complexity and achieved performance. Furthermore, when more receive antennas than RF chains is implemented, a generalized norm based antenna selection algorithm is proposed to enhance the error performance. Symbol error probability expressions are derived and the effectiveness of proposed scheme is demonstrated through numerical simulations compared with the conventional MU-MIMO and non-orthogonal multiple-access (NOMA) systems over Rayleigh fading channels. The results show a substantial increase in user capacity up to two-fold for the available number of RF chains. In addition, significant signal-to-noise ratio gain is achieved using GL-MUD compared with different MUD techniques.

On Superimposed Pilot for Channel Estimation in Multicell Multiuser MIMO Uplink: Large System Analysis

This paper concerns the uplink of multicell multiuser multiple-input multiple-output (MIMO) systems. To tackle the effect of pilot contamination that is generally viewed as a bottleneck in previous work, this paper presents a superimposed pilot (SP)-aided uplink channel estimation scheme and mathematically characterizes the impact that an SP has on the performance of such a very large MIMO system. It is shown that there are two types of interference components that do not vanish, even when the number of antennas $M$ grows to infinity. The first type, which is referred to as cross-contamination, is due to the correlation between the SP and data among different cells. The second type, which is referred to as self-contamination, is due to the dependence between channel estimation and estimation error. Cross-contamination is, in principle, similar to pilot contamination in a conventional pilot-based multicell MIMO system, whereas self-contamination is unique for the SP-aided scheme. Both theoretical and simulation results demonstrate that the SP-aided scheme can effectively reduce the estimation contamination by increasing the data frame size and, in turn, achieve a significant improvement over the spectral efficiency, in comparison with conventional pilot-based methods.

Statistical Framework for Optimization in the Multi-User MIMO Uplink With ZF-DFE

We consider performance optimization in the uplink of a multiuser multiantenna communication system. Each user multiplexes data onto several independently encoded data streams, which are spatially precoded and conveyed over a fading narrowband multiple-input multiple-output (MIMO) channel. All users' data streams are decoded successively at the receiving base station using zero-forcing decision feedback equalization (ZF-DFE). We target the joint optimization of a decoding order and linear precoders for all users based on long-term channel information. For a class of general MIMO channel models, including the separable-correlation and double-scattering models, we show that the choice of precoder for a certain user does not affect the performance of the others. This leads to a particularly straightforward characterization of general user utility regions as a polyblock, or a convex polytope if time-sharing is allowed. We formulate the decoding-ordering problem under transmit-correlated Rayleigh fading as a linear assignment problem, enabling the use of existing efficient algorithms. Combining decoding ordering with single-user precoder optimization by means of alternating optimization, we propose an efficient iterative scheme that is verified numerically to converge fast and perform close to optimally, successfully reaping the benefits of both precoding and ordering in the MIMO uplink.