Pilot pattern adaptation for 5G MU-MIMO wireless communications

To meet the goal of tenfold increase in spectral efficiency, interference cancellation and multiuser detection are expected to be important tasks of fifth-generation (5G) radio access systems. Both tasks can be realized by joint detection algorithms. However, joint detection algorithms such as maximum likelihood (ML) detection have a high computational complexity. Previous works have shown that joint detectors based on Markov chain Monte Carlo (MCMC) methods can achieve similar results compared to ML detection with a large reduction in the computational complexity for systems with a large number of streams or users. The purpose of this work is to present a MIMO joint detector based on MCMC methods and evaluate it within the constraints of LTE Advanced (LTE-A), namely, using at most 8 transmit antennas and 64-QAM modulation. The evaluation is done separately from the channel decoder. Moreover, the complexity of the presented algorithm is compared to the one of an ML detector. The results show that the proposed MIMO detector offers a similar detection error rate compared to an ML detector. Furthermore, it was observed that the complexity reduction is significant for systems with more than six transmit antennas.

Massive MIMO has been proposed as a means of achieving huge spectral efficiency in cellular networks. However, its implementation is usually based on linear precoding schemes using Channel State Information (CSI). For this reason, the use of the Time Division Duplex (TDD) mode is typically assumed since it removes the dependence on the number of antennas at the transmitter to acquire CSI. Unfortunately, most of the cellular network operate in the Frequency Division Duplex (FDD) mode in which the acquisition of CSI depends proportionally to the number of transmit antennas. For this reason, the implementation of Massive MIMO is handicapped to few transmit antennas for the current cellular networks. In this paper we study the use of reconfigurable antennas that can switch among a set of preset modes, i.e., radiation patterns, through Blind Interference Alignment schemes. We show that it is a solution to exploit a very large number of antennas at the transmit side, overcoming the bottleneck that today constitutes the acquisition of CSI in Massive MIMO systems operating in FDD mode.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

This work presents a first comprehensive analysis of the impact of vector coded caching (VCC) in multi-user multiple-input multiple-output (MU-MIMO) systems with multiple receive antennas and variable pathloss — two key factors that critically influence systems with inherent MU unicasting behavior. We investigate two widely adopted precoding strategies: (i) block-diagonalization (BD) at the transmitter combined with maximal ratio combining (MRC) at the receivers, and (ii) zero-forcing (ZF) precoding. Our analysis explicitly accounts for practical considerations such as channel fading, channel state information (CSI) acquisition overhead, and fairness-oriented power allocation. Our contributions span both analytical and simulation-based fronts. On the analytical side, we derive analytical expressions for the achievable throughput under BD-MRC and ZF, highlighting the performance benefits of equipping multi-antenna users with cache-aided interference management. Specifically, we develop a low-complexity BD-MRC optimization method that leverages matrix structure to significantly reduce the dimensionality involved in precoding computation, followed by solving the associated max-min fairness problem through an efficient one-dimensional search. In the massive MIMO regime, an asymptotic expression for the achievable throughput over Rayleigh fading channels is also derived. Simulations validate our theoretical results, confirming that VCC delivers substantial performance gains over optimized cacheless MU-MIMO systems. For example, with 32 transmit antennas and 2 receive antennas per user, VCC yields throughput improvements exceeding 300%. These gains are further amplified under imperfect CSI at the transmitter, where VCC’s ability to offload interference mitigation to the receivers ensures robust performance even in the face of degraded CSI quality and elevated acquisition costs.

User scheduling in multiuser multiple-input-multiple-output (MU-MIMO) systems is fundamentally different with single-user systems 1 , in the sense that without spatial multiplexing, users in single-user systems are sharing the time-frequency degree-of-freedoms (DoFs), whereas in MU-MIMO systems, due to the fact that the number of spatial DoFs scales with the number of users (assuming sufficient base station (BS) antennas), users are not sharing the DoFs, but rather creating additional DoFs for their own use. However, instead of limited by the available DoFs, the number of simultaneous users are limited by the channel state information (CSI) acquisition overhead in pilot-assisted MU-MIMO systems. In this paper, we investigate the user scheduling scheme in pilot-assisted time-division-duplex (TDD) MU-MIMO systems. Leveraging the Lyapunov optimization techniques, we derive the throughput-optimal scheduling policy which serves as a performance bound due to its non-causality and high complexity. We then propose a heuristic scheme, which is causal and substantially decreases the complexity. Moreover, it performs fairly close to the optimum.

Statisticalprecoding is considered as a promising technique to release the channel state information (CSI) acquisition overhead. This article investigates a linear precoder design for frequency-division duplexing (FDD) massive MIMO downlink with only statistical CSI. We use a beam-based statistical channel model to capture the spatial correlation characteristics of the channels. The objective of the precoder design is to maximize the ergodic sum-rate under total power constraint. Based on the minorize-maximize (MM) algorithm, a stationary solution of the ergodic sum-rate maximization problem can be obtained. The stationary solution is shown to be the same as the optimal solution to a stochastic weighted minimum mean square error (SWMMSE) problem. Further, we establish the approximations for rate expressions with deterministic equivalent. The deterministic equivalents of ergodic rates are only related to the precoding matrices and statistical CSI. According to these closed-form rate expressions, we propose a linear precoder design algorithm and obtain tractable expressions for precoding matrices. Numerical comparisons with the existing precoding approach demonstrate the significant advantages of the developed algorithm.

The multi-user eigen transmission (MET) scheme has been extensively investigated in multi-user multiple-input multiple-output (MU-MIMO) downlink transmission due to its remarkable performance at low computational complexity. Based on the existing solutions of MET discussed in open literatures, we propose an enhanced MET (eMET) scheme in this paper in order to further enhance the performance of cellular network in terms of spectral efficiency, especially for the cell-edge users. The theoretical analysis shows that by employing the successive transmit eigen-channel optimization and selection, the eMET scheme outperforms the classic MET approach in multi-cell downlink transmission. In parallel, Our system-level simulation results demonstrate that our proposed scheme outperforms its counterpart in both cell average and cell edge performance. Specifically, upon taking the performance of MET method as the baseline, the eMET regime can achieve over 15% spectral efficiency (SE) gain for cell edge users in single-cell (SC) transmission and over 30% SE gain in coordinated multi-point transmission (CoMP) without any loss of the system's average performance.

The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.

Since atmospheric turbulence and pointing errors cause signal intensity fluctuations and the background radiation surrounding the free-space optical (FSO) receiver contributes an undesired noisy component, the receiver requires accurate channel state information (CSI) and background information to adjust the detection threshold. In most previous studies, for CSI acquisition, pilot symbols were employed, which leads to a reduction of spectral and energy efficiency; and an impractical assumption that the background radiation component is perfectly known was made. In this paper, we develop an efficient and robust sequence receiver, which acquires the CSI and the background information implicitly and requires no knowledge about the channel model information. It is robust since it can automatically estimate the CSI and background component and detect the data sequence accordingly. Its decision metric has a simple form and involves no integrals, and thus can be easily evaluated. A Viterbi-type trellis-search algorithm is adopted to improve the search efficiency, and a selective-store strategy is adopted to overcome a potential error floor problem as well as to increase the memory efficiency. To further simplify the receiver, a decision-feedback symbol-by-symbol receiver is proposed as an approximation of the sequence receiver. By simulations and theoretical analysis, we show that the performance of both the sequence receiver and the symbol-by-symbol receiver, approach that of detection with perfect knowledge of the CSI and background radiation, as the length of the window for forming the decision metric increases.

Massive multiple-input–multiple-output (MIMO) systems which include a large number of service antennas have received a great deal of attention as a means to improve both spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems. In this article, we study the feasibility of using Massive MIMO to support a vast amount of underwater Industrial Internet of Things (IIoT) devices. By using a large number of antennas as compensation entities for the low bandwidth in the underwater channel, we show that underwater Massive MIMO can simultaneously support a massive amount of underwater IIoT devices. Electromagnetic radio frequency (RF) and acoustic waves are used to present the performance with respect to SE, throughput (TP) per user equipment (UE), and coverage. Additionally, we show that both Massive MIMO with RF and acoustic waves have the same closed-form upper bounds which can be used as an important design tool of underwater IIoT networks. Numerical results verify the theoretical analysis.

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.

Energy efficiency is a basic need of the fifth-generation (5G) wireless networks, because the energy sources and capability of the battery are very narrow against the huge demand. Massive MIMO is a great invention of 5G cellular network with a large number of antennas. In this research, the prime aim is to maximize the energy efficiency and spectral efficiency simultaneously of massive MIMO system. Spectral and energy efficiency are formulated with fixed power, number of users, number of antennas with the impact of multiple users and without the impact of multiple users, the ratio of number of antennas and number of users. Trade-off also has been analyzed among the co-located, distributed and hybrid massive MIMO with the Nakagami-m fading channel, which can subtract the co-channel interference in a system and compared with each other. From the simulation results, it is seen that the energy efficiency of distributed massive MIMO is higher than the other two with the variation of fixed power, number of user and the ratio of number of antenna and number of user with respect to spectral efficiency. The energy efficiency of co-located massive MIMO is higher than the other two with the variation of number of antenna.

The growing interest in backscatter communications as a low-powered solution in various fields necessitates pushing the envelope of current backscatter systems in multiple frontiers, among which is spectral efficiency. The increase in spectral efficiency can drive real-time applications such as augmented reality. In most of backscatter-system applications such as radio-frequency identification, backscatter modulation is implemented using binary schemes realized with square pulses, which are not spectral efficient. To address the spectral efficiency concern, this article reviews—with a scope limited to prototyped systems—some of the existing works in the literature that pertain to increasing the spectral efficiency of backscatter systems. The prototyped systems can be grouped—based on the implementation technique used—into three groups: High-order modulation, single-sideband modulation, and pulse shaping. Based on the current trends and studied literature, the article concludes with discussions on some future directions and open-ended research problems such as optimal pulse shaping, the use of artificial intelligence, multi-carrier modulation, and agile modulation; which are all aim for overcoming the spectral-efficiency limit of contemporary backscatter systems.

Large scale multiuser multi-input multi-output (MU-MIMO) system is an emerging wireless technology, which can impart huge spectral efficiency, enhanced link reliability, and serves several users simultaneously in the same frequency–time resource. Pilot contamination (PC) is the major bottleneck in achieving the optimum performance of a multi-cell large MU-MIMO system. We propose a new multi-cell block diagonalization-QR-minimum mean square error (M-BD-QR-MMSE) precoding scheme to diminish the effects of pilot contamination in a multi-cell MIMO system. The reduced effects of Pilot contamination result in the enhancement of spectral efficiency for the multi-cell MU-MIMO system. The design of the proposed scheme utilizes block diagonalization (BD) in combination with MMSE precoding that obtains large null space using QR decomposition operation to reduce intercell and intracell interference much efficiently than conventional precoding schemes. The gain in spectral efficiency with the proposed scheme is enhanced through non-universal pilot reuse in contrast to single-cell schemes. Furthermore, the downlink spectral efficiency expression has been derived for a large scale MIMO system, and the same is computed using Monte Carlo simulations to analyze the results. The numerical results depict that the proposed M-BD-QR-MMSE provides a significant improvement in the spectral efficiency in comparison with traditional single-cell MMSE (S-MMSE), regularized zero-forcing (R-ZF), ZF, Maximum ratio transmission (MRT) and multi-cell MMSE (M-MMSE) schemes. The proposed precoding design provides the spectral enhancements of 32.19 and 50.13 bit/s/Hz/cell in comparison to conventional multi-cell MMSE and single-cell MMSE precoding schemes respectively.

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.