Multiple-input Multiple-output Systems Research Articles

Moving Object Localization in Distributed MIMO With Clock and Frequency Offsets

This paper investigates the localization problem of the moving target in distributed multiple-input multiple-output (MIMO) systems, where the transmitters are subject to clock and frequency offsets. The position and velocity of the moving target are jointly determined by applying time delay (TD) and Doppler shift (DS) measurements. For this purpose, two efficient solution approaches are proposed to estimate the unknowns of the moving target, as well as the clock and frequency offsets. Firstly, the nonconvex localization problem is first relaxed into a convex semidefinite programming (SDP) form. Additionally, a two-stage closed-form solution (TSCFS) approach is proposed to determine the unknowns. The performance of the proposed methods is examined by the mean squared error (MSE) analysis. The simulated results show that the performance of the proposed solutions are able to approach the Cramér-rao lower bound (CRLB) accuracy at low noise levels. In contrast, the SDP solution performs better than the TSCFS in the presence of high noise levels or a small number of receivers.

ADMM Based Interference Exploitation Multi-User One-Bit Massive MIMO Precoding

In this paper, we focus on the 1-bit precoding design for a multi-user massive multiple-input multiple-output (MIMO) system, where the concept of constructive interference (CI) is employed to formulate the optimization problem. By adopting the symbol-scaling CI metric, the formulated optimization problem is transformed such that the alternating direction method of multipliers (ADMM) framework can be used to solve this problem. Furthermore, we obtain the closed-form solution to the subproblem in each step within the ADMM iteration, thus achieving lower complexity. We further provide the computational complexity analysis of the proposed algorithm. Numerical experiments verify the superior bit error rate (BER) performance of the proposed ADMM-based CI precoding (CI-ADMM) scheme over the state-of-the-art, with proper initialization.

Unsupervised Deep Learning for Power Control of Cell-Free Massive MIMO Systems

The problem of power control for the uplink and the downlink in cell-free massive multiple-input multiple-output (CF mMIMO) systems is considered in this paper. In order to achieve a balance between the conflicting requirements of good performance levels and of low computational complexity, we employ unsupervised learning based on deep learning (DL). The proposed power control strategies can work using as input the large scale fading coefficients only and are capable to obtain very satisfactory performance levels, as compared to conventional, highly complex, optimization methods and to heuristic methodologies. Moreover, they can be used also in a user centric system wherein each mobile station is served by a subset of the active access points.

Low-Rank Matrix Sensing-Based Channel Estimation for mmWave and THz Hybrid MIMO Systems

This paper studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training structures. In addition, it does not require knowledge about the array responses at the transceivers, in contrast to most existing solutions allowing low training overhead. Furthermore, a preconditioned conjugate gradient (PCG) algorithm-based implementation and a low-rank matrix completion (LRMC) formulation are proposed to further reduce the computational complexity. In order to enhance the channel estimation performance for fat and tall channel matrices, we introduce a matrix reshaping approach that can preserve the channel rank by exploiting the shift-invariance property of uniform arrays. We also introduce a spectrum denoising (SD) approach for further improving the performance when the array responses are known and the number of paths is small. These approaches can effectively enhance the performance at a given training overhead. Simulation results suggest that the proposed solutions can achieve higher channel estimation accuracy and reduce the computational complexity as compared to several representative channel estimation schemes.

A Broadband MIMO Antenna Based on Multimodes for 5G Smartphone Applications

In this letter, an eight-element broadband multiple-input multiple-output (MIMO) antenna array with a simple structure based on multiple modes is presented, which covers 3.3–6 GHz for fifth-generation (5G) smartphone. Double coupled-fed loop modes and a slot mode are well excited by the feeding strip connecting with the tuning branch. The broadband antenna is well designed without loading any lumped elements, thanks to the tuning branch on the side frames. Furthermore, the polarization diversity technique is exploited to improve isolation. As a result, the MIMO system operates in the 3.3-6 GHz (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> < -6 dB) with desirable isolation higher than 12.1 dB, which satisfies the coverage of the 5G communication bands: n77/n78/n79 and 5G WLAN. Besides, acceptable efficiency and envelope correlation coefficients (ECCs) of lower than 0.2 can also be obtained, which demonstrates that the presented MIMO array has potential for the mobile communication of 5G smartphones.

Multiband and Wideband Self-Multipath Decoupled Antenna Pairs

We propose a series of closely spaced self-multipath decoupled antenna pairs that is capable of realizing self-decoupling performance from a single band to four bands for 5G mobile terminals. Firstly, we propose a self-multipath counteraction method by building multiple coupling paths on the radiating branch of one of the antennas, where the coupling energy passing through the multiple coupling paths could be counteracted at the feeding ports. Based on this principle, two types of single-band self-multipath decoupled antenna pairs are constructed with the shorted-end and the open-end without using any additional decoupling structures. Then, a dual-band and wideband self-multipath decoupled antenna pair is designed by combining the multipath of such two types of single-band self-multipath decoupled antenna pairs. Finally, a four-band and wideband self-multipath decoupled antenna pair is constructed to further widen the decoupling bandwidth based on the dual-band self-multipath decoupled antenna pair. Both simulated and measured results show that the four-band antenna pair can be self-decoupled at four bands with the isolation more than 20 dB over entire N77, N78 and N79 bands, demonstrating excellent multi-band and wideband self-decoupling performance. The proposed self-multipath counteraction method, with the merits of simple antenna structure and wideband self-decoupling performance, should provide significant application values for the very compact and wideband multiple-input multiple-output systems.

Deep Learning Aided 5G Channel Estimation

Abstract: Wireless communications involves the transfer of voiceand data without a cable or wires. It uses orthogonal frequencydivision multiplexing, also known as a multicarrier transmission technique. In multiple input multiple output (MIMO) wireless communication (4G and 5G technologies), channel estimation is crucial. Multiple antennas are used at both the transmit and receive sides ofa MIMO system to increase spectral efficiency and reliability. In 5G channel estimation is performed to improve the accuracy of the received signal. Least-squares estimation is a cheap method with relatively large channel estimationerrors, but it is supported in this work by using a new channel estimation method that leverages deep learning. LS (least squares) and MMSE (minimum mean squared error) are two popular traditional approaches to channel estimation, but deep learning provides much more accurate results than previous channel estimation methods.

Semi-Blind Receivers for Two-Hop MIMO Relay Systems with a Combined TSTF-MSMKron Coding.

Due to the increase in the number of mobile stations in recent years, cooperative relaying systems have emerged as a promising technique for improving the quality of fifth-generation (5G) wireless networks with an extension of the coverage area. In this paper, we propose a two-hop orthogonal frequency division multiplexing and code-division multiple-access (OFDM-CDMA) multiple-input multiple-output (MIMO) relay system, which combines, both at the source and relay nodes, a tensor space-time-frequency (TSTF) coding with a multiple symbol matrices Kronecker product (MSMKron), called TSTF-MSMKron coding, aiming to increase the diversity gain. It is first established that the signals received at the relay and the destination satisfy generalized Tucker models whose core tensors are the coding tensors. Assuming the coding tensors are known at both nodes, tensor models are exploited to derive two semi-blind receivers, composed of two steps, to jointly estimate symbol matrices and individual channels. Necessary conditions for parameter identifiability with each receiver are established. Extensive Monte Carlo simulation results are provided to show the impact of design parameters on the symbol error rate (SER) performance, using the zero-forcing (ZF) receiver. Next, Monte Carlo simulations illustrate the effectiveness of the proposed TSTF-MSMKron coding and semi-blind receivers, highlighting the benefit of exploiting the new coding to increase the diversity gain.

Impact of channel imperfections on variant SMTs MIMO systems over revised Nakagami-[formula omitted] channel model

Recently, a revised model for the Nakagami-m multiple-input multiple-output (MIMO) channel has been proposed, yielding distinct results and conclusions compared to the existing model. This article focuses on evaluating and discussing the performance of MIMO space modulation techniques (SMTs) transmission schemes, namely space shift keying (SSK), quadrature space shift keying (QSSK), spatial modulation (SM), and quadrature spatial modulation (QSM), over the revised channel model. The evaluation considers spatial correlation, imperfect channel state information (CSI), and examines various performance metrics such as average bit error rate (ABER), mutual information, and capacity. Specifically, a closed-form expression for the ABER is derived under the assumption of a maximum-likelihood (ML) receiver. Additionally, formulas for mutual information and capacity are derived and thoroughly discussed. To validate the derived formulas, extensive Monte Carlo simulations are performed. Furthermore, the impact of system parameters on the ABER, mutual information, and capacity is investigated, providing a comprehensive analysis of their variations. A comparative study is conducted between different transmission schemes using the revised model, elucidating their relative performance. Moreover, a comparison is made between the reported results of the revised model and the existing Nakagami-m model, as well as other channel distributions such as Rayleigh and Rice fading channels. The findings clearly demonstrate the disparities between the existing and revised models, with the revised model exhibiting superior performance. Notably, when comparing the scenario of m=1 with a Rayleigh fading channel, an exact match is observed for the revised channel model, while some differences are detected for the existing model.

Low complexity joint hybrid precoding for RIS-assisted wideband wireless systems

Reconfigurable intelligent surface (RIS) has been expected to be a promising technology for next generation communication systems based on its capability of tailoring propagation environments. At the same time, hybrid analog and digital precoding is also a key technology for improving the energy efficiency of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless systems. The requirements of low cost and power consumption for future wireless systems can be met by combining these two technologies. In this paper, we formulate the problem of jointly designing the hybrid precoding at the base station (BS) and the phase shifts at the RIS for both single-cell and multi-cell scenarios to maximize the achievable sum rate. For the single-cell scenario, we propose an improved sparse precoding (ISP) scheme for analog transmit precoding (TPC) design while considering the beam squint effect, and utilize the two-stage digital TPC scheme to reduce the computational complexity. Inspired by the two-stage digital TPC design, we propose the three-stage digital TPC design for the multi-cell scenario, which handles inter-cell and intra-cell interference, and power allocation respectively. Finally, simulation results demonstrate that the proposed scheme has better performance than existing hybrid precoding schemes.

Suppression of Mainlobe Jammers with Quadratic Element Pulse Coding in MIMO Radar

The problem of suppressing mainlobe deceptive jammers, which spoof radar systems by generating multiple false targets, has attracted widespread attention. To tackle this problem, in this paper, the multiple-input multiple-output (MIMO) radar system was utilized by applying a quadratic element phase code (QEPC) to the transmitted pulses of different elements. In the receiver, by utilizing the spatial frequency and Doppler frequency offset generated after decoding, the jammers were equivalently distributed in the sidelobes of the joint Doppler-transmit-receive domain and were distinguishable from the true target. Then, further spatial frequency compensation and Doppler compensation were performed to align the true target to the zero point in the transmit spatial and Doppler domains. Moreover, by designing appropriate coding coefficients, the jammers were suppressed by data-independent Doppler-transmit-receive three-dimensional beamforming. However, the beamforming performance was sensitive to angular estimation mismatches, resulting in performance degradation of jammer suppression. To this end, a center-boundary null-broadening control (CBNBC) approach was used to broaden the nulls in the equivalent beampattern by generating multiple artificial jammers with preset powers around the nulls. Thus, the false targets (FTs) with deviations were sufficiently suppressed in the broadened notches. Numerical simulations and theoretical analysis demonstrated the performance of the developed jammer suppression method.

Wide-Beam Designs for Terahertz Massive MIMO: SCA-ATP and S-SARV

Terahertz (THz) communication is expected to be one of the core enabling technologies for future systems. Due to the poor scattering and severe reflection loss of THz waves, line-of-sight (LoS) communication is considered as a leading feature in THz multiple-input multiple-output (MIMO) systems. To realize LoS communication, beam training is a promising scheme to find the beamforming vectors without leveraging explicit channel state information (CSI). In this context, a crucial issue for THz MIMO is how to design the beam codewords for realizing any expected radiation pattern during the training. In particular, the narrow beams can be realized by array response vectors whereas the wide-beam design is still an open problem. In this paper, we propose two high-quality algorithms, namely, successive convex approximation (SCA)-based auxiliary target pursuit (SCA-ATP) and the sum of symmetrical array response vectors (S-SARV), for offline design and real-time design, respectively. Numerical results show that SCA-ATP yields the best performance in terms of the beam-pattern error (BPE) compared with benchmarks, and S-SARV can achieve a close performance to SCA-ATP with low computational complexity.

BP-algorithm based on factor diagram about massive MIMO system

In order to meet the growing demand for high-speed mobile data business, there has been a constant concern for the fifth-generation mobile communication system. 5G large-scale Multiple Input Multiple Output (MIMO) technique has significantly improved the system spectral efficiency and energy efficiency. In order to study the signal detection method under the large-scale MIMO system and improve the existing methods, the article describes the background of signal detection. It introduces the development of the belief propagation (BP) algorithm. There are four main BP algorithms: Original-BP algorithm, RDF-BP algorithm, EBRDF-BP algorithm, and GAI-BP algorithm. One algorithm is proposed based on these fundamental algorithms to improve the whole detection performance. This algorithm reduces complexity while not increasing the error rate too much, which means that it has certain feasibility and practicability.

Iterative algorithms and architectures in massive MIMO detection

Massive multiple-input multiple-output (MIMO) wireless systems play an important role in the 5G networks. The complexity of signal detection in massive MIMO is increasing rapidly due to the growth of the number of transmitting antennas. In this paper, we introduced different iterative algorithms to decrease the computational cost of the approximate minimum mean-square error (MMSE) algorithm, including the Neumann Series algorithm, Jacobi method, Gauss-Seidel method, Successive Over Relaxation method and the Conjugate Gradient method. In addition, the VLSI architecture implementations of algorithms mentioned above are also discussed in the article.

A Novel Shaped Four Port MIMO Antenna for Wireless Communication

This paper presents a compact novel shaped micro strip line fed four element Multiple Input Multiple Output (MIMO) antenna for wireless application at 2.4 GHz. Four vertically oriented identical rectangular patch antennas are used to form a hollow cylinder of rectangular cross section. The direction of radiation of each antenna is thus oriented at 900 with respect to its two adjacent antennas providing good isolation between adjacent antennas in this compact MIMO system. Each antenna element has the dimensions of 0.30λx0.38λx0.012λ. The whole MIMO antenna has the outer dimensions of 0.38λx0.38λx0.30λ. The uniqueness of the proposed MIMO antenna is that the inner space of the hollow cylindrical structure can be used for hosting the associated signal processing unit of the whole system. Performance parameters of the proposed MIMO antenna are investigated using Computer Simulation Technology (CST). Envelope Correlation Coefficient (ECC) less than 0.033 and almost 10dB Diversity Gain (DG) are obtained at 2.4 GHz for this simple and compact antenna system. Ratio of Mean Effective Gain (MEG) of any two antennas is less than 0.02dB. Performance parameters of the proposed MIMO antenna system are compared with similar types of antennas found in recent literature.

A Study of Mutual Coupling Suppression between Two Closely Spaced Planar Monopole Antenna Elements for 5G New Radio Massive MIMO System Applications

5G NR (new radio) introduces the concept of massive MIMO (multiple-input multiple-output) technology, in which a larger number of antenna arrays are installed on the transceiver. Due to the increased number of antenna elements allocated close to each other (approximately at a distance of half a wavelength), mutual coupling becomes a serious problem leading to performance degradation of the MIMO communication system. In this communication, two different configurations of closely spaced antenna array elements are studied. In order to reduce the mutual coupling, a combination of a metamaterial-based frequency-selective surface (FSS), a metallic strip, and a slot element in the ground plane is examined. It is found that the proposed technique significantly suppresses mutual coupling from −12 dB to −25 dB. Both designs are fabricated and experimentally validated. The simulation results are in good agreement with the measurements. The proposed mutual coupling reduction technique may be suitable for massive MIMO systems in fifth-generation (5G) new radio applications.

Distributed MMSE-based uplink receive combining, downlink transmit precoding and optimal power allocation in cell-free massive MIMO systems

In cell-free massive multiple-input multiple-output systems, a large number of distributed wireless access points (AP) simultaneously serve a number of user equipments (UEs). This setup has recently been introduced as a promising alternative for the current 5G cellular networks. The setup has the ability to offer a good quality of service, especially for the UEs on the cell edges, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal power allocation combined with uplink receive combining and downlink transmit precoding is tackled by providing efficient distributed MMSE-based algorithms. The necessary fronthaul communications to estimate the combining/precoding vectors and the necessary large-scale channel statistics are reduced to a minimum and rely on in-network summation that can be accomplished whenever the APs can be arranged into a tree-topology. Non-weighted max-sum and max–min are used as utilities for the power allocation, but the algorithms are not limited to these cases. Simulations show that the proposed algorithms outperform heuristic power allocation methods, both in uplink and downlink.

Complex field manipulation based on OAM spectrum analysis with an arbitrarily distributed antenna array

In the next generation multiple-input multiple-output (MIMO) communication system, a higher effective degree of freedom (EDoF) is desired to increase the channel capacity. Hence, complex field manipulation, which enriches the diversity of the amplitude and phase distribution, is necessary for an antenna array. In this paper, the complex field manipulation (CFM) scheme based on an arbitrarily distributed antenna array is proposed, which takes the orbital angular momentum (OAM) spectrum analysis as the bridge. Whether the antenna element transformation states are fixed or flexible, the CFM can be achieved with different schemes. This work may promote the application of OAM spectrum analysis and the development of complex field manipulation in the high-EDoF MIMO communication system.

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

AbstractThe estimation of the channel in multiple input multiple output (MIMO) systems over wireless communication has faced many challenges, such as high bit rate transmission with less time complexity, low error rate, and high convergence rate. And also, there is some potential for enhancing the system performance regarding channel efficiency diversity. MIMO system uses a huge count of antennas on the receiver and the transmitter side. This increases the cost requirements and power consumption. For achieving coherent detection in the MIMO system, optimization such as water filling and beam forming are required to find the proper channel. Hence, multiple transceivers used in both the transmitter side and receiver side create a channel estimation issue that is complicated when analyzed to a single input, single output system. Therefore, several solutions have been recommended to decrease the computational cost and power consumption in channel estimation of MIMO systems. This work intends to propose a novel deep structured architecture to handle the channel estimation problem in a MIMO system. The major objective of the developed method is to validate the channel coefficients of MIMO at the transmitter according to the obtained SNR feedback data packet from an acceptor with the help of the hybrid intelligence‐based deep learning model (HI‐DLM). For both scenarios, the HI‐DLM is introduced, in which the convolution neural network (CNN) is integrated with other deep‐structured architecture. Especially, the fully connected layers of CNN are restored by the long short‐term memory. In the HI‐DLM‐aided channel estimation, the hyperparameter optimization is accomplished by the novel dingo‐horse herd optimization for reducing the mean square error and bit error rate to acquire the estimated channel. The corresponding algorithm is validated on the communication of the MIMO system by experimentations. Thus, it is revealed that the offered algorithm provides better convergence and less computational cost.

Path Loss and Auxiliary Communication Analysis of VANET in Tunnel Environments

Vehicular ad hoc network (VANET) communications face severe fading problems due to the signal reflections and diffractions within tunnels. Unlike the open road, the space of a tunnel is very limited, so VANET communication performance in a tunnel is seriously affected. In the process of signal transmission, the reflected signal is symmetrical with the incident signal after it is reflected by the road and the wall. In this paper, we establish a mathematical model of path loss for V2V (Vehicle-to-Vehicle) communication based on the principle of signal reflection symmetry in tunnels and considering several factors, such as the tunnel surface and the color of the tunnel wall. In addition, we use cooperative communication to form a virtual multiple-input multiple-output (V-MIMO) system, to improve the communication quality in tunnels. In the proposed system, the OBU (On-Board-unit) and RSU (Road-Side-Unit) share each other’s antennas, so that wireless cooperative communication can be employed, without increasing the number of antennas in a one-way tunnel. Therefore, this multipath fading internal electromagnetic wave propagation model can be used to improve performance. A deep reinforcement learning algorithm was used to solve the pairing problem to obtain a more accurate OBU and RSU pair, to form a V-MIMO system. Here, the RSU is regarded as an agent and interacts with the OBU in the tunnel. The optimal strategy was learned in a real-time changing simulation environment, and the experiment verified the convergence of the algorithm. The simulation results showed that, compared with the Q-learning based scheme, the optimal matching algorithm based on V-MIMO and a DQN (Deep Q-network) could effectively reduce the probability of transmission outages and improve the communication efficiency in tunnels.