Multiple-input Multiple-output Wireless Research Articles

Secrecy Throughput Maximization for IRS-Aided MIMO Wireless Powered Communication Networks

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

A Conformal Four-Antenna Module for Capsule Endoscope MIMO Operation

In this article, a four-antenna module in a capsule endoscopy is proposed for multiple-input multiple-output (MIMO) operation to achieve the purpose of real-time transmission of high-resolution images with high data rates. For compactness, the proposed design adopts conformal architecture and fully utilizes the inner wall space of the capsule body to accommodate four antennas in the very limited space of the capsule endoscope. For the availability of MIMO operation, three different technical approaches are used to decouple or improve the isolation between neighboring antenna elements and between diagonal antenna elements. System considerations (interaction between the electronic components and the antenna in the endoscope) and specific absorption ratio (SAR) evaluation are performed to verify the robustness and human safety of the proposed design, respectively. In addition, link margin analysis and MIMO channel parameter studies are carried out to verify the wireless telemetry performance and MIMO operation performance, respectively. Antenna prototypes were fabricated and implanted in minced pork for ex-vivo measurements (S-parameters, gain, and radiation patterns). The measured overlapping impedance bandwidth of the four antenna elements is 120 MHz, which can fully cover the 915 MHz industrial scientific medical (ISM) band. The measured isolation between the neighboring antenna elements and the diagonal antenna elements is better than 20 and 10 dB, respectively. The measured maximum peak realized gain is −18.1 dBi.

Mobility and Routing based Channel Estimation for Hybrid Millimeter-Wave MIMO WSNs

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

Proper Power Azimuth Spectrum Estimation for MIMO Channels via Gans Mapping Inversion

The use of antenna arrays is indispensable in radar and multiple-input multiple-output (MIMO) wireless communications areas, as it permits the treatment of wavefronts in the spatial domain, enabling important applications such as beamforming and target detection. Spatial information is commonly given in terms of spatial correlation functions (SCFs), but its representation in terms of angular domain, such as the power azimuth spectrums (PASs), is sometimes more convenient. Therefore, transforming from SCF to PAS, and vice versa, is often required. This is commonly done by employing so-called beamformers, where the Bartlett beamformer (BB) is the most commonly utilized. However, the use of beamformers alone is not enough for retrieving PAS information from a given SCF. Particularly, this paper shows that a non-linear operation is missing in relating SCFs to PASs as provided by current beamformers. The paper also shows the mathematical expressions that correctly relate SCFs to PASs and wavenumber spectrums (WNSs) for the general case of systems with antenna arrays. The developed theory results in two new beamformers, which, corroborated by simulation results, solve any previous inconsistency in estimating PASs from SCFs.

Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a “best-effort” at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.

Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels

In this paper, we propose a geometry-based channel model for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communications in practical tunnel scenarios. In the proposed channel model, the waves transmitted from the transmitter experience line-of-sight (LoS) and non-LoS (NLoS) before arriving at the receiver. The time-varying parameters of the propagation paths and angles are derived to characterize the channel non-stationarity. Furthermore, we derive the correlation property functions of the proposed V2V channel model; they are space cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency CCFs, as well as the Doppler power spectrum distributions (PSDs). Numerous simulation results of the propagation characteristic for different physical properties of the tunnel, as well as the different motion properties of the transmitter and receiver, are studied and discussed; they demonstrate that the proposed channel model is suitable for describing the practical V2V tunnel communication scenarios.

Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Approaches

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing- (CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity.

An optimal location strategy for multiple drone base stations in massive MIMO

The concept of drone base stations (DBSs) has been applied to reduce the distance of the wireless link between a macro base station and its active users under diverse scenarios in military communications, smart industries, and high-density networks, and to provide service in topologies with damaged infrastructure. In this paper, we address the optimal positioning of multiple DBSs in a multiple-input multiple-output wireless network setting. We present a low-complexity machine learning-based algorithm to optimize the location of the DBSs by minimizing the collective wireless received signal strength experienced by the active terminals. The proposed algorithm reduces the propagation loss in the system and provides a lower bit error rate when compared with the Euclidean cost benchmark.

A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications

Multiple-input multiple-output (MIMO) wireless system is a hot research topic in 5th generation mobile communication technology (5G), which can increase the channel capacity of the antenna system to meet new communication demands without changing the bandwidth and transmission power. However, adding the number of antenna elements in a limited space size can lead to mutual coupling effects and impair the communication quality. Therefore, it is significant to design a compact MIMO wireless system using decoupling techniques. Several studies on the reduction of mutual coupling effects have been proposed. However, a concrete overview of the features and resemblances of these decoupling techniques is lacking. In this review, we provide a brief description of the research background on the mutual coupling effect of MIMO. Then, various decoupling techniques worthy of reference are classified and outlined. We further summarize the additional impacts due to the existence of different decoupling structures, and the possible usability and defects of these techniques are revealed clearly in this paper. Finally, a future outlook on the development direction of decoupling techniques for MIMO wireless systems is also concluded and discussed.

Gradient Relative Permittivity Superstrate for Decoupling of Two Closely Located Dual-Polarized Slot Antennas

A simple decoupling method between two closely located dual-polarized slot antennas is proposed in this communication. Based on substrate integrated waveguide (SIW), the dual-polarized antenna is realized using cross-shaped slot and U-shaped feed line, and two identical elements are closely located in edge-to-edge space of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.031\lambda _{0}$ </tex-math></inline-formula> to form the multiple-input–multiple-output (MIMO) array, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at the center operating frequency. Wideband characteristics, high dual-polarized isolation, and low envelope correlation coefficient (ECC) are realized by introducing a superstrate with gradient relative permittivity (GRP) above the array. The GRP is obtained using drilling hole arrays on low-cost FR-4 materials. To validate the proposed design, the antenna array is fabricated and measured. The measured result shows that 13 and 16 dB isolation enhancements are provided for two orthogonally polarized fields, simultaneously. Moreover, the measured operating bandwidth is from 8 to 10.35 GHz (about 25.6%, the isolation is more than 20 dB), and the ECC between two antennas is less than −52 dB for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{1}$ </tex-math></inline-formula> -pol and −62.4 dB for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{2}$ </tex-math></inline-formula> -pol over entire operating band. The proposed method has the potential to be used in MIMO wireless systems.

Centralized and Distributed Millimeter Wave Massive MIMO-Based Data Fusion With Perfect and Bayesian Learning (BL)-Based Imperfect CSI

This paper presents low-complexity decision rules as well as the pertinent analysis for data fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). The proposed framework considers both unknown and known parameter scenarios, and the spatial correlation arising due to close proximity of the sensors for both the centralized MIMO (C-MIMO) and distributed MIMO (D-MIMO) antenna configurations. The resulting detection performance is characterized by determining the closed-form expressions of probabilities of detection and false alarm for both antenna configurations. The optimal sensor gains are also determined for both the D-MIMO and C-MIMO architectures to further improve the detection performance. Additionally, asymptotic analysis is presented for both antenna configurations to determine the power scaling laws for the mmWave massive MIMO WSN, which lead to an improved sensor battery life without sacrificing the system performance. Furthermore, decision rules are also derived along with the pertinent analysis for a practical scenario with uncertainty in the channel state information (CSI) at the fusion center, wherein CSI of the mmWave massive MIMO channel is estimated using the novel sparse Bayesian learning (SBL) framework. Simulation results are presented to illustrate the performance of the proposed schemes and to validate the analytical results.

Ultra-High Mobility Analysis of MIMO Wireless System in Tunnel Scenarios

This letter studies the effect of ultra-high mobility on the performance of multiple-input multiple-output (MIMO) wireless communication system in tunnel scenarios. It is found that the Doppler frequency shift generated by the ultra-high mobility reduces the correlation coefficient. Importantly, it is observed that there exists a key inflection point distance in the ultra-high speed tunnel scene. When this distance is exceeded, the channel correlation of MIMO can be significantly reduced. Moreover, it is perceived that the inflection point distance is relatively stable and is less affected by the communication frequency. On this basis, we propose a high-quality communication area with low MIMO channel correlation. For the ultra-high speed of 720km/h, the inflection distance is about 95m, and the reduction of MIMO channel correlation at 3.5GHz and 28GHz can reach up to 52.7% and 41.2%, respectively. The findings are extremely useful for the applications of the mobile MIMO communication system in tunnel and railway scenarios.

Physical (PHY) Layer Analysis of Data Transmission in MIMO Wireless Networks in Line- of-Sight (LoS) Environments

This research paper presents the Physical (PHY) layer analysis of multiple input multiple output (MIMO) systems in line-of-sight environments between a user equipment (UE) and a base station when an information data is transmitted. The data transmission in wireless networks often takes place in non-line-of-sight in urban environments and Line-of-Sight (LoS) in rural environments depending on the bandwidth, power constraints. Using MIMO technology, the data transmission takes place with increased number of transmit and receive antennas which results in reduced signal to noise ratio (SNR) requirement. In this research work, simulation for PHY layer metric of probability of error, mean square error (MSE) for data transmission in MIMO wireless networks is obtained Matrix Laboratory (MATLAB) for LoS environment which follows Rician distribution. Probability of error reduces to a significant extent when the Rician factor increases as the line-of-sight component prevails which also takes lesser power in the lower SNR limits. Further, in MIMO systems also the probability of error and MSE reduces in concatenation with single antenna terminals thereby exploring power control a significant possibility for implementation in massive MIMO and millimeter wave communication systems for 5G and 6G systems.

Meeting 5G network requirements with Massive MIMO

Massive MIMO (multiple-input, multiple-output) radios are the leading radio solution for new 5G deployments on mid-band TDD spectrum. The ability to use a larger number of radio chains - 16–64, for example, compared with the 2–8 typically used in conventional radio solutions - makes it possible for communication service providers (CSPs) to benefit significantly from multi-antenna techniques.

Analysis and Design of Distributed MIMO Networks With a Wireless Fronthaul

We consider the analysis and design of distributed wireless networks wherein remote radio heads (RRHs) coordinate transmissions to serve multiple users on the same resource block (RB). Specifically, we analyze two possible multiple-input multiple-output wireless fronthaul solutions: multicast and zero forcing (ZF) beamforming. We develop a statistical model for the fronthaul rate and, coupled with an analysis of the user access rate, we optimize the placement of the RRHs. This model allows us to formulate the location optimization problem with a statistical constraint on fronthaul outage. Our results are cautionary, showing that the fronthaul requires considerable bandwidth to enable joint service to users. This requirement can be relaxed by serving a low number of users on the same RB. Additionally, we show that, with a fixed number of antennas, for the multicast fronthaul, it is prudent to concentrate these antennas on a few RRHs. However, for the ZF beamforming fronthaul, it is better to distribute the antennas on more RRHs. For the parameters chosen, using a ZF beamforming fronthaul improves the typical access rate by approximately 8% compared to multicast. Crucially, our work quantifies the effect of these fronthaul solutions and provides an effective tool for the design of distributed networks.

Magnetic Field Leakage Cancellation in Multiple-Input Multiple-Output Wireless Power Transfer Systems

This letter presents a method to cancel magnetic field leakage while maintaining high power transfer efficiency (PTE) in multiple-input multiple-output wireless power transfer systems. To achieve low leakage and high PTE, the problem of PTE maximization under the cancellation condition of the magnetic field leakage is solved, which is a maximization problem of affinely constrained Rayleigh quotients. Subsequently, the optimal amplitudes and phases of the input voltages at the transmitter array and the optimal load impedances at the receivers are shown. This method can further reduce the magnetic field leakage by up to 31 dB compared with the PTE maximization method, while having a PTE drop within 5.5%.

Joint Spatial Division and Coaxial Multiplexing for Downlink Multi-User OAM Wireless Backhaul

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies (SEs). However, the existing research on OAM wireless communications is mainly focused on point-to-point transmission in the line-of-sight (LoS) scenario. In this paper, we propose an overall scheme of the downlink multi-user OAM (MU-OAM) wireless backhaul based on uniform circular arrays (UCAs) for broadcasting networks, which can achieve the joint spatial division and coaxial multiplexing (JSDCM). A salient feature of the proposed downlink MU-OAM wireless backhaul systems is that the channel matrices are completely characterized by the position of each small base station (SBS), independent of the numbers of subcarriers and antennas, which avoids estimating large channel matrices required by the traditional downlink multi-user multiple-input multiple-output (MU-MIMO) wireless backhaul systems. Thereafter, we propose an OAM-based multi-user distance and angle of arrival (AoA) estimation method, which is able to simultaneously estimate the positions of multiple SBSs with a flexible number of training symbols. With the estimated distances and AoAs, a MU-OAM preprocessing scheme is applied to eliminate the co-mode and inter-mode interferences in the downlink MU-OAM channel. At last, the proposed methods are extended to the downlink MU-OAM-MIMO wireless backhaul system equipped with uniform concentric circular arrays (UCCAs), for which much higher spectral efficiency (SE) and energy efficiency (EE) than traditional MU-MIMO systems can be achieved. Both mathematical analysis and simulation results validate that the proposed scheme can effectively eliminate both interferences of the practical downlink MU-OAM channel and approaches the performance of the ideal MU-OAM channel.

Adaptive Impedance Matching Scheme for Magnetic MIMO Wireless Power Transfer System

In coupled magnetic resonance (CMR) wireless energy transfer systems, the energy transfer power is low and the power transfer efficiency changes with the coil position. One reason for this reduction in power and efficiency is the impedance mismatching (IM) between the Tx and Rx coils; achieving impedance matching for multiple-input multiple-output (MIMO) CMR IM wireless power transmission (WPT) is quite complex due to the uncertainty in the number of coils and the interaction between coils. In this paper, we provide an analytical model of MIMO CMR which fully formulates the complex relationship between multiple Tx and Rx channels. Then, we design an impedance matching network (IMN) for MIMO CMR and derive an optimal IM solution. Base on this solution, we also develop an adaptive impedance matching scheme to control IMN, based on an automatic analysis of MIMO CMR system; the resulting control scheme achieves optimal values for transmission power and efficiency through IMN and coil selection. The simulation results indicate that the scheme is able to automatically adjust the impedance matching network according to the changes of the relative positions between Tx and Rx coils to achieve high energy transfer power and efficiency.

Hardware-Aware Beamspace Precoding for All-Digital mmWave Massive MU-MIMO

Massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems operating at millimeter-wave (mmWave) frequencies enable simultaneous wideband data transmission to a large number of users. In order to reduce the complexity of MU precoding in all-digital basestation architectures, we propose a two-stage precoding architecture that first performs precoding using a sparse matrix in the beamspace domain, followed by an inverse fast Fourier transform that converts the result to the antenna domain. The sparse precoding matrix requires a small number of multipliers and enables regular hardware architectures, which allows the design of hardware-efficient all-digital precoders. Simulation results demonstrate that our methods approach the error-rate of conventional Wiener filter precoding with more than $2\times $ reduced complexity.