Multiple-input Multiple-out Research Articles

Joint Moving Target and Antenna Localization for Distributed MIMO Radar With a Calibration Object

This paper investigates the problem of simultaneously locating the moving target and refining erroneous antenna parameters with a calibration object in distributed multiple-input multiple-out (MIMO) radars. Albeit traditional methods achieve moving target localization with antenna parameter errors, they still have some disadvantages, such as the high computational complexity of joint localization or the high noise sensitivity of estimating the target parameters. To overcome the drawbacks of existing algorithms, we first formulate a new localization model that joint target and antenna localization with a calibration object and then present an algebraic solution. Unlike previous work, the proposed estimator has additional unknowns of antenna parameter errors and presents new matrix equations by incorporating the calibration measurements. The proposed method is shown theoretically to attain the Cramer–Rao lower bound (CRLB) accuracy both in theory and simulations under small measurement noise and antenna error levels. Furthermore, simulation results show that the proposed method significantly improves the localization performance compared with existing methods.

Space–time-range clutter suppression via tensor-based STAP for airborne FDA-MIMO radar

Frequency diverse array (FDA) multiple-input multiple-out (MIMO) radar has an inherent higher-dimensional data structure that can be exploited in temporal, spatial, and range-dependent dimensions for space–time-range adaptive processing (STRAP). This paper addresses the problem of tensor filters in application to space–time-range clutter suppression for FDA-MIMO radar. The characteristics of space–time-range clutter are discussed to establish the multidimensional signal models. FDA-MIMO radar can divide the secondary range cell within the range resolution cell of MIMO radar when they have the same matching filter bandwidth in the receive channel. Specifically, the target-free covariance information can be estimated from more secondary training cells for FDA-MIMO radar. According to the discussed properties of space–time-range clutter, namely secondary clutter rings, clutter degrees of freedom (DOFs), and the range-dimensional non-stationary characteristic, a tensor-based STRAP approach with range compensation is proposed to suppress clutter for FDA-MIMO radar. Numerical results verify the mentioned clutter characteristics and illustrate the importance of the samples number and range compensation operation for the proposed tensor filter. Finally, the STRAP performance benefits of FDA-MIMO radar over MIMO radar are discussed by simulations.

Dual-band dual-polarized MIMO design for vehicular applications

A low-cost dual-band dual-polarized Multiple input–Multiple out (MIMO) antenna system is proposed. The proposed configuration is based on a singly fed single-layer patch antenna, working in the 4400-5000 MHz and 5925-7125 MHz frequency bands. Based on surface current analysis, dual-band dual-polarized operation is achieved through structure modification by cutting the edges of the patch and adding a slot. A size reduction of 24.08% is achieved in the lower frequency band. The radiation mechanism of the antenna is explained using characteristics mode analysis. The proposed design has a similar radiation characteristic in both bands along with orthogonal linear polarization. The minimum axial ratios of 18.23 dB and 12 dB are observed in both bands. The proposed 4-element MIMO antenna system has a compact size of 40.3 mm × 40.3 mm × 1.6 mm, minimum inter-element isolation of −20 dB, and an optimal envelope correlation coefficient value of 0.03, hence making it a suitable candidate for future Internet of Vehicles applications.

OAM-SDM Solution Toward a Submarine Cable

Determining how to add more signal channels without enlarging the cable volume is continually an issue in managing submarine cables. To improve the cost per bit and the power efficiency, space division multiplexing (SDM) is regarded as a promising solution that could be used to address optical density and cable capacity challenges regarding submarine cables. In this study, we focus on angular momentum (OAM) modes supporting few-mode ring-core fiber (RCF) solutions for submarine cables. Considering the inhibition of the hydrogen loss for the submarine cable, we propose a center-region-lifted 3-OAM-mode-group supporting RCF (abbreviated to 3-OAM-MG RCF) with a core-cladding index contrast of 0.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> , and meanwhile we also design and fabricate a matched 3-OAM-MG erbium-doped fiber (EDF). To confirm the short-haul transmission ability, we conduct a wavelength/mode/polarization division multiplexing (WDM-MDM-PDM) transmission test through a 25.3-km 3-OAM-MG RCF, where the coupled modes inside MG are demultiplexed by using 2 × 2 multiple-input multiple-out (MIMO) equalization. The transmission capacity is approximately 35 Tbit/s and the bit error rates (BERs) of all the channels are less than 2.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 10^{-2}$</tex-math></inline-formula> . For the application scenario of long-haul submarine communication, a cascade WDM-MDM transmission system consisting of 3-OAM-MG RCF and 3-OAM-MG EDF is set up, which makes the BERs of all the mode and wavelength channels below the 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> soft-decision forward error correction (SD-FEC) threshold. It indicates that data-carrying OAM mode multiplexing transmission has potential application in a long-haul submarine cable system.

EXIT-Aided Scheduled Iterative MIMO Detection Under Non-Homogeneous Antenna Propagation Gain Scenarios

The non-homogeneous antenna propagation gain, potentially caused by the diverse large-scale fading effects in wireless communication channels, has a significant impact on the reliabilities of multiple-input multiple-out (MIMO) systems. We propose to utilize extrinsic information transfer (EXIT) to analyze the convergence characteristic of the factor graph (FG) based iterative MIMO detection mechanisms in an effective way. Based on the EXIT analysis, we propose a low-complexity scheduled algorithm for FG-based iterative MIMO detection, which speeds up the convergence of the mutual information exchange between the variable nodes and the observation nodes. To address the complexity issue in 5G new radio (NR) systems, where low-density parity check (LDPC) codes are adopted for data transmission, we also extend the proposed algorithm to concatenated detection and decoding in MIMO-LDPC systems to achieve low complexity. Simulation results show that the convergence speed of MIMO detection can be improved by at most 50%, while for the MIMO-LDPC system, the proposed algorithm can achieve 2 dB gain compared to the conventional minimum mean square error (MMSE) detection mechanism.

Moving target localization in distributed MIMO radar systems with sensor position errors in the presence of a calibration object

This paper investigates the moving target localization problem in distributed multiple-input multiple-out (MIMO) radars with sensor position errors. Unlike the previous work, by invoking a moving calibration object, we develop a closed-form solution to determine the position and velocity of the unknown target for alleviating the performance degradation caused by sensor position errors in traditional methods. The proposed method has three stages. The calibration object is utilized in the first stage to estimate the sensor position errors and improve the sensor parameters, and these parameters are devoted to establishing the pseudo-linear equations by introducing nuisance variables in the second stage. After solving the pseudo-linear equations, the target parameters are refined by exploiting the relationships between the extra variables and target parameters in the final stage. The proposed method is shown analytically to reach the Cramér-Rao lower bound (CRLB) performance under sufficiently small measurement noise and sensor localization error conditions, and it can be easily extended to general cases where exist imprecise calibration object parameters or multiple calibration objects. Simulation results corroborate the theoretical analysis and show that the proposed method has a significant performance improvement over the state-of-the-art methods.

Parameter estimation of MIMO FSO systems using saddlepoint approximation

In this paper, the challenges of parameter estimation for multiple-input multiple-out (MIMO) free-space optical (FSO) communication systems employing equal gain combining diversity techniques over the Gamma–Gamma turbulence channels with pointing errors are addressed. According to the goodness-of-fit statistical tests results previously reported by Kolmogorov-Smirnov, the approximate probability density function obtained by a saddlepoint approximation (SAP) method provide a highly efficient approximation of the actual channel models over a wide range of channel conditions. To reveal the importance of the SAP approximation method, we propose a maximum likelihood estimation algorithm to estimate its parameters. The accuracy of the proposed estimator is investigated by using the mean square error (MSE) and normalized mean square error (NMSE); they exhibit satisfactory performance in both noiseless and noisy environments. The bit error rate (BER) performance for MIMO FSO systems with an SAP estimator is also discussed.

Joint Altitude and Hybrid Beamspace Precoding Optimization for UAV-Enabled Multiuser mmWave MIMO System

The combination of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) multiple-input multiple-out (MIMO) system is regarded as a key enabling technology for beyond 5G networks, as it provides high data rate aerial links. However, establishing UAV-enabled mmWave MIMO communication is quite challenging due to the high hardware cost in terms of radio frequency (RF) chains. As a cost-effective alternative, a beamspace precoding with discrete lens arrays (DLA) architecture has received considerable attention. However, the underlying optimal design in beamspace precoding has not been fully exploited in UAV-enabled communication scenario. In this paper, the joint design of the UAV’s altitude and hybrid beamspace precoding is proposed for the UAV-enabled multiuser MIMO system, in which the DLA is exploited to reduce the number of the RF chain. In the proposed scheme, the optimization problem is formulated as a minimum weighted mean squared error (MWMSE) method. Then an efficient algorithm with the penalty dual decomposition (PDD) is proposed that aims to jointly optimize the altitude of UAV, beam selection and digital precoding matrices. Simulation results confirm the comparable performance of the proposed scheme and perform close to full-digital beamforming in terms of achievable spectral efficiency.

Multi-Dimensional Polarized Modulation for Land Mobile Satellite Communications

In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems.

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

AbstractThis work proposes a transmitter antenna selection (TAS) method for multiple‐input multiple‐out (MIMO) nonorthogonal multiple access (NOMA) that is a promising multiple access technique for the fifth generation mobile communications systems. Specifically, we propose to activate the more suitable subset of base station antennas while allocating users into appropriate NOMA clusters such that the system operates in energy efficiency mode, selecting such appropriate antenna subset indexes that maximize the sum‐rate (SR) of the NOMA‐MIMO system. Once that the TAS based on exhaustive‐search is very complex to be implemented in real communication systems, we propose an effective TAS method based on machine learning while keeping very promising performance. A convolutional neural network‐based transmitter antenna selection (CNN‐TAS) method is proposed for efficiently select antennas aiming at maximizing the system SR. Hence, extensive numerical results demonstrate that the CNN‐TAS can suitably learn the problem, performing with very high accuracy choosing properly the antenna subset that maximizes the system spectral efficiency while reducing substantially the processing time of real‐time operations.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operating without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can obtain a perfect CSI at no cost. Then, we provide four achievable schemes, where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay–destination link into two parts and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observations to the destination node. Numerical results show that, with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds that come quite close to the upper bound on a wide range of relevant system parameters.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operate without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can get the perfect CSI at no cost. Then, we provide four achievable schemes where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay-destination link into two parts, and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observation to the destination node. Numerical results show that with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds coming quite close to the upper bound on a wide range of relevant system parameters.

MIMO Radar Accurate Imaging and Motion Estimation for 3-D Maneuvering Ship Target

Image deterioration problem occurs in radar imaging for ship target, which results from the complex time-varying motions of ship, the noise in channels, and the clutter on sea surface. It is hard to be solved effectively due to coherent accumulation sampling time and high-dimensional parametric model. Hence, an accurate imaging and motion estimation method based on multiple-input–multiple-out (MIMO) radar is presented. First, the multidimensional signal model is built to characterize target features accurately. To reduce the interference from sea clutters, a preprocessing strategy is exerted based on the space–time adaptive processing (STAP) theory, clutter signals can be suppressed effectually with constructing a Doppler spectrum model. Then, for accurate imaging and motion estimation, a combined trace norm minimization problem is deduced based on the relaxation of tensor rank, where the noise in sea environments is also considered. Meanwhile, generalized tensor total variation constraint is developed to ensure stable estimation and smooth imaging results when separating the noise term. Accordingly, an effective decomposition criterion is formulated based on alternating direction multiplier method (ADMM) strategy, and motion parameters can be precisely calculated based on the least square (LS) method. Finally, theoretical analysis and simulation results present the accurate performance of the proposed method.

Transmit BeampattarnOptimization for Automotive MIMO Radar Coexistedwith Communication in V2V Networks.

Due to the flourishing development of vehicle-to-vehicle (V2V) communications and autonomous driving, interference between radar sensing and communication signals becomes a challenging issue. We propose a transmit beamforming based spectrum sharing scheme to achieve peaceful coexistence between automotive multiple-input multiple-out (MIMO) radar and communication systems. Our objective is to maximize the signal-to-interference-plus-noise ratio (SINR) of the automotive radar receiver subject to the communication capacity and the transmitted power budget constraints to optimize both the communication covariance matrix and the radar transmit precoder. The formulated optimization problem is non-convex, which is converted to convex by introducing a new slack variable and then solving it using the block coordinate descent, also called alternation optimization, approach. Additionally, the ellipsoid sub-gradient method is then employed to reduce the computational complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional schemes.

MIMO channels: optimizing throughput and reducing outage by increasing multiplexing gain

The two main aims of deploying multiple input multiple out (MIMO) are to achieve spatial diversity (improves channel reliability) and spatial multiplexing (increase data throughput). Achieving both in a given system is impossible for now, and a trade-off has to be reached as they may be conflicting objectives. The basic concept of multiplexing: divide (multiplex) transmit a data stream several branches and transmit via several (independent) channels. In this paper, we focused mainly on achieving spatial multiplexing by modeling the channel using the diagonal Bell Labs space time scheme (D-BLAST) and the vertical Bell Labs space time architecture (V-BLAST) Matlab simulations results were a lso given to further compare the advantages of spatial multiplexing.

Beamspace Precoding and Beam Selection for Wideband Millimeter-Wave MIMO Relying on Lens Antenna Arrays

Millimeter-wave (mmWave) multiple-input multiple-out (MIMO) systems relying on lens antenna arrays are capable of achieving a high antenna-gain at a considerably reduced number of radio frequency (RF) chains via beam selection. However, the traditional beam selection network suffers from significant performance loss in wideband systems due to the effect of beam squint. In this paper, we propose a phase shifter-aided beam selection network, which enables a single RF chain to support multiple focused-energy beams, for mitigating the beam squint in wideband mmWave MIMO systems. Based on this architecture, we additionally design an efficient transmit precoder (TPC) for maximizing the achievable sum-rate, which is composed of beam selection and beamspace precoding. Specifically, we decouple the design problems of beamspace precoding and beam selection by exploiting the fact that the beam selection matrix has a limited number of candidates. For the beamspace precoding design, we propose a successive interference cancellation (SIC)-based method, which decomposes the associated optimization problem into a series of subproblems and solves them successively. For the beam selection design, we propose an energy-max beam selection method for avoiding the high complexity of exhaustive search, and derive the number of required beams for striking an attractive trade-off between the hardware cost and system performance. Our simulation results show that the proposed beamspace precoding and beam selection methods achieve both a higher sum-rate and a higher energy efficiency than its conventional counterparts.

An effective localization method for mixed far-field and near-field strictly non-circular sources

In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-circularity of the sources, an extended signal is generated by concatenating the received array data and its conjugate counterparts. Then the DOAs of far-field signals are estimated based on the extended covariance matrix with the traditional MUSIC algorithm. After eliminating the far-field components from the extended signal subspace, the extended covariance matrix of the near-field signals is obtained. Thus a near-field estimator is constructed based on symmetric property of the extended array manifold where the generalized ESPRIT method is adopted to estimate the DOAs of near-field sources. Finally, the range estimator is derived using the DOA estimations of near-field sources. Simulation results are provided to validate that the proposed method has achieved a better performance than existing ones and is quite suitable for massive MIMO (multiple-input multiple-out) system.

Angle estimation in MIMO radar using a new sparse representation approach

ABSTRACTExisting direction of arrival (DOA) estimation methods in multiple-input multiple-out (MIMO) radar systems will encounter the performance degradation in the cases of few snapshots, low signal-to-noise ratio (SNR), closely spaced targets, or strongly correlated sources. To improve it, this paper develops a new sparse representation-based DOA estimation method. The main contributions are as follows: i) we construct a new real-valued double weighted -norm minimisation model; ii) we derive an improved reduced-dimension technique to enhance estimation accuracy; and iii) we design optimal and sparse weights carefully to improve the corresponding estimation accuracy. Finally, the effectiveness and theoretical analysis of the presented approach are verified by extensive numerical simulations, which proves that the new algorithm performs well at low SNR and with a small number of snapshots as well as at the coherent source case.

The Polarization Sensitive FDA-MIMO Radar: Performance Analysis of Beamforming Based on Oblique Projection

In this paper, a beamforming algorithm based on oblique projection technique is proposed for a hybrid radar of polarization sensitive array (PSA), frequency diversity array (FDA) and multiple-input multiple-out (MIMO) radar, termed as PSFDA-MIMO radar. Firstly, the signal model of the proposed PSFDA-MIMO radar is established. Secondly, oblique projection technique is proposed to solve the beamforming problem of the novel radar. Finally, the simulation results show that the overall performance of beamforming based on the proposed radar is better than that of other combined radar systems.

PCS Threshold Selection for Spatial Reuse in High Density CSMA/CA MIMO Wireless Networks

Multiple-input multiple-out (MIMO) equipment improves the spectral efficiency of wireless local area network (WLAN) systems. However, in a large-scale or dense MIMO WLANs, overlapping radio cells or basic service sets (BSSs) are inherent. This prevents multiple concurrent transmissions and degrades spatial reuse. The inability to separate multiple simultaneous transmissions in space is detrimental to overall system performance. The carrier sense multiple access collision avoidance (CSMA/CA) protocol uses the physical carrier sensing (PCS) threshold to determine channel state at the physical layer (PHY) and decide on the number of concurrent transmissions allowed per time slot. Since the PCS threshold determines the spatial reuse under the CSMA/CA protocol, which consequently determines the interference level and the network aggregate throughput, we address PCS threshold selection for dense uplink (UL) MIMO WLAN systems. A closed-form expression is derived for selecting the PCS threshold based on the fundamental parameters of the network where nodes are randomly placed according to a Poisson point process (PPP). We obtain the PCS threshold value that maximizes the spatial density of throughput (SDT) and study the effectiveness of the proposed framework under moderate to high node density. In addition, we analyze the effect of WLAN density on interference from concurrent transmitters. The key observation is that PCS threshold selection should take into account key network characteristics including node density, target SINR or threshold, path-loss exponent and antenna configuration.