Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications

As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramer–Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment.

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Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

Facing the demand for decoupling and filtering for large-scale antenna arrays in modern communication systems, a transmission-line-based scheme is proposed and studied in this article. Different from other decoupling networks published recently featuring narrow decoupling bandwidths and high spurious levels, the proposed approach uses simple T-shaped networks where decoupling and filtering responses are realized simultaneously, leading to high-frequency selectivity and improved decoupling bandwidth. The proposed design is a simple 1-D configuration but powerful for 2-D arrays. Based on the study case of a $4\times {4}$ dual-polarized patch array, theoretical analysis and full-wave simulation are carried out to verify the performance in decoupling and frequency selectivity of this method. A prototype is further fabricated, assembled, and measured to demonstrate the performance of the proposed method in practice. The measured and simulated results are consistent with each other where a low insertion loss of around 0.6 dB is observed. The results denote that the proposed method is easily realized with a very small effect on the radiation performance of antenna elements, making it to be a potential and valuable decoupling and filtering solution for large-scale arrays.

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A Simple Decoupling Network With Filtering Response for Patch Antenna Arrays

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization. 

Multi-UAV Cooperative Localization for Marine Targets Based on Weighted Subspace Fitting in SAGIN Environment

Millimeter wave (mmWave) vehicular channels are highly dynamic, and the communication link needs to be reconfigured frequently. In this work, we propose to use a passive radar receiver at the roadside unit to reduce the training overhead of establishing an mmWave communication link. Specifically, the passive radar will tap the transmissions from the automotive radars of the vehicles on the road. The spatial covariance of the received radar signals will be estimated and used to establish the communication link. We propose a simplified radar receiver that does not require the transmitted waveform as a reference. To leverage the radar information for beamforming, the radar azimuth power spectrum (APS) and the communication APS should be similar. We outline a radar covariance correction strategy to increase the similarity between the radar and communication APS. We also propose a metric to compare the similarity of the radar and communication APS that has a connection with the achievable rate. We present simulation results based on ray-tracing data. The results show that: (i) covariance correction improves the similarity of radar and communication APS, and (ii) the radar-assisted strategy significantly reduces the training overhead, being particularly useful in non-line-of-sight scenarios.

Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links

In the practical radar with multiple antennas, the antenna imperfections degrade the system performance. In this paper, the problem of estimating the direction of arrival (DOA) in a multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling effect between antennas is investigated. To exploit the target sparsity in the spatial domain, the compressed sensing based methods have been proposed by discretizing the detection area and formulating the dictionary matrix, so an off-grid gap is caused by the discretization processes. In this paper, different from the present DOA estimation methods, both the off-grid gap due to the sparse sampling and the unknown mutual coupling effect between antennas are considered at the same time, and a novel sparse system model for DOA estimation is formulated. Then, a novel sparse Bayesian learning (SBL)-based method named sparse Bayesian learning with the mutual coupling (SBLMC) is proposed, where an expectation-maximum-based method is established to estimate all the unknown parameters including the noise variance, the mutual coupling vectors, the off-grid vector, and the variance vector of scattering coefficients. Additionally, the prior distributions for all the unknown parameters are theoretically derived. With regard to the DOA estimation performance, the proposed SBLMC method can outperform state-of-the-art methods in the MIMO radar with unknown mutual coupling effect, while keeping the acceptable computational complexity.

Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling

In this article, a novel slot-array defected ground structure (DGS) for decoupling microstrip antenna array is proposed. The slot-array DGS is etched surrounding each antenna element on the ground plane and parallel to the radiating edges of each antenna element. The decoupling mechanism is elucidated via an equivalent circuit model and the coupled current field analysis, which reveals slot-array DGS has the spatial band-stop characteristic and changes the direction of the partially coupled current, respectively. Both characteristics of the slot-array DGS contribute to mutual coupling reduction. Three practical design examples of applying slot-array DGS to single-linearly polarized (LP), dual-LP, and compact circularly polarized (CP) antenna array are given to illustrate the design process and considerations. The simulated and measured results show that about 50 dB isolation enhancement is obtained by using the slot-array DGS when the edge-to-edge spacing between CP antenna elements is 0.057 wavelength. Additionally, a wheel-shaped absorber based on the electromagnetic loss material is designed and fabricated to reduce the backward radiation caused by slot-array DGS. The absorber has an absorptivity of more than 95% in the frequency range of 1.2–1.35 GHz and suppresses the backward radiation over 12.5 dB in the plane phi = 0° and 16.1 dB in the plane phi = 90° without deteriorating other antenna performances.

A Novel Slot-Array Defected Ground Structure for Decoupling Microstrip Antenna Array

The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.

A New Atomic Norm for DOA Estimation With Gain-Phase Errors

In millimeter wave (mmWave) communications, the feature of relatively large signal absorption and directional transmission render new challenges for wireless communications and signal processing. To further improve the performance of mmWave communications, a novel radar-aided mmWave communication (RAMC) approach is proposed, which can be used in vehicular communications. There are two parts in the proposed RAMC system, including the radar subsystem and the mmWave communication subsystem. In the radar subsystem, the bistatic co-prime multi-input and multi-output (MIMO) arrays are considered. With the radar antenna arrays, both the directions of departure (DoD) and the directions of arrival (DoA) are estimated. Additionally, the compressed sensing (CS)-based method is proposed to obtain the target positions. Using the estimated angle and position information, the channel estimation and feedback link of the mmWave communication subsystem can be eliminated. Moreover, a hybrid beamforming algorithm is proposed in the mmWave communication subsystem, which can overcome the shortage of the analog-only beamforming. Simulation results show that the better estimation performance can be achieved by the bistatic co-prime MIMO arrays than that by the traditional uniform linear arrays (ULA), and with the radar aided, the mmWave communication subsystem can reduce the beam search time, and the cell discovery time is improved significantly.

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramer–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

https://www.mdpi.com/2079-9292/7/11/341/pdf

Zhenxin Cao

Papers

Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling

A Novel Slot-Array Defected Ground Structure for Decoupling Microstrip Antenna Array

A New Atomic Norm for DOA Estimation With Gain-Phase Errors

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect