Complex Propagation Environments Research Articles

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

LEO Satellite Access Network (LEO-SAN) Toward 6G: Challenges and Approaches

With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network. Aside from terrestrial base stations, satellite nodes, especially the low-earth-orbit (LEO) satellites, can also serve as base stations for Internet access, and constitute the LEOsatellite access network (LEO-SAN). LEO-SAN is expected to provide seamless massive access and extended coverage with high signal quality. However, its practical implementation still faces significant technical challenges, such as high mobility and limited budget for communication payloads of LEO satellite nodes. This paper aims at revealing the main technical issues that have not been fully addressed by the existing LEO-SAN designs, from three aspects namely random access, beam management and Doppler-resistant transmission technologies. More specifically, the critical issues of random access in LEO-SAN are discussed regarding low flexibility, long transmission delay, and inefficient handshakes. Then the beam management for LEOSAN is investigated in complex propagation environments under the constraints of high mobility and limited payload budget. Furthermore, the influence of Doppler shifts on LEO-SAN is explored. Correspondingly, promising technologies to address these challenges are also discussed, respectively. Finally, the future research directions are envisioned.

Research on electromagnetic scattering and radio wave propagation algorithm based on machine learning in half-space

In this paper, to investigate the problem of radio wave propagation in complex environments, a new algorithm is proposed based on the equivalence principle algorithm and the domain decomposition method to rapidly and accurately predict electromagnetic scattering and radio wave propagation in half-space. First, equivalent surfaces are introduced to decompose the obstacle region and replace the scattering effect of obstacles within the equivalent surfaces with equivalent input and output electromagnetic currents. Then, the operator S, representing the relationship between the input and output equivalent electromagnetic currents on the equivalent surfaces, and the operator T, representing the interactions between the equivalent electromagnetic currents on each equivalent surface, are derived; this allows the calculation process of the overall spatial field in a complex environment to be simplified to the iterative calculation process of operators S and T. To reduce the complexity of calculating S, a neural network embedded with physical information is introduced to replace the calculation process. Finally, the training results are applied to an example and compared with the theoretical numerical results yielded by the method of moments in different cases with obstacles. The results reveal that the new method proposed in this paper is highly accurate and achieves a substantial improvement in calculation speed. Therefore, our proposed algorithm provides a new and reliable method for calculating the spatial field in complex environments.

Intelligent Reflecting Surface-Enhanced UAV Communications: Advances, Challenges, and Prospects

As a cost-effective aerial platform, unmanned aerial vehicles (UAVs) can provide reliable air-to-ground (A2G) line-of-sight (LOS) transmission and controllable maneuverability. However, the LOS links may deteriorate severely in some complex propagation environments, such as in urban areas. In addition, UAV communications often suffer from serious interference caused by the A2G channel. To address these challenges, intelligent reflecting surface (IRS) has been recently proposed to compensate for the path loss and reduce interference. In this article, after briefly introducing the comprehensive background of UAV communications and IRS-based beamforming, the main advantages and the potential of IRS-assisted UAV communications are discussed. Finally, based on a novel active IRS, two case studies are investigated where the transmission beamforming and the active IRS's reflecting coefficient are jointly optimized. In the first case, transmit power of UAVborne base stations can be greatly reduced while ensuring the achievable secure rate. Based on the first case, in the second one, considering the channel error caused by UAV jittering, a robust and secure beamforming was presented. Simulation results are provided to demonstrate the better performance enhancement brought by combining UAVs and active IRSs. Finally, some challenging issues to be resolved for practical implementations were discussed.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification

Wireless propagated signals encapsulate rich information for high-accuracy localization and environment sensing. However, the full exploitation of positional and environmental features as well as their correlation remains challenging in complex propagation environments. In this paper, we propose a methodology of variational inference over deep neural networks for concurrent distance estimation and environmental identification. The proposed approach, namely inter-instance variational auto-encoders (IIns-VAEs), conducts inference with latent variables that encapsulate information about both distance and environmental labels. A deep learning network with instance normalization is designed to approximate the inference concurrently via deep learning. We conduct extensive experiments on real-world datasets and the results show the superiority of the proposed IIns-VAE in both distance estimation and environmental identification compared to conventional approaches.

Direction of Arrival Estimation of Coherent Wideband Sources Using Nested Array.

Due to their ability to achieve higher DOA estimation accuracy and larger degrees of freedom (DOF) using a fixed number of antennas, sparse arrays, etc., nested and coprime arrays have attracted a lot of attention in relation to research into direction of arrival (DOA) estimation. However, the usage of the sparse array is based on the assumption that the signals are independent of each other, which is hard to guarantee in practice due to the complex propagation environment. To address the challenge of sparse arrays struggling to handle coherent wideband signals, we propose the following method. Firstly, we exploit the coherent signal subspace method (CSSM) to focus the wideband signals on the reference frequency and assist in the decorrelation process, which can be implemented without any pre-estimations. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. Next, an enhanced spatial smoothing algorithm is applied to make full use of the information available in the data covariance matrix, as well as to improve the decorrelation effect, after which stage the multiple signal classification (MUSIC) algorithm is used to obtain DOA estimations. In the simulation, with reference to the root mean square error (RMSE) that varies in tandem with the signal-to-noise ratio (SNR), the algorithm achieves satisfactory results compared to other state-of-the-art algorithms, including sparse arrays using the traditional incoherent signal subspace method (ISSM), the coherent signal subspace method (CSSM), spatial smoothing algorithms, etc. Furthermore, the proposed method is also validated via real data tests, and the error value is only 0.2 degrees in real data tests, which is lower than those of the other methods in real data tests.

ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.

Broadband Multiple Input Multiple Output antenna for sub‐6‐GHz 5G communications

SummaryThis paper presents the design of a miniaturized broadband monopole antenna for 5G and Wireless Local Area Network (WLAN) applications in mobile handsets. The proposed monopole evolved from a rectangular geometry of size 12 × 5 mm. The slot and stub loading techniques are used to improve the impedance matching offered by the antenna. Furthermore, bandwidth broadening is achieved using lumped elements loaded onto the aperture of the antenna. The proposed miniaturized antenna exhibits a measured impedance bandwidth of 63.6% (3.0–5.8 GHz) covering the 5G spectrum allocations under sub‐6 GHz and the WLAN services. The antenna elements are replicated along the sides of the mock mobile handset PCB to study the functionality of the eight‐element MIMO antenna. The prototype MIMO antenna fabricated and tested in the laboratory offers a peak gain of 3 dBi and total efficiency greater than 72%. Owing to miniaturization, the spatial distribution of the antenna element provides a low envelope correlation (ECC) of less than 0.2 and good diversity gain (DG) greater than 7.8 dB. In addition, the mean effective gain (MEG), channel capacity loss (CCL), multiplexing efficiency (ME), and total active reflection coefficient (TARC) are evaluated and presented. The estimated MIMO metrics are within the desired range of operation and hence make the antenna suitable for a complex propagation environment. The prototype antenna is developed on a thin microwave laminate with low‐loss characteristics and tested under laboratory conditions. The outcomes indicate that the proposed eight‐element antenna can be applied to 5G MIMO communications.

A method for resolving the phase difference ambiguity in interferometric monopulse angle of arrival estimation

This paper presents a method to eliminate the phase difference ambiguity in angle of arrival (AOA) estimation using the radio-frequency-based interferometric monopulse technique. In order to uniquely determine the AOA, we utilize four directivities which are formed by using selected elements with the same weight function as the AOA estimation. We investigate the AOA estimation accuracy for the direction of the incoming wave from a high elevation angle and in multi-path propagation environments using a three-dimensional fading emulator. The results show that the proposed method is valid for complex propagation environments.

An Iterative Implementation-Based Approach for Joint Source Localization and Association Under Multipath Propagation Environments

We address the problem of joint source localization and association (JSLA) under a multipath propagation environment for sensor arrays. Taking into account inaccurate prior information in practical applications, we propose a JSLA algorithm based on the iterative implementation of the minimum mean-square error (MMSE) framework with semi-unitary and sparsity constraints and the subspace technique. The proposed algorithm exploits benefits of both spatial signals' sparse characteristic and coherence structure when localizing unknown sources in a mixed interference environment. In contrast with previous works, the proposed algorithm can associate the incident paths to each source from the complex propagation environment with improved association performance even under low signal-to-noise ratio conditions. Neither additional decorrelation preprocessing nor prior information pertaining to the multipath propagation is required. Both simulations and real data experiments demonstrate the effectiveness and robustness of the proposed algorithm.

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

Practical Approach to Radar Detection Performance for NCI System

In noncoherent integration (NCI) radar systems, analyzing detection probability is a basis for designing and evaluating the system performance. Most previous studies have approached it with a complex integrand: a joint probability distribution function of signal components. In calculating the detection probability of NCI systems, some numerical computation techniques are essential in the numerical integration process. The calculation becomes even more intricate when the system considers complex clutter or propagation environment, as well as additive white Gaussian noise. This letter discusses a practical approach to estimate radar detection performance without using complicated numerical methods. This is based on the ideas of how the mixed signal sample follows sample mean properties and central limit theorem under the NCI system. Some comparisons are given against the Swerling II case of target fluctuation and compound sea clutter. The result shows how much this approach is practically applicable in design and analysis of the NCI system.

MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) continues to increase. As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures, including satellites and unmanned aerial vehicles (UAVs). Thus, we can build a non-terrestrial network (NTN) using a cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN must be empowered using mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN, which makes it difficult to coordinate the resources. In this study, we propose a process-oriented framework to design communication and MEC systems in a time-division manner. In this framework, large-scale channel state information (CSI) is used to characterize the complex propagation environment at an affordable cost, where a nonconvex latency minimization problem is formulated. Subsequently, the approximated problem is provided, and it can be decomposed into sub-problems. These sub-problems are then solved iteratively. The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms, implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources, and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

A performance predictor of beamforming versus time-reversal based far-field wireless power transfer from linear array

For far-field wireless power transfer (WPT) in a complex propagation environment, a time-reversal (TR) based WPT that can overcome the drawbacks of conventional beamforming (BF) by taking advantage of multipath has been recently proposed. However, due to the WPT performance of BF and TR depending on the complexity of the propagation environment, the performance prediction between BF versus TR would be required. We present a detailed and generalized analysis of the recently proposed performance metric referred to as the peak received power ratio (PRPR) for linear array-based WPT. Here, the effectiveness of PRPR is verified via measurement for free space and indoor scenarios. The results demonstrate that PRPR is directly related to the complexity of the propagation environment and the corresponding power transmission capability of BF and TR. That is, the higher the complexity, the greater the value of PRPR and TR outperforms BF with higher peak power given the same average transmit power and vice versa. The mode decision between BF and TR based on PRPR potentially promises efficient far-field WPT even in a dynamic propagation environment.

Multiscale modeling and simulation methods for electromagnetic and multiphysics problems

Multiscale modeling and simulation methods for electromagnetic and multiphysics problems

Reconfigurable Intelligent Surface-Assisted Aerial-Terrestrial Communications via Multi-Task Learning

The aerial-terrestrial communication system constitutes an efficient paradigm for supporting and complementing terrestrial communications. However, the benefits of such a system cannot be fully exploited, especially when the line-of-sight (LoS) transmissions are prone to severe deterioration due to complex propagation environments in urban areas. The emerging technology of reconfigurable intelligent surfaces (RISs) has recently become a potential solution to mitigate propagation-induced impairments and improve wireless network coverage. Motivated by these considerations, in this paper, we address the coverage and link performance problems of the aerial-terrestrial communication system by proposing an RIS-assisted transmission strategy. In particular, we design an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame. On this basis, we formulate an RIS-assisted transmission strategy optimization problem as a mixed-integer non-linear program (MINLP) to maximize the overall system throughput. We then employ multi-task learning to speed up the solution to the problem. Benefiting from multi-task learning, the computation time is reduced by about four orders of magnitude. Numerical results show that the proposed RIS-assisted transmission protocol significantly improves the system throughput and reduces the transmit power.

Diffuse field cross-correlation in a programmable-metasurface-stirred reverberation chamber

Programmable metasurfaces can endow complex scattering environments with reconfigurability. Here, we make use of these configurational degrees of freedom to retrieve the impulse response between two passive antennas via cross-correlation techniques. An ensemble of stirred chaotic wave fields in a reverberation chamber (RC) can play the role of thermal noise in passive Green's function retrieval. Instead of using a conventional mechanical mode stirrer, we generate the ensemble of RC configurations with random configurations of a programmable metasurface. We adapt the data processing of the diffuse field cross-correlation technique to this stirring mechanism, which is, given the size of the RC, nominally inefficient, and we investigate the convergence of the cross-correlated signals toward the impulse response. Finally, we apply our scheme to phaseless imaging in complex propagation environments, with potential applications in indoor context-awareness.

Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments

Time Reversal (TR) techniques allow optimal refocusing of ultrawideband (UWB) electromagnetic waves in complex propagation environments, due to the invariance of the wave equations. Propagation of electromagnetic waves through dispersive or lossy media breaks this invariance. Consequently, such media degrade the refocusing ability of TR techniques. In this work, we propose a novel algorithm for the enhancement of the UWB TR microwave imaging resolution in dispersive environments. The presented algorithm takes into account the frequency-dependent complex permittivity of the propagation medium across the entire bandwidth of the UWB pulse. Using this complex permittivity, it models the medium-, time-, and frequency- dependent attenuation in the wavelet domain to create inverse filters which compensate for the effects of the attenuation. This is the first algorithm that constructs inverse filters in the wavelet domain using the complex permittivity of the dispersive propagation medium across the entire bandwidth, rather than at a center frequency, of the excitation pulse. We also introduce a smart scaling concept to minimize undesired noise amplification. While our proposed approach is agnostic to the application scenario and thus could be utilized in various disciplines, we apply it in a biomedical imaging scenario and achieve enhancement in the resolution of UWB microwave TR imaging of a simulated brain tumor inside the digital human phantom (DHP).

Robust position sensing with wave fingerprints in dynamic complex propagation environments

Irregular propagation environments with complex scattering effects challenge traditional ray-tracing-based localization. However, the environment's complexity enables solutions based on wave fingerprints (WFPs). Yet, since WFPs rely on the extreme sensitivity of the chaotic wave field to geometrical details, it is not clear how viable WFP techniques may be in a realistic dynamically evolving environment. Here, we reveal that environmental perturbations reduce both the diversity of the WFP dictionary and the effective signal-to-noise ratio (SNR), such that the amount of information that can be obtained per measurement is reduced. This unfavorable effect can, however, be fully compensated by taking more measurements. We show in simulations and experiments with a low-cost software-defined radio that WFP localization of non-cooperative objects is possible even when the scattering strength of the environmental perturbation significantly exceeds that of the object to be localized. Our results underline that diversity is only one important ingredient to achieve high sensing accuracy in compressed sensing, the other two being SNR and the choice of decoding method. We find that sacrificing diversity for SNR may be worthwhile and observe that artificial neural networks outperform traditional decoding methods in terms of the achieved sensing accuracy, especially at low SNR.