Scientific publications—2022

Year: 2022

Abstract: Non-uniform constellations (NUCs) successfully utilize geometrical shaping to approach the Shannon capacity, but increase demapping complexity. In this paper, enhanced non-uniform constellations for low-complexity and high-capacity communications are investigated and addressed in two steps: low-complexity demappers for fixed constellations and the constrained design of enhanced NUCs. First, simplified demappers for NUCs are studied, and modified virtual point searching (VPS)-based demappers are proposed. Theoretical analysis and numerical results show that the proposed modified VPS-based demappers reduce demapping complexity with negligible performance degradation. Secondly, two shaping constraint schemes are proposed to NUC design for the purpose of low complexity and high capacity. NUCs optimized under the proposed constraints outperform the uniform constellations, and the proposed demappers have good applicability to the optimized NUCs. The proposed NUC design constraint schemes and simplified demappers enable solutions to achieve low-complexity high-capacity communication. © 1963-12012 IEEE.

Source title: IEEE Transactions on Broadcasting

DOI: 10.1109/TBC.2022.3176194

Series Number: Volume 68, issue 3

Link: Enhanced Nonuniform Constellations for High-Capacity Communications With Low-Complexity Demappers

Year: 2022

Abstract: The media landscape is undergoing unprecedented transformations. Content creators and service providers had been for decades in control of how technology tailored to media applications was deployed and adopted in networks and devices to serve audiences. Disruption started when the Internet and global technologies and networks enabled content delivery and consumption at anytime, anywhere, and by means of any device. This new paradigm, where the Internet is fully accessible to the media industry, creates opportunities for innovation across the entire value chain, involving new services, innovative products, and a direct relationship with users

Source title: IEEE Transactions on Broadcasting

DOI: 10.1109/TBC.2022.3163818

Series Number: Volume 68, issue 2

Link: IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

Year: 2022

Abstract: Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). However, DSA requires an accurate prediction of spectrum usage in both time and spatial domains with minimal sensing cost. In this paper, we propose NN-ResNet prediction model to address this challenge in two steps. First, in order to make the best use of the sensors in the region, we deploy a deep learning prediction model based on convolutional neural networks (CNNs) and residual networks (ResNets), to predict spatio-temporal spectrum usage of the region. Second, to reduce sensing cost, the nearest neighbor (NN) interpolation is applied to recover spectrum usage data in the unsensed areas. In this case, fewer sensors are needed for prediction with the help of the reconstruction procedure. The model is verified through groups of comparison simulations in terms of the sensors' sparsity and the number of transmitters involved. In addition, the proposed model is compared with CNN and ConvLSTM prediction model. The results show that the proposed NN-ResNet model maintains a lower error rate under various sparse sensor circumstances. © 2015 IEEE.

Source title: IEEE Transactions on Cognitive Communications and Networking

DOI: 10.1109/TCCN.2021.3139030

Series Number: Volume 8, issue 2 

Link: Spatio-Temporal Spectrum Load Prediction Using Convolutional Neural Network and ResNet

Year: 2022

Abstract: This study provides the load–displacement behavior of stiffened plates with parallel ribs when modeled using shell elements with and without considering the large-displacement effects in comparison with the conventional equivalent beam analysis (EBA) in which such stiffened plates are simplified into an imaginary beam with the geometrical properties of an equivalent built-up cross-section. This study highlights the advantages of FE modeling with shell elements and large-displacement analysis (LDA) over the existing EBA method and then provides a path for using this method in the ultimate limit state (ULS) design of stiffened plates. The stress distributions in the panel plate part of the stiffened plates are presented to demonstrate the significance of considering the large-displacement effects in the analysis. The numerical investigation indicated that the linear beam theory does not correctly presume the true behavior of the stiffened plates and that the corresponding load–displacement estimation does not align with its behavior predicted by the LDA. The results also demonstrate that when shell element modeling is utilized, the internal forces and stresses in the panel plate are calculated correctly, only if the large-displacement effects are considered in the analysis. Finally, a method for estimating the ultimate load capacity of stiffened plates with parallel ribs is provided, based on the large-displacement FE analysis with shell elements, which can be used to establish empirical design equations. © 2022 Elsevier Ltd

Source title: Engineering Structures

DOI: 10.1016/j.engstruct.2022.114125

Series Number: Volume 259

Link: Large displacement analysis of stiffened plates with parallel ribs under lateral pressure using FE modeling with shell elements

Year: 2022

Abstract: Digital Terrestrial Television (DTV) has been widely deployed globally for more than a decade. The transmitter identification (Tx-ID) technique specified in modern DTV standards becomes important today as the number of DTV transmitters grows with the expanded coverage area. In the ATSC standards, Kasami sequences, a crucial class of pseudo random sequences, are considered as feasible Tx-ID sequences because they possess several favorable properties leading to nearly Dirac-delta autocorrelation/cross-correlation functions and large sequence capacities. It is well known that the interference-pluse-noise level (INL) or signal-to-interferenec-plus-noise ratio (SINR) plays a very important role in the Tx-ID system performance. Nontheless, such a crucial factor has been evaluated only in the statistical average due to the difficulty of characterizing the exact pertinent probabilitistic analysis. In this work, we combat the aforementioned difficulty by applying a probability-density approximation method to statistically characterize random variables like INL and SINR. With the exact probability density functions of INL and SINR, we can analyze the detailed statistical characteristics of numerous Tx-ID related parameters, including Tx-ID capacity and reception quality in terms of SINR. Extensive numerical experiments are also undertaken to justify the effectiveness of our proposed new analysis and explore detailed quantitative insight for critical Tx-ID system parameters and performance metrics. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Source title: Wireless Networks

DOI: 10.1007/s11276-021-02884-9

Series Number: Volume 28, issue 4

Link: New probabilistic SINR analysis for capacity and reception-quality studies of DTV transmitter identification systems

Year: 2022

Abstract: This article presents a uniform ray description of electromagnetic wave scattering by locally periodic metasurfaces of polygonal shape. The model is derived by asymptotically evaluating the critical-point contributions of a physical optics (PO) scattering integral. It is valid for metasurfaces whose bulk scattering coefficients are periodic functions of a phase parameter, which, in turn, is a continuous and smooth function of surface coordinates. The scattered field is expressed in terms of reflected, transmitted, and diffracted rays that do not generally obey conventional geometrical constraints (i.e., Snell's law and the Keller cone). An iterative technique is presented to determine the locations of critical points on one or multiple interacting metasurfaces. Numerical results demonstrating the utility and accuracy of the asymptotic PO model are also provided. © 1963-2012 IEEE.

Source title: IEEE Transactions on Antennas and Propagation

DOI: 10.1109/TAP.2021.3137191

Series Number: Volume 70, issue 4

Link: Uniform Ray Description of Physical Optics Scattering by Finite Locally Periodic Metasurfaces

Year: 2022

Abstract: Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node's Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by η ≥ 0 dB, our proposed new CAMC scheme's correct classification probability Pcc could reach up close to 100%. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Source title: Sensors

DOI: 10.3390/s22051797

Series Number: Volume 22, issue 5

Link: Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Year: 2022

Abstract: Low-density parity-check (LDPC) codes are widely used as error correction codes in new generation digital TV standards, such as the second generation of terrestrial digital video broadcasting standard (DVB-T2), Advanced Television Systems Committee (ATSC) 3.0, etc. The nonlinear belief propagation (BP) algorithm has excellent decoding performance for LDPC codes, but is often simplified in hardware implementations by linear min-sum (MS) algorithm due to its high complexity. This simplification also leads to over-estimation problems, which can be corrected by adding factors in conventional algorithms (e.g., normalized min-sum (NMS), offset min-sum (OMS), and variable scaling normalized min-sum (VMS) algorithms). However, the correction factors of these modified MS algorithms cannot adapt to different channels and modulations, and the performance needs further improvement. In this paper, the concepts of over-estimation value (OEV) and over-estimation rate (OER) are introduced to describe the over-estimation problem of the MS algorithm. Then, under the guidance of OEV and OER, a polygonal line min-sum (PMS) algorithm with correction factors adapted to different channels and modulations is proposed according to LLR distribution. Following the properties of OEV and OER, PMS algorithm is further simplified into Simplified PMS (SPMS) algorithm. LDPC codes from ATSC 3.0 are adopted in this paper to evaluate SPMS algorithm in comparison with the conventional algorithms. Extensive simulation results show that the SPMS algorithm for ATSC 3.0 LDPC decoder has at most 1.61dB, 0.24dB and 0.36dB gain over NMS, OMS and VMS algorithms respectively when frame error rate (FER) is at 10^-4 level over additive white Gaussian noise (AWGN) channel with QPSK modulation. More importantly, the simulation results show that the SPMS algorithm can achieve much better performance than these modified MS algorithms over AWGN and Rayleigh channel with higher-order modulations or under limited maximum iteration number. © 1963-12012 IEEE.

Source title: IEEE Transactions on Broadcasting

DOI: 10.1109/TBC.2021.3105025

Series Number: Volume 68, issue 1

Link: A Polygonal Line Min-Sum Decoding Scheme for Low Density Parity Check Codes

Year: 2022

Abstract: This work continues the development of the ray-tracing method of de Jong (2021) for computing the scattered fields from metasurfaces characterized by locally periodic reflection and transmission coefficients. In this work, instead of describing the metasurface in terms of scattering coefficients that depend on the incidence direction, its scattering behavior is characterized by the surface susceptibility tensors that appear in the generalized sheet transition conditions (GSTCs). As the latter quantities are constitutive parameters, they do not depend on the incident field and, thus, enable a more compact and physically motivated description of the surface. The locally periodic susceptibility profile is expanded into a Fourier series subject to a spatially varying phase parameter, and the GSTCs are rewritten in a form that enables them to be numerically solved for the reflected and transmitted surface fields. The phase parameter can either be determined from a prescribed surface transformation or extracted from known surface susceptibilities. A method for the extraction of this phase parameter from a susceptibility profile using a spatial variant of the short-time Fourier transform (STFT) is proposed. The scattered field at arbitrary detector locations is constructed by evaluating critical-point contributions of the first and second kinds using a forward ray-tracing (FRT) scheme. The accuracy of the resulting framework has been verified with an integral equation-based boundary element method (BEM)-GSTC full-wave solver for a variety of examples, such as a periodically modulated metasurface, a metasurface diffuser, and a beam collimator. © 1963-2012 IEEE.

Source title: IEEE Transactions on Antennas and Propagation

DOI: 10.1109/TAP.2021.3111665

Series Number: Volume 70, issue 2

Link: Ray-Optical Evaluation of Scattering from Electrically Large Metasurfaces Characterized by Locally Periodic Surface Susceptibilities

Year: 2022

Abstract: Spectrum monitoring to ensure compliance with regulatory requirements is one of the key spectrum management activities that contribute to preventing harmful interference and improving the overall quality of spectrum. It protects the integrity of spectrum and radio environments, which in turn enables orderly implementation of related management activities such as spectrum engineering, planning, and licensing activities. This chapter presents an automated data-driven system that leverages advanced technologies to facilitate spectrum monitoring for scalable and efficient compliance verification. The goal of the system is to reduce the manual workload from spectrum managers by automatically collecting spectrum data from different sources, identifying compliance issues, and performing analytics to provide actionable insights. The automated data-driven system presented has great potential to speed-up the resolution of compliance issues and address a wide range of compliance issues. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source title: Wireless Networks (United Kingdom)

DOI: 10.1007/978-3-030-98064-1_13

Link: Automated Data-Driven System for Compliance Monitoring

Year: 2022

Abstract: An innovative bluetooth radio-frequency (RF) fingerprint identification scheme using the random forest classifier involving multiwavelet-based linear-predictive-coding (LPC) features is introduced in this paper. In our proposed approach, finite-element multiwavelet with an arbitrary multiplicity (MWAM) is first constructed to decompose an RF signal emitted by an electronic equipment into multiple subbands. Next, LPC coefficients, which can be employed to mitigate the background noise, are further estimated from these subband signal sequences. Such multiwavelet-based LPC coefficients will be utilized as the features of the adopted random-forest classifier to recognize the bluetooth RF fingerprints emitted from different wireless transmitters. Monte Carlo simulation results demonstrate the effectiveness of our proposed new RF fingerprint identification technique. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828678

Series Number: 2022-June

Link: Novel Multiwavelet-based LPC Random Forest Classifier for Bluetooth RF-Fingerprint Identification

Year: 2022

Abstract: Future digital terrestrial television (DTT) systems, such as ATSC 3.0, are evolving to offer users new applications and services. These new applications (e.g., target advertisement, connected cars, 4k/8k video content) require a considerable increase in transmission capacity. In contrast, the current standards and technologies need to improve the offered spectral efficiency rate to adapt to the new use cases. A good alternative is to enable full-duplex communications among the ATSC transmission centers to build a mesh network of transmitters (i.e., Inter-Tower Communication Network or ITCN). The main drawback of this idea is the self-interference (SI) or loopback signal that is generated and added to the received signal. To reduce the impact of the SI, the transmission centers combine different signal isolation and cancellation techniques. This paper focuses on the characterization of the signal power isolation between the transmitted and the received signals under different conditions. In particular, this paper shows the results obtained during a measurement campaign in a real broadcast transmission center. The measurements were carried out following a novel signal isolation measurement methodology, and the results analyze the obtained signal isolation and the time variability of the received signal power. The results show that a minimum isolation value of 69.5 dB can be obtained for the worst evaluated case. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828757

Series Number: 2022-June

Link: Signal Isolation in Full-Duplex Inter-Tower Communication Networks: Field Trials

Year: 2022

Abstract: Wireless backhaul and inter-tower communications networks were previously proposed as key enabling technologies for the next generation digital broadcasting systems, e.g. the Advanced Television Systems Committee (ATSC) 3.0 system. Due to the backward compatibility constraint, conventional symmetrical multi-input multi-output (MIMO) techniques cannot be applied directly in the existing broadcast infrastructure. In this paper, we propose a non-symmetrical approach to integrate MIMO in wireless backhaul and inter-tower communications networks. The proposed schemes maintain the existing broadcast infrastructure, while only adding a low-power RF feeding cable and one or more highly directional antennas to achieve low-cost MIMO implementation for high throughput data distribution and inter-tower networking for the next generation broadcasting systems. The advantages of the proposed system are low deployment cost and full backward compatibility with the ATSC 3.0 broadcast services. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828705

Series Number: 2022-June

Link: MIMO Integration for Wireless Backhaul and Inter-Tower Communications in ATSC 3.0

Year: 2022

Abstract: This paper investigates that by incorporating Layered Division Multiplexing (LDM) in 5G NR, we can create a two-layer network that can simultaneously deliver single frequency network broadcast service as well as a near full capacity broadband service. It is beneficial to transmit both layers on the same antennas following the same specifications to reduce system complexity and implementation cost. However, the narrow beams used in 5G broadband communications are not suitable for broadcast services which require continuous seamless coverage in the entire service area. This paper shows it is viable to perform beamforming and broadcasting at the same time. In addition, simulation results prove that using a 5G antenna and following 5G broadband specifications, an embedded SFN broadcast network can offer sufficiently high SINR to support high-definition video services, while providing narrow beam point-to-point communication services. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828645

Series Number: 2022-June

Link: Combining LDM and MIMO for Mixed Broadcast-Broadband Service Delivery in 5G

Year: 2022

Abstract: Dynamic bandwidth allocation based on multimedia network-traffic prediction has been emerging as an important problem in multimedia networks. The well-known Kalman filter has been adopted for such network-traffic prediction but it is assumed that the state-transition model is linear and known a priori. Therefore, it is favorable to extend the conventional linear state-transition model to be nonlinear and dynamically estimate it. It is not trivial to estimate such a nonlinear model especially for a multimedia network supporting the 5G technology and operating in a highly mobile environment. In this work, we would like to address the aforementioned challenges by designing a new matrix-based Levenberg-Marquardt algorithm based extended Kalman filter (MLMA-EKF) to dynamically estimate the video frame-sizes in compiance with MPEG-4 specifications. Numerical results over MPEG-4 encoded movies demonstrate that our proposed novel MLMA-EKF frame-size predictor is effective for predicting the future bit rates, or video frame-sizes, in terms of normalized mean square error (NMSE). © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828691

Series Number: 2022-June

Link: Novel Extended Kalman Filter Using Matrix-Based Levenberg-Marquardt Algorithm and Its Application for Variable Bit-Rate Video Frame-Size Prediction

Year: 2022

Abstract: In this paper, an advanced machine learning technique is proposed to enable robust real-time metal-surface-detect detection and classification using video streams. The industrial informatics can be inferred from video data according to our proposed new approach. Different from the conventional schemes, our proposed machine-learning technique can detect and classify the metal-surface defects by selecting critical statistical and structural features using Renyi's entropy. To demonstrate the effectiveness of our proposed new detection and classification algorithm, simulation results and performances are compared with the prevalent conventional decision-tree classifier. Based on numerous experimental results, our proposed metal-surface defect detection and classification scheme greatly outperforms the conventional decision-tree classifier. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828748

Series Number: 2022-June

Link: Real-Time Metal-Surface-Defect Detection and Classification Using Advanced Machine Learning Technique

Year: 2022

Abstract: This paper proposes realistic channel models to describe the fading effects in high-power high-tower (HPHT) single-frequency network (SFN) environments. The proposed models empirically characterize the ultra-high frequency wireless channels based on field data obtained from an operational SFN in a metropolitan area. To this end, large-scale channel sounding experiments are conducted by leveraging on-air transmitter identification signals. The unique features of HPHT SFN transmission are identified and reflected in the tapped delay line parameter definitions. Dedicated models are built for stationary and mobile environments so that they could relevantly assist network planning, system implementation, and performance test in the industry. Free MATLAB source code of the fading simulator is available at https://github.com/ETRI-KMOU/FadingChannelSimulator. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828617

Series Number: 2022-June

Link: Empirical Modeling of UHF Wireless Channel in HPHT SFNs: Based on Seoul Metropolitan Case

Year: 2022

Abstract: Wireless in-band backhaul technology was recently proposed for the next-generation TV broadcasting system with Single Frequency Networks as a spectrum and cost-efficient alternative to conventional fibre-optic or dedicated microwave links. Self-interference cancellation (SIC) is the key technology enabling wireless in-band backhaul to operate in the more spectrum efficient full-duplex mode. For the broadcasting headend transceiver, although the separation between the co-located transmit and receive antennas and digital/baseband SIC can achieve significant self-interference reduction and cancellation, it is desirable to have an analog/Radio Frequency (RF) SIC before the digital/baseband SIC, to relax the hardware dynamic range requirement and reduce the effect of nonlinear distortions due to the hardware imperfection as well as adjacent channel interferences. In this paper, a frequency-domain blind RF SIC (RF-SIC) framework with a novel filter weight optimization algorithm is proposed, which does not require a training process and can achieve fast convergence with the capability of tracking the self-interference channel variation with affordable computation complexity. The proposed techniques can work with different broadcasting and communications systems such as ATSC 3.0, DVB-T/T2, WiFi and 4G/5G. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828730

Series Number: 2022-June

Link: Blind RF Self-Interference Cancellation for In-Band Distribution Link in ATSC 3.0

Year: 2022

Abstract: Motivated by the need to offer large amounts of data, user interactivity, and other requirements to enhance user experience, digital TV standards like ATSC 3.0 have evolved significantly. Particularly, in the case of ATSC 3.0, In-band Distribution Link (IDL) and Inter Tower Communication Networks (ITCN) have been proposed, among other novelties. These technologies imply the implementation of In-Band Full-Duplex (IBFD) communications, which increase the overall network capacity but have to manage strong self-interference signals. In this paper, an artificial intelligence technique based on Convolutional Neural Networks (CNN) is proposed to perform the cleaning of the loopback channel estimation. Moreover, computer-based simulations have been carried out, and methods proposed in previous papers are compared to Neural Networks (NN). Results indicate that NNs show a greater cleaning capacity than the previously mentioned techniques. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828767

Series Number: 2022-June

Link: AI-based Inter-Tower Communication Networks: First approach

Year: 2022

Abstract: MATLAB is one of the most widely used simulation platforms for academia and research. It contains a powerful 5G toolbox for performing both link-level and system-level simulations. As far as we are aware, the 5G Toolbox implementation is incomplete for system-level simulations that would comply with 3GPP assumptions. We modify the toolbox to make it compatible with 3GPP calibration simulation scenarios. Simulation results of the Rural-eMBB and Urban Macro-mMTC scenarios show that the resulting SINR falls within 1 dB from the 3GPP calibration average, well within the tolerance margin of 12 dB, suggesting the 5G toolbox is a suitable platform for 5G system-level simulations. One downside to the toolbox is its long execution time, which makes testing and developing very time-consuming. Currently, we are working on abstracting some of the link-level features to reduce the complexity. We also plan to incorporate multicast and broadcast transmission as well as layered division multiplexing into the toolbox. Once completed, the new features will be packaged as plug-in functions to the 5G Toolbox, and will be open-source, available for interested research groups. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828588

Series Number: 2022-June

Link: 5G System Level Simulation Calibration Using MATLAB 5G Toolbox

Year: 2022

Abstract: This paper proposes a novel automatic posture recognition approach using the skeletal data of human subjects acquired from the Kinect sensors. The acquired skeletal data are used as the input features for training the artificial-intelligence driven recognizer. In this work, we formulate the underlying human-posture recognition problem as the classical multi-classification problem. The graph convolutional network (GCN) is trained to identify the human postures by successive frames through an activity using the Kinect skeletal data (three-dimensional skeletal coordinates). Experimental results using realworld data demonstrate that our proposed GCN leads to a promising classification-accuracy of 92.2% for automatic human-posture recognition. As a result, our proposed novel GCN-based human-posture recognizer greatly outperforms other existing schemes. © 2022 IEEE.

Source title: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

DOI: 10.1109/BMSB55706.2022.9828603

Series Number: 2022-June

Link: Automatic Human Posture Recognition Using Kinect Sensors by Advanced Graph Convolutional Network

Year: 2022

Abstract: [No abstract available]

Source title: Frontiers of Information Technology and Electronic Engineering

DOI: 10.1631/FITEE.2210000

Link: Theory and techniques for "intellicise" wireless networks

Year: 2022

Abstract: Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix).

• Our approach includes a distribution of contacts by age that is responsive to the underlying age distributions of the mixing populations.
• Our approach maintains different age mixing patterns by contact type, such that changes to the numbers of different types of contacts are appropriately reflected in changes to overall age mixing patterns.
• Our approach distinguishes between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch, and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch. © 2021 The Author(s)

Source title: MethodsX

DOI: 10.1016/j.mex.2021.101614

Link: Adaptive data-driven age and patch mixing in contact networks with recurrent mobility

Year: 2022

Abstract: This paper proposes several realistic fading channel models to describe the signal distortions that arise in single frequency network (SFN) environments. The proposed models characterize ultra-high frequency (UHF) wireless channels based on measurements from operating networks in metropolitan areas. In this paper, the unique features of SFN transmission are identified compared to the conventional, single antenna transmission channels. The impacts of urban propagation, tower configuration, and mobile and terrain effects are considered based on the tapped delay line structure. The proposed models can provide a useful reference to facilitate the SFN system design, network planning, verification, and receiver performance test. © 2022 IEEE.

Source title: IEEE Transactions on Broadcasting

DOI: 10.1109/TBC.2022.3210382

Series Number: Volume 68, Issue 4

Link: Characterization and Modeling of UHF Wireless Channel in Terrestrial SFN Environments: Urban Fading Profiles

Year: 2022

Abstract: A ray optical methodology based on the uniform theory of diffraction (UTD) is proposed to model electromagnetic (EM) field scattering from curved metasurfaces (MSs). The problem addressed is the illumination of a purely reflective uniform cylindrical MS by a line source, models the surface with susceptibilities and employs a methodology previously used for cylinders coated in thin dielectric layers [Kim and Wang (1989)]. The approach is fundamentally based on a representation of the MS using the generalized sheet transition conditions (GSTCs) which characterizes the surface in terms of susceptibility dyadics. An eigenfunction (EF) description of the MS problem is derived considering both tangential and normal surface susceptibilities, and used to develop a ray optics (RO) description of the scattered fields including the specular geometrical optical field, surface diffraction described by creeping waves and a transition region over the shadow boundary. The specification of the fields in the transition region is dependent on the evaluation of the Pekeris caret function integral and the method follows [Kim and Wang (1989)]. The proposed RO-GSTC model is then successfully demonstrated for a variety of cases and is independently verified using a rigorous EF solution (EF-GSTC) and full-wave Integral Equation method (IE-GSTC), over the entire domain from the deep lit to deep shadow. © 1963-2012 IEEE.

Source title: IEEE Transactions on Antennas and Propagation

DOI: 10.1109/TAP.2022.3184540

Series Number: Volume 70, Issue 10

Link: Ray Optical Scattering from Uniform Reflective Cylindrical Metasurfaces Using Surface Susceptibility Tensors

Year: 2022

Abstract: A coupled complementary split ring resonator (CSRR) for mutual coupling suppression in dual differentially fed microstrip patch antenna is presented in this paper. The antenna is a 1 ×2 array of microstrip patches with a pair of CSRR placed on the ground plane to reduce the coupling between the two patches. By properly designing and adjusting coupling coefficient between the pair of CSRR, the coupled resonator can effectively suppress mutual coupling. The isolation is improved by employing a grounded single split ring resonator (SRR) that provides an alternative coupling path for space radiations. The approach significantly improves the isolation between the patches by over 10 dB and 5 dB in the E and H-planes respectively. An impedance bandwidth of 9.34-9.45 GHz with mutual coupling below -30 dB is also attained. © 2022 IEEE.

Source title: IEEE International Symposium on Phased Array Systems and Technology

DOI: 10.1109/PAST49659.2022.9975013

Series Number: Oct-22

Link: Implementing Complementary Split Ring Resonators for Mutual Coupling Suppression in Dual Differentially-Fed Microstrip Patch Array Antenna

Year: 2022

Abstract: The rapid uptake of wireless technologies over the past decade has resulted in an increasing pressure on the limited radio spectrum resources. To improve the efficiency of current allocation policies, regulators in many jurisdictions are considering dynamic spectrum sharing. The success, however, of an optimized system hinges on the ability to sense, characterize, and forecast spectrum usage behaviour. Since traditional methods prove unable to scale to a wide range of channels, we propose DeepAir, a robust and scalable model that is capable of learning and predicting complex temporal and spectral dependencies in multivariate spectrum data. Specifically, we design a Sequence-to-Sequence model that employs an encoder-decoder architecture with two Deep Temporal Convolutional Networks. Using a test set consisting of approximately 900 channels in the Land Mobile Radio bands, we obtain a median RMSE and median MAE of 6.51 and 5.15, respectively. We then apply transfer learning to demonstrate the effectiveness of this model in forecasting patterns from any sensor, regardless of the band, sensitivity, and geographical location. Furthermore, the model exhibits no performance degradation up to three years after training for both short and long forecast horizons. Finally, we use DeepAir to quantify spectrum availability to enhance existing spectrum sharing capabilities. Crown

Source title: IEEE Transactions on Cognitive Communications and Networking

DOI: 10.1109/TCCN.2022.3222792

Link: DeepAir: Enabling Data-Driven Dynamic Spectrum Sharing via Scalable Forecasting

Year: 2022

Abstract: Big data analysis plays a very important role in today's information age. Based on big data analysis, this paper evaluates and analyzes students' subject examination results. First, check whether the university subject examination is a regular target reference examination, and check its descriptive statistical indicators, The derived scores were obtained from the original scores using descriptive statistical indicators; Secondly, test whether the students' test scores obey the normal distribution, draw the histogram of students' scores with Excel software and SPSS software, fit the curve to get that the students' scores obey the normal distribution, test whether the test scores obey the normal distribution with chi square goodness of fit, and test and analyze the real test scores; Then, it will test whether the test questions are written according to the teaching objectives and whether they are effective. It will be tested from two indicators: difficulty and distinction; Finally, whether the test is reliable will be tested from the reliability index. Applying the analysis results to the actual teaching management can provide more reasonable reference suggestions for future teaching, which has a certain practical guiding significance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source title: Lecture Notes in Electrical Engineering

DOI: 10.1007/978-981-19-4775-9_57

Series Number: 895 LNEE

Link: Evaluation Index System of Student Achievement Based on Big Data Analysis | SpringerLink

Year: 2022

Abstract: In this paper, mutual coupling suppression in a dual differentially fed microstrip patch antenna array with polarization diversity is studied. The investigated antenna is an array of two square patches and each patch is fed with two differential pairs to generate multiple polarization. A metamaterial absorbing wall is placed between the patches to isolate the patches in both E and H planes. The absorber wall comprises of an array of split ring resonators (SRR) printed within a dielectric substrate to decouple the patches. The introduction of the absorber wall results in an isolation enhancement of 15 dB and 5 dB in the E and H-planes respectively over a frequency bandwidth of 9.33-9.45 GHz. © 2022 IEEE.

Source title: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 – Proceedings

DOI: 10.1109/AP-S/USNC-URSI47032.2022.9886055

Link: Suppression of Mutual Coupling in Dual Differentially Fed Microstrip Patch Array Antenna

Year: 2022

Abstract: This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed. © 2022 IEEE.

Source title: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 – Proceedings

DOI: 10.1109/AP-S/USNC-URSI47032.2022.9887000

Link: Extending Machine Learning Based RF Coverage Predictions to 3D

Year: 2022

Abstract: Wireless backhaul has recently gained a significant amount of interest as a cost-effective solution in comparison with conventional backhaul technologies with dedicated microwave links or fiber optics. Self-interference cancellation (SIC) is an enabling technology that allows wireless backhaul to operate in the more spectrum-efficient in-band full-duplex (IBFD) operation mode instead of the out-of-band mode. Compared to Wi-Fi IBFD transceivers, wireless in-band backhaul systems face some unique challenges, such as significantly higher transmission power and much larger propagation delay spread for the self-interference signal, especially in the low-frequency bands under 1 GHz, which often prevent accurate SIC performance. The SIC is often implemented with an interference-cancelling filter, where the filter weights are essentially the channel estimates of the self-interference signals. In this paper, a frequency-domain Radio Frequency (RF) SIC (RF-SIC) framework with a novel filter weight optimization algorithm is proposed to tackle the challenges of wireless in-band backhaul. The proposed RF-SIC does not require a dedicated training phase which needs to stop the transmission of the backhaul signal. Moreover, it has the capability of tracking the self-interference channel variation since the filter weights are updated in a block-by-block fashion. IEEE

Source title: IEEE Transactions on Wireless Communications

DOI: 10.1109/TWC.2022.3211196

Link: Frequency-Domain RF Self-Interference Cancellation for In-Band Full-Duplex Communications