No. 3 (2022)
Published:
2022-09-30
ARTICLES FROM THIS ISSUE
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Ranging and Positioning Accuracy for Selected Correlators under VHF Maritime Propagation Conditions
Abstract
The article presents an analysis of the features of selected correlators impacting the accuracy of determining the receiver’s range and position in VHF marine environment. The paper introduces the concept of various correlators – including the double delta correlator – and describes the proposed measurement scenarios that have been designed to demonstrate the effectiveness of those components. The entire work was performed as part of the R-Mode Baltic and R-Mode Baltic 2 projects, with our goals including analyzing the impact of multipath phenomena, changes in the sampling frequency or signal type on the determination of the received signal delay at the receiver. The measured data were processed in a signal correlation application and in a TOA-based tool in order to determine the receiver’s position. This process made it possible to compare the selected correlating devices. The results presented in this article are to be used by IALA in developing a current version of the VHF data exchange system’s (VDES) specification.
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Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions
Abstract
This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established using i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links
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An Approximate Evaluation of BER Performance for Downlink GSVD-NOMA with Joint Maximum-likelihood Detector
Abstract
Generalized Singular Value Decomposition (GSVD) is the enabling linear precoding scheme for multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. In this paper, we extend research concerning downlink MIMO-NOMA systems with GSVD to cover bit error rate (BER) performance and to derive an approximate evaluation of the average BER performance. Specifically, we deploy, at the base station, the well-known technique of joint-modulation to generate NOMA symbols and joint maximum-likelihood (ML) to recover the transmitted data at end user locations. Consequently, the joint ML detector offers almost the same performance, in terms of average BER as ideal successive interference cancellation. Next, we also investigate BER performance of other precoding schemes, such as zero-forcing, block diagonalization, and simultaneous triangularization, comparing them with GSVD. Furthermore, BER performance is verified in different configurations in relation to the number of antennas. In cases where the number of transmit antennas is greater than twice the number of receive antennas, average BER performance is superior.
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An Extended Version of the Proportional Adaptive Algorithm Based on Kernel Methods for Channel Identification with Binary Measurements
Abstract
In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well. In this paper, we propose a novel recursive kernel approach allowing to identify the finite impulse response (FIR) in non-linear systems, with binary value output observations. This approach employs a kernel function to perform implicit data mapping. The transformation is performed by changing the basis of the data in a high-dimensional feature space in which the relations between the different variables become linearized. To assess the performance of the proposed approach, we have compared it with two other algorithms, such as proportionate normalized least-meansquare (PNLMS) and improved PNLMS (IPNLMS). For this purpose, we used three measurable frequency-selective fading radio channels, known as the broadband radio access network (BRAN C, BRAN D, and BRAN E), which are standardized by the European Telecommunications Standards Institute (ETSI), and one theoretical frequency selective channel, known as the Macchi’s channel. Simulation results show that the proposed algorithm offers better results, even in high noise environments, and generates a lower mean square error (MSE) compared with PNLMS and IPNLMS.
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Modeling of Microwave Cavities Based on SIBC-FDTD Method for EM Wave Focalization by TR Technique
Abstract
The time reversal (TR) techniques used in electromagnetics have been limited for a variety of reasons, including extensive computations, complex modeling and simulation, processes as well as, large-scale numerical analysis. In this paper, the SIBC-FDTD method is applied to address these issues and to efficiently model TR systems. An original curvilinear modeling method is also proposed for constructing various obstacles in a 2D microwave cavity and for processing the corners of the cavity. The EM waves’ spatio-temporal focalization has been realized, and results of the simulations further prove the accuracy and effectiveness of this modeling method. Furthermore, they demonstrate that the microwave cavity processes may significantly improve the focalization quality in terms of SSLL enhancement.
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Joint Optimization of Sum and Difference Patterns with a Common Weight Vector Using the Genetic Algorithm
Abstract
A monopulse searching and tracking radar antenna array with a large number of radiating elements requires a simple and efficient design of the feeding network. In this paper, an effective and versatile method for jointly optimizing the sum and difference patterns using the genetic algorithm is proposed. Moreover, the array feeding network is simplified by attaching a single common weight to each of its elements. The optimal sum pattern with the desired constraints is first generated by independently optimizing amplitude weights of the array elements. The suboptimal difference pattern is then obtained by introducing a phase displacement π to half of the array elements under the condition of sharing some sided elements weights of the sum mode. The sharing percentage is controlled by the designer, such that the best performance can be met. The remaining uncommon weights of the difference mode represent the number of degrees of freedom which create a compromise difference pattern. Simulation results demonstrate the effectiveness of the proposed method in generating the optimal sum and suboptimal difference patterns characterized by independently, partially, and even fully common weight vectors.
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Semantic Knowledge Management and Blockchain-based Privacy for Internet of Things Applications
Abstract
Design of distributed complex systems raises several important challenges, such as: confidentiality, data authentication and integrity, semantic contextual knowledge sharing, as well as common and intelligible understanding of the environment. Among the many challenges are semantic heterogeneity that occurs during dynamic knowledge extraction and authorization decisions which need to be taken when a resource is accessed in an open, dynamic environment. Blockchain offers the tools to protect sensitive personal data and solve reliability issues by providing a secure communication architecture. However, setting-up blockchain-based applications comes with many challenges, including processing and fusing heterogeneous information from various sources. The ontology model explored in this paper relies on a unified knowledge representation method and thus is the backbone of a distributed system aiming to tackle semantic heterogeneity and to model decentralized management of access control authorizations. We intertwine the blockchain technology with an ontological model to enhance knowledge management processes for distributed systems. Therefore, rather than relying on the mediation of a third party, the approach enhances autonomous decision-making. The proposed approach collects data generated by sensors into higher-level abstraction using n-ary hierarchical structures to describe entities and actions. Moreover, the proposed semantic architecture relies on hyperledger fabric to ensure the checking and authentication of knowledge integrity while preserving privacy.
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Multi-operator Differential Evolution with MOEA/D for Solving Multi-objective Optimization Problems
Abstract
In this paper, we propose a multi-operator differential evolution variant that incorporates three diverse mutation strategies in MOEA/D. Instead of exploiting the local region, the proposed approach continues to search for optimal solutions in the entire objective space. It explicitly maintains diversity of the population by relying on the benefit of clustering. To promote convergence, the solutions close to the ideal position, in the objective space are given preference in the evolutionary process. The core idea is to ensure diversity of the population by applying multiple mutation schemes and a faster convergence rate, giving preference to solutions based on their proximity to the ideal position in the MOEA/D paradigm. The performance of the proposed algorithm is evaluated by two popular test suites. The experimental results demonstrate that the proposed approach outperforms other MOEA/D algorithms.
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Multimodal Sarcasm Detection via Hybrid Classifier with Optimistic Logic
Abstract
This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performed using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.
