No. 4 (2022)
Published:
2022-12-30
ARTICLES FROM THIS ISSUE
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Modeling the Geometry of an Underwater Channel for Acoustic Communication
Abstract
The achievement of efficient data transmissions via underwater acoustic channels, while dealing with large data packets and real-time data fed by underwater sensors, requires a high data rate. However, diffraction, refraction, and reflection phenomena, as well as phase and amplitude variations, are common problems experienced in underwater acoustic (UWA) channels. These factors make it difficult to achieve high-speed and long-range underwater acoustic communications. Due to multipath interference caused by surface and ocean floor reflections, the process of modeling acoustic channels under the water’s surface is of key importance. This work proposes a simple geometry-based channel model for underwater communication. The impact that varying numbers of reflections, low water depth values, and distances between the transmitter and the receiver exert on channel impulse response and transmission loss is examined. The high degree of similarity between numerical simulations and actual results demonstrates that the proposed model is suitable for describing shallow underwater acoustic communication environments.
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Design Low Complexity SCMA Codebook Using Arnold’s Cat Map
Abstract
In 5G wireless communications, sparse code multiple access (SCMA) – a multi-dimensional codebook based on a specific category of the non-orthogonal multiple access (NOMA) technique - enables many users to share non-orthogonal resource components with a low level of detection complexity. The multi-dimensional SCMA (MD-SCMA) codebook design presented in this study is based on the constellation rotation and interleaving method. Initially, a subset of the lattice Z 2 is used to form the mother constellation’s initial dimension. The first dimension is then rotated to produce other dimensions. Additionally, interleaving is employed for even dimensions to enhance fading channel performance. Arnold’s chaotic cat map is proposed as the interleaving method to reduce computational complexity. Performance of the SCMA codebook based on interleaving is evaluated by comparing it with selected codebooks for SCMA multiplexing. The metrics used for performance evaluation purposes include bit error rate (BER), peak to average power ratio (PAPR), and minimum Euclidean distance (MED), as well as complexity. The results demonstrate that the suggested codebook with chaotic interleaving offers performance that is equivalent to that of the conventional codebook based on interleaving. It is characterized by lower MED and higher BER compared to computer-generated and 16-star QAM codebook design approaches, but its complexity is lower than that of the conventional codebook based on interleaving.
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Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks
Abstract
Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.
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Chaotic-based Orthogonal Frequency Division Multiplexing with Index Modulation
Abstract
Orthogonal frequency division multiplexing with index modulation (OFDM-IM), stands out among conventional communication technologies, as it uses the indices of the available transmit entities. Thanks to such an approach, it offers a novel method for the transmission of extra data bits. Recent years have seen a great interest in chaos-based communications. The spectrum-spreading signals used in chaotic signal modulation technologies are orthogonal to the existing mixed signals. This paper presents how well a non-coherent differential chaos shift keying communication system performs across an AWGN. Different types of detection methods are simulated, bit error rate and power spectral density are calculated and then compared with standard OFDM with index modulation. The results of simulations concerning the performance of a DCSK system, adding to the security of the proposed solution and offering a comparable bit error rate performance, are presented in the paper as well.
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Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs
Abstract
Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resourceoptimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.
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Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI
Abstract
In this paper, the performance of a deep learningbased multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Furthermore, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.
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Reliability of Communication Systems Used in Offshore Wind Farms
Abstract
In the era of renewable energy, offshore wind farms play a very important role. The number of such installations in Europe is increasing rapidly. With the growing capacity of wind turbines installed in these farms (3, 5, 10 MW), the profitability of this type of energy systems plays an increasing role. The number of wind energy turbines installed at offshore wind farms is growing constantly as well. Once installed, the power plants must be under constant technical supervision, with reliability of electronic communication systems being a particularly important aspect in the operation of offshore wind farms. Considerations focusing on this subject form the very core of this paper. After an introduction to offshore wind farms, the following aspects will be discussed: redundant topologies, e.g. multiple HiPERRings, redundant switches and routers within the backbone networks, redundancy of the transmission media used, alternative transmission technologies, e.g. WLANs (IEEE 802.11h, IEEE 802.11g). Finally, requirements applicable to reliable electronic communication systems used in offshore wind farms will be formulated.
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High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network
Abstract
Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics.
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An Efficient Hybrid Protocol Framework for DDoS Attack Detection and Mitigation Using Evolutionary Technique
Abstract
The ever-increasing use of the Internet has created massive amounts network traffic, causing problems related to its scalability, controllability, and manageability. Sophisticated network-based denial of service (DoS) and distributed denial of service (DDoS) attacks increasingly pose a future threat. The literature proposes various methods that may help stop all HTTP DoS/DDoS assaults, but no optimal solution has been identified so far. Therefore, this paper attempts to fill the gap by proposing an alternative solution known as an efficient hybrid protocol framework for distributed DoS attack detection and mitigation (E-HPFDDM). Such an architecture addresses all aspects of these assaults by relaying on a three-layer mechanism. Layer 1 uses the outer advanced blocking (OAB) scheme which blocks unauthorized IP sources using an advanced backlisted table. Layer 2 is a validation layer that relies on the inner service trackback (IST) scheme to help determine whether the inbound request has been initiated by a legitimate or an illegitimate user. Layer 3 (inner layer) uses the deep entropy based (DEB) scheme to identify, classify and mitigate high-rate DDoS (HR-DDoS) and flash crowd (FC) attacks. The research shows that in contrast to earlier studies, the structure of the proposed system offers effective defense against DoS/DDoS assaults for web applications.
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Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features
Abstract
Nematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed approach relies on deep learning techniques, specifically on convolutional neural networks (CNNs), to solve the problem and achieve high classification accuracy by focusing on non-handcrafted self-learned features. Various networks known from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have been investigated and adapted for the purposes of the C. elegans muscle aging dataset by applying transfer learning and data augmentation techniques. The proposed approach of unfreezing different numbers of convolutional layers at the feature extraction stage and introducing different structures of newly trained fully connected layers at the classification stage, enable to better fine-tune the selected networks. The adjusted CNNs, as featured in this paper, have been compared with other state-of-art methods. In anti-aging drug research, the proposed CNNs would serve as a very fast and effective age determination method, thus leading to reductions in time and costs of laboratory research.
