Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.165722
Ravi Shankar, Bhanu Pratap Chaudhar, R. Mishra
In this paper, the performance of a deep learning-based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identi-cally 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 termi-nal’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.
{"title":"Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI","authors":"Ravi Shankar, Bhanu Pratap Chaudhar, R. Mishra","doi":"10.26636/jtit.2022.165722","DOIUrl":"https://doi.org/10.26636/jtit.2022.165722","url":null,"abstract":" In this paper, the performance of a deep learning-based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identi-cally 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 termi-nal’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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42548218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.164722
Ch.S.N. Sirisha Devi, Suman Maloj
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 resource-optimized 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.
{"title":"Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs","authors":"Ch.S.N. Sirisha Devi, Suman Maloj","doi":"10.26636/jtit.2022.164722","DOIUrl":"https://doi.org/10.26636/jtit.2022.164722","url":null,"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 resource-optimized 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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41952413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.164822
Hala A. Naman, A. E. Abdelkareem
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.
{"title":"Modeling the Geometry of an Underwater Channel for Acoustic Communication","authors":"Hala A. Naman, A. E. Abdelkareem","doi":"10.26636/jtit.2022.164822","DOIUrl":"https://doi.org/10.26636/jtit.2022.164822","url":null,"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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":"259 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41290425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.164222
Roshni Padate, Amit Jain, M. Kalla, Arvind Sharma
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.
{"title":"High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network","authors":"Roshni Padate, Amit Jain, M. Kalla, Arvind Sharma","doi":"10.26636/jtit.2022.164222","DOIUrl":"https://doi.org/10.26636/jtit.2022.164222","url":null,"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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41381274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.165322
B. Czaplewski, M. Dzwonkowski, Damian Panas
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 understand-ing 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 ad-justed 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.
{"title":"Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features","authors":"B. Czaplewski, M. Dzwonkowski, Damian Panas","doi":"10.26636/jtit.2022.165322","DOIUrl":"https://doi.org/10.26636/jtit.2022.165322","url":null,"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 understand-ing 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 ad-justed 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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43480795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.26636/jtit.2022.164922
Sanjeevkumar Jeevangi, Shivkumar S. Jawaligi, Vilaskumar M. Patil
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.
{"title":"Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks","authors":"Sanjeevkumar Jeevangi, Shivkumar S. Jawaligi, Vilaskumar M. Patil","doi":"10.26636/jtit.2022.164922","DOIUrl":"https://doi.org/10.26636/jtit.2022.164922","url":null,"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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43343108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-29DOI: 10.26636/jtit.2022.161122
Rachid Fateh, A. Darif, S. Safi
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.
{"title":"An Extended Version of the Proportional Adaptive Algorithm Based on Kernel Methods for Channel Identification with Binary Measurements","authors":"Rachid Fateh, A. Darif, S. Safi","doi":"10.26636/jtit.2022.161122","DOIUrl":"https://doi.org/10.26636/jtit.2022.161122","url":null,"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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43519099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-29DOI: 10.26636/jtit.2022.153021
Zhigang Li, Y. Aimer, T. H. C. Bouazza
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.
{"title":"Modeling of Microwave Cavities Based on SIBC-FDTD Method for EM Wave Focalization by TR Technique","authors":"Zhigang Li, Y. Aimer, T. H. C. Bouazza","doi":"10.26636/jtit.2022.153021","DOIUrl":"https://doi.org/10.26636/jtit.2022.153021","url":null,"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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45084614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-29DOI: 10.26636/jtit.2022.162122
P. Nandi, J. S. Roy
The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.
{"title":"Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network","authors":"P. Nandi, J. S. Roy","doi":"10.26636/jtit.2022.162122","DOIUrl":"https://doi.org/10.26636/jtit.2022.162122","url":null,"abstract":"The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46224798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-29DOI: 10.26636/jtit.2022.160922
Ngo Thanh Hai, D. L. Khoa
Generalized Singular Value Decomposition (GSVD) is the enabling linear precoding scheme for multiple-input multiple-output (MIMO) non-orthogonal multiple access (NO-MA) 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. Consequent-ly, 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.
{"title":"An Approximate Evaluation of BER Performance for Downlink GSVD-NOMA with Joint Maximum-likelihood Detector","authors":"Ngo Thanh Hai, D. L. Khoa","doi":"10.26636/jtit.2022.160922","DOIUrl":"https://doi.org/10.26636/jtit.2022.160922","url":null,"abstract":" Generalized Singular Value Decomposition (GSVD) is the enabling linear precoding scheme for multiple-input multiple-output (MIMO) non-orthogonal multiple access (NO-MA) 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. Consequent-ly, 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.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47943058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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