Pub Date : 2023-08-24DOI: 10.2174/2210327913666230824145823
Zeina Ali, Qutaiba Ibrahim Ali
Exteroceptive sensors on an autonomous vehicle require a high-performance communication bus. The number of exteroceptive sensors keeps rising, and the CAN bus, the most common intra-network bus in vehicles, cannot keep up. This paper investigates the effect of Exteroceptive Sensors of Autonomous Vehicles on the CAN and CAN FD buses. Four types of sensors (4 cameras, 6 radars, 1 LiDAR, and 1 INS) have been introduced to create five different scenarios in two different test environments. The simulation used a highly effective environment to obtain accurate measurements and results. The results showed that the LiDAR sensor has huge data and requires a high-efficiency bus; the CAN bus could not handle it, and the rest of the sensors can transfer their data through the traditional CAN bus.
{"title":"Examining the Effects of Exteroceptive Sensors of Autonomous Vehicles (AV) on CAN Bus","authors":"Zeina Ali, Qutaiba Ibrahim Ali","doi":"10.2174/2210327913666230824145823","DOIUrl":"https://doi.org/10.2174/2210327913666230824145823","url":null,"abstract":"\u0000\u0000Exteroceptive sensors on an autonomous vehicle require a high-performance communication bus. The number of exteroceptive sensors keeps rising, and the CAN bus, the most common intra-network bus in vehicles, cannot keep up.\u0000\u0000\u0000\u0000This paper investigates the effect of Exteroceptive Sensors of Autonomous Vehicles on the CAN and CAN FD buses. Four types of sensors (4 cameras, 6 radars, 1 LiDAR, and 1 INS) have been introduced to create five different scenarios in two different test environments.\u0000\u0000\u0000\u0000The simulation used a highly effective environment to obtain accurate measurements and results.\u0000\u0000\u0000\u0000The results showed that the LiDAR sensor has huge data and requires a high-efficiency bus; the CAN bus could not handle it, and the rest of the sensors can transfer their data through the traditional CAN bus.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75007794","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 : 2023-08-23DOI: 10.2174/2210327913666230823094503
S. Lam, Duc-Tan Tran
In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks. In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper. The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions. In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.
{"title":"Optimizing Performance of Worst Case User in Ultra-Dense Networks utilizing Deep Q-learning","authors":"S. Lam, Duc-Tan Tran","doi":"10.2174/2210327913666230823094503","DOIUrl":"https://doi.org/10.2174/2210327913666230823094503","url":null,"abstract":"\u0000\u0000In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance\u0000\u0000\u0000\u0000Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks.\u0000\u0000\u0000\u0000In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper.\u0000\u0000\u0000\u0000The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions.\u0000\u0000\u0000\u0000In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74036207","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 : 2023-08-16DOI: 10.2174/2210327913666230816091012
Indu Malik, Anurag Singh Baghel
Herbicides are chemicals that are used to destroy weeds. It is commonly used in agriculture to kill undesired plants and increase crop yield, even though it has negative effects on humans and the environment. Pesticides sprayed on crops must be decreased in the real world to protect humans, animals, and birds from dangerous diseases such as cancer, eyes, and skin infection. Pesticides are classified as herbicides. Deep learning is being used in this research to minimize chemical compounds. Scientists seek to limit the amount of pesticide sprayed on crops to protect humans and the environment from toxic exposure. In this research, A neural network classifier is built using Convolutional Neural Network (CNN), dropout, rectified linear activation unit (ReLU), the Root Mean Squared Propagation (RMSprop) optimization technique, and stochastic gradient descent (SGD). The algorithms based on CNN outperformed the others. This study uses generated dataset (unique dataset and processes it row-wise through the Neural network) to train a categorized neural network, and the dataset was created with the assistance of the agriculture professor. This study offers a method for classifying weed images and spraying herbicides solely on weeds/unwanted plants rather than crops. The model should first be trained using the training dataset before being tested using the testing datasets. This model's training accuracy is 96%, while testing accuracy is 89%. This model reduced herbicide (it is a type of pesticide/chemical) spray over the crop (foods, vegetables, sugarcane) to protect humans, animals, birds, and the environment from harmful chemicals.
{"title":"Elimination of herbicides after the classification of weeds using Deep Learning","authors":"Indu Malik, Anurag Singh Baghel","doi":"10.2174/2210327913666230816091012","DOIUrl":"https://doi.org/10.2174/2210327913666230816091012","url":null,"abstract":"\u0000\u0000Herbicides are chemicals that are used to destroy weeds. It is commonly used in agriculture to kill undesired plants and increase crop yield, even though it has negative effects on humans and the environment. Pesticides sprayed on crops must be decreased in the real world to protect humans, animals, and birds from dangerous diseases such as cancer, eyes, and skin infection. Pesticides are classified as herbicides. Deep learning is being used in this research to minimize chemical compounds. Scientists seek to limit the amount of pesticide sprayed on crops to protect humans and the environment from toxic exposure.\u0000\u0000\u0000\u0000In this research, A neural network classifier is built using Convolutional Neural Network (CNN), dropout, rectified linear activation unit (ReLU), the Root Mean Squared Propagation (RMSprop) optimization technique, and stochastic gradient descent (SGD). The algorithms based on CNN outperformed the others. This study uses generated dataset (unique dataset and processes it row-wise through the Neural network) to train a categorized neural network, and the dataset was created with the assistance of the agriculture professor.\u0000\u0000\u0000\u0000This study offers a method for classifying weed images and spraying herbicides solely on weeds/unwanted plants rather than crops. The model should first be trained using the training dataset before being tested using the testing datasets.\u0000\u0000\u0000\u0000This model's training accuracy is 96%, while testing accuracy is 89%.\u0000\u0000\u0000\u0000This model reduced herbicide (it is a type of pesticide/chemical) spray over the crop (foods, vegetables, sugarcane) to protect humans, animals, birds, and the environment from harmful chemicals.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84797611","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 : 2023-08-16DOI: 10.2174/2210327913666230816090948
Mohammad Zand, Morteza Azimi Nasab, S. Padmanaban, Bassam Khan
Nowadays, due to the increasing development of distribution networks, their safety and high-reliability performance are of particular importance. One of the most important problems that endanger the security and reliability of these networks is the creation of some faults in them. In case of a fault in the network, identifying its location and type can be of great help in repairing faulty equipment. Also, by detecting the disconnection of one of the equipment or lines, it is possible to prevent accidents in the network. Phasor Measurement Unit (PMU) has been widely and successfully used as Transmission-Phasor Measurement Unit (T-PMU). The reporting time of PMUs is much shorter than the old Supervisory Control and Data Acquisition (SCADA) systems. They can provide synchronized phasor measurements that can generate voltage phasors of different network nodes. This study aimed to investigate the various applications of phasor measurement units in distribution networks and present a new method for detecting and analyzing the location and type of fault and topology fault of the distribution network using the Internet of Things (IOT) analysis method. To implement this method, it is necessary to measure different parameters of the distribution network before and after the occurrence of a fault, which is used by the DPMU for measurement. The simulation results indicate that for both single-topology and multi-topology faults, the proposed method has higher accuracy and better detection than the remaining common methods and effectively detects single-topology and multi-topology faults in the distribution network. This method can provide a more accurate network topology to estimate the state of the distribution network, which improves the accuracy of the state estimation and is suitable for implementing various advanced functions of the distribution management system.
{"title":"Introducing a New Method for DPMU in Detecting the Type and Location of the Fault","authors":"Mohammad Zand, Morteza Azimi Nasab, S. Padmanaban, Bassam Khan","doi":"10.2174/2210327913666230816090948","DOIUrl":"https://doi.org/10.2174/2210327913666230816090948","url":null,"abstract":"\u0000\u0000Nowadays, due to the increasing development of distribution networks, their safety and high-reliability performance are of particular importance. One of the most important problems that endanger the security and reliability of these networks is the creation of some faults in them. In case of a fault in the network, identifying its location and type can be of great help in repairing faulty equipment. Also, by detecting the disconnection of one of the equipment or lines, it is possible to prevent accidents in the network.\u0000\u0000\u0000\u0000Phasor Measurement Unit (PMU) has been widely and successfully used as Transmission-Phasor Measurement Unit (T-PMU). The reporting time of PMUs is much shorter than the old Supervisory Control and Data Acquisition (SCADA) systems. They can provide synchronized phasor measurements that can generate voltage phasors of different network nodes. This study aimed to investigate the various applications of phasor measurement units in distribution networks and present a new method for detecting and analyzing the location and type of fault and topology fault of the distribution network using the Internet of Things (IOT) analysis method.\u0000\u0000\u0000\u0000To implement this method, it is necessary to measure different parameters of the distribution network before and after the occurrence of a fault, which is used by the DPMU for measurement. The simulation results indicate that for both single-topology and multi-topology faults, the proposed method has higher accuracy and better detection than the remaining common methods and effectively detects single-topology and multi-topology faults in the distribution network.\u0000\u0000\u0000\u0000This method can provide a more accurate network topology to estimate the state of the distribution network, which improves the accuracy of the state estimation and is suitable for implementing various advanced functions of the distribution management system.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79615727","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 : 2023-08-15DOI: 10.2174/2210327913666230815121221
Jayabala Pradeep, P. Arunagiri, M. Harikrishnan, L. Martin
Wind energy, being a non-conventional and sustainable renewable resource, provides electrical energy through the rotation of the blades of a wind turbine caused by wind impact. To ensure the sustainability of this resource, maintenance of the wind turbines is essential. The incorporation of emerging technologies into the tedious processes has enabled quality improvement in the performance of systems. Augmented reality, which enhances the 3D digital content over the real world, may be used to leverage the tedious process of wind turbine maintenance by providing a user-friendly environment. AR utilization provides great insights into the problems occurring in specific parts of a wind turbine, thereby easing out the complexity of field workers. The objective is to create an augmented reality environment to monitor the proper functioning and detect the faultiness in a wind turbine with accuracy. AR utilization can help facilitate better maintenance service, thereby increasing the life of a wind turbine.
{"title":"Fault Detection in Windmills Using Augmented Reality","authors":"Jayabala Pradeep, P. Arunagiri, M. Harikrishnan, L. Martin","doi":"10.2174/2210327913666230815121221","DOIUrl":"https://doi.org/10.2174/2210327913666230815121221","url":null,"abstract":"\u0000\u0000Wind energy, being a non-conventional and sustainable renewable resource, provides electrical energy through the rotation of the blades of a wind turbine caused by wind impact. To ensure the sustainability of this resource, maintenance of the wind turbines is essential.\u0000\u0000\u0000\u0000The incorporation of emerging technologies into the tedious processes has enabled quality improvement in the performance of systems. Augmented reality, which enhances the 3D digital content over the real world, may be used to leverage the tedious process of wind turbine maintenance by providing a user-friendly environment.\u0000\u0000\u0000\u0000AR utilization provides great insights into the problems occurring in specific parts of a wind turbine, thereby easing out the complexity of field workers. The objective is to create an augmented reality environment to monitor the proper functioning and detect the faultiness in a wind turbine with accuracy.\u0000\u0000\u0000\u0000AR utilization can help facilitate better maintenance service, thereby increasing the life of a wind turbine.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"23 26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88667400","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 : 2023-07-27DOI: 10.2174/2210327913666230727095458
Shefin Shoukath, P. Haris
Large-scale MIMO OFDM technique satisfies the demands on performance and the service quality preferred in wireless communication systems. Since numerous antenna terminals have been incorporated in the base station, multiuser detection is crucial for retrieving the data appropriately. Thus, the complexities of the detectors increase rapidly in large-scale MIMO OFDM schemes. This work is a solution to achieve an extensively high rate of data transmission, which will help improve the capacity of the LS MIMO OFDM system. A unique detection approach of multiuser detection in LS MIMO OFDM model with channel coding, like low density parity check codes (LDPC), is suggested in this paper. The LDPC-coded large-scale MIMO OFDM system has also been analysed in the study with users of around ten at the transmitter and several antennas in the base station. BER of the LDPC-coded LS MIMO OFDM exhibited a waterfall region for SNR greater than 6dB as the study has been done with different decoding iterations. The BER performance worsened with the increase in modulation symbols. The study has shown how the BER performance has improved with respect to the increasing fading channels and subcarriers. The proposed system exhibited performance closer to the MIMO capacity with low complexity MMSE detection. The multiuser detector of LDPC-coded LS MIMO OFDM has been analysed by error rate in received bits (BER) with respect to different parameters, such as modulation orders, iteration values, receiving antennas, and OFDM subcarriers.
大规模MIMO OFDM技术满足了无线通信系统对性能和服务质量的要求。由于许多天线终端已并入基站，多用户检测对于适当检索数据至关重要。因此，在大规模MIMO ofdm方案中，检测器的复杂性迅速增加。这项工作是实现广泛高速率数据传输的一种解决方案，有助于提高LS MIMO OFDM系统的容量。本文提出了一种具有低密度奇偶校验码(LDPC)等信道编码的LS MIMO OFDM多用户检测方法。ldpc编码的大规模MIMO OFDM系统也在研究中进行了分析，其中发射机用户约为10人，基站中有几个天线。在不同的译码迭代下，ldpc编码的LS MIMO OFDM的误码率在信噪比大于6dB时呈现瀑布区。随着调制符号的增加，误码率性能下降。研究表明，随着衰落信道和子载波的增加，误码率性能得到了提高。该系统具有更接近MIMO容量的性能和低复杂度的MMSE检测。分析了ldpc编码的LS MIMO OFDM多用户检测器在调制顺序、迭代值、接收天线和OFDM子载波等不同参数下的误码率。
{"title":"Analysis of MMSE Multiuser Detector in a Low-density Parity Check Coded Large Scale MIMO OFDM","authors":"Shefin Shoukath, P. Haris","doi":"10.2174/2210327913666230727095458","DOIUrl":"https://doi.org/10.2174/2210327913666230727095458","url":null,"abstract":"\u0000\u0000Large-scale MIMO OFDM technique satisfies the demands on performance\u0000and the service quality preferred in wireless communication systems. Since numerous antenna terminals have been incorporated in the base station, multiuser detection is crucial for retrieving the data\u0000appropriately. Thus, the complexities of the detectors increase rapidly in large-scale MIMO OFDM\u0000schemes.\u0000\u0000\u0000\u0000This work is a solution to achieve an extensively high rate of data transmission, which will\u0000help improve the capacity of the LS MIMO OFDM system.\u0000\u0000\u0000\u0000A unique detection approach of multiuser detection in LS MIMO OFDM model with channel coding, like low density parity check codes (LDPC), is suggested in this paper. The LDPC-coded\u0000large-scale MIMO OFDM system has also been analysed in the study with users of around ten at the\u0000transmitter and several antennas in the base station.\u0000\u0000\u0000\u0000BER of the LDPC-coded LS MIMO OFDM exhibited a waterfall region for SNR greater\u0000than 6dB as the study has been done with different decoding iterations. The BER performance\u0000worsened with the increase in modulation symbols. The study has shown how the BER performance\u0000has improved with respect to the increasing fading channels and subcarriers.\u0000\u0000\u0000\u0000The proposed system exhibited performance closer to the MIMO capacity with low\u0000complexity MMSE detection. The multiuser detector of LDPC-coded LS MIMO OFDM has been\u0000analysed by error rate in received bits (BER) with respect to different parameters, such as modulation\u0000orders, iteration values, receiving antennas, and OFDM subcarriers.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80891079","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}
Software Defined Radio (SDR) is a technology that offers a high level of reconfigurability to address the issue of spectrum sparsity in wireless communication systems. This technology is widely used in Cognitive radio (CR), and researchers aim to develop new spectrum sensing methods that ensure a high signal detection performance and a low signal-to-noise ratio (SNR). In this context, deep learning (DL) based models can be an appropriate solution for building spectrum detection methods. This paper proposes a spectrum sensing architecture combining a convolutional neural network and long short-term memory (CNN-LSTM). This architecture takes advantage of the spatial modelling of CNN and the temporal modelling of LSTM to produce more separable features for detection. The paper aims to propose an SDR implementation of the CNN-LSTM model for real-time detection by using the Universal Software Radio Peripheral (USRP) board and GNU radio platform. Results and Discussion: Numerical Simulation results reveal that the proposed CNN-LSTM outperforms the CNN, the LSTM, and the energy detector (ED) in terms of higher detection probability Pd and lower false alarm probability Pfa, even at low SNR. The SDR implementation results show the robustness of the CNN-LSTM method under several real-time detection scenarios: FM, GSM, and OFDM. The CNN-LSTM model used for spectrum sensing provides a high detection performance in a low SNR environment compared to LSTM, CNN, and the ED detector.
{"title":"SDR Implementation of Spectrum Sensing Using Deep Learning","authors":"Zeghdoud Sabrina, Teguig Djamal, Tanougast Camel, Mesloub Amar, Sadoudi Said, Nesraoui Okba","doi":"10.2174/2210327913666230719152400","DOIUrl":"https://doi.org/10.2174/2210327913666230719152400","url":null,"abstract":"\u0000\u0000Software Defined Radio (SDR) is a technology that offers a high level of reconfigurability to address the issue of spectrum sparsity in wireless communication systems. This technology is widely used in Cognitive radio (CR), and researchers aim to develop new spectrum sensing methods that ensure a high signal detection performance and a low signal-to-noise ratio (SNR). In this context, deep learning (DL) based models can be an appropriate solution for building spectrum detection methods.\u0000\u0000\u0000\u0000This paper proposes a spectrum sensing architecture combining a convolutional neural network and long short-term memory (CNN-LSTM). This architecture takes advantage of the spatial modelling of CNN and the temporal modelling of LSTM to produce more separable features for detection. The paper aims to propose an SDR implementation of the CNN-LSTM model for real-time detection by using the Universal Software Radio Peripheral (USRP) board and GNU radio platform.\u0000\u0000\u0000\u0000Results and Discussion: Numerical Simulation results reveal that the proposed CNN-LSTM outperforms the CNN, the LSTM, and the energy detector (ED) in terms of higher detection probability Pd and lower false alarm probability Pfa, even at low SNR. The SDR implementation results show the robustness of the CNN-LSTM method under several real-time detection scenarios: FM, GSM, and OFDM.\u0000\u0000\u0000\u0000The CNN-LSTM model used for spectrum sensing provides a high detection performance in a low SNR environment compared to LSTM, CNN, and the ED detector.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73159301","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 : 2023-06-05DOI: 10.2174/2210327913666230605120441
Leila Ghabeli
The compute-and-forward strategy is one of the outstanding methods which is used for interference management in wireless relay networks where decoding linear combinations of code words is required. Recently, many efforts have been made for decoding integer and non-integer combinations [1]-[7]. The difference between the methods is the manner of handling different conditions of networks, such as equal or unequal power constraints and equal or unequal channel gains. In this work, we present a modified n-step asymmetric successive compute-and-forward strategy for the communication network where we have both unequal power constraints and unequal channel gains conditions. In the proposed method, we scale channel gains and coefficients with the square root of power constraints. In this way, despite previous methods, without the need for scaling factors in our formulation, it is still able to solve the problem of general Gaussian relay networks with unequal power constraints and unequal channel gains. We also use scaling factors in our method in order to have the ability to divide the rates between users fairly. We evaluate the ability of the modified strategy for the uplink communication of the two-way relay channel, where one relay can help communication between the two users. At the relay, we decode the linear combinations of the messages of the two users and obtain 1/2 bit/sec/Hz per user capacity gap from the cut-set bound. Through some theoretical and simulation results, we show that by appropriately adjusting parameters, different points and areas of rate regions are achievable.
{"title":"Asymmetric Successive Compute-and-Forward and the Capacity Gap for the Gaussian Two-Way Relay Channel","authors":"Leila Ghabeli","doi":"10.2174/2210327913666230605120441","DOIUrl":"https://doi.org/10.2174/2210327913666230605120441","url":null,"abstract":"The compute-and-forward strategy is one of the outstanding methods which is used for interference management in wireless relay networks where decoding linear combinations of code words is required. Recently, many efforts have been made for decoding integer and non-integer combinations [1]-[7]. The difference between the methods is the manner of handling different conditions of networks, such as equal or unequal power constraints and equal or unequal channel gains.\u0000\u0000\u0000\u0000In this work, we present a modified n-step asymmetric successive compute-and-forward strategy for the communication network where we have both unequal power constraints and unequal channel gains conditions.\u0000\u0000\u0000\u0000In the proposed method, we scale channel gains and coefficients with the square root of power constraints. In this way, despite previous methods, without the need for scaling factors in our formulation, it is still able to solve the problem of general Gaussian relay networks with unequal power constraints and unequal channel gains. We also use scaling factors in our method in order to have the ability to divide the rates between users fairly.\u0000\u0000\u0000\u0000We evaluate the ability of the modified strategy for the uplink communication of the two-way relay channel, where one relay can help communication between the two users. At the relay, we decode the linear combinations of the messages of the two users and obtain 1/2 bit/sec/Hz per user capacity gap from the cut-set bound. Through some theoretical and simulation results, we show that by appropriately adjusting parameters, different points and areas of rate regions are achievable.","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90597620","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 : 2023-06-01DOI: 10.2174/2210327913666230601153113
L.Aziz, A. Gourari, S.Achki
Heterogeneous networks (HetNet) represent a promising technology that satisfies the needs of mobile users. However, several problems have influenced the performance of wireless communication, such as the maximization of energy efficiency and the problem of interferences due to the uncontrolled association of the user equipment (UE). Solving the problem of maximizing energy efficiency has captured the attention of several researchers. In this work, we propose an effective user association based on K-nearest Neighbors (KNN) approach considering a large dataset. The major novelty of this work is that the supervised learning perspective is applied to a dataset regrouped from an optimal user association, where the most valuable parameters are considered. Additionally, it allows for mitigating the problem of interferences using individual user association. Simulation results have proven the efficiency of the proposed methodology The suggested results have outperformed the two works in terms of accuracy, where the proposed method presents a better accuracy of 95%.
{"title":"A New Effective Strategy for User Association in Heterogeneous Networks","authors":"L.Aziz, A. Gourari, S.Achki","doi":"10.2174/2210327913666230601153113","DOIUrl":"https://doi.org/10.2174/2210327913666230601153113","url":null,"abstract":"\u0000\u0000Heterogeneous networks (HetNet) represent a promising technology that satisfies the needs of mobile users. However, several problems have influenced the performance of wireless communication, such as the maximization of energy efficiency and the problem of interferences due to the uncontrolled association of the user equipment (UE).\u0000\u0000\u0000\u0000Solving the problem of maximizing energy efficiency has captured the attention of several researchers. In this work, we propose an effective user association based on K-nearest Neighbors (KNN) approach considering a large dataset. The major novelty of this work is that the supervised learning perspective is applied to a dataset regrouped from an optimal user association, where the most valuable parameters are considered.\u0000\u0000\u0000\u0000Additionally, it allows for mitigating the problem of interferences using individual user association. Simulation results have proven the efficiency of the proposed methodology\u0000\u0000\u0000\u0000The suggested results have outperformed the two works in terms of accuracy, where the proposed method presents a better accuracy of 95%.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79283009","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 : 2023-05-25DOI: 10.2174/2210327913666230525141053
C. K. Marigowda, T. J, G. S, V. K. R., Muthyamala A
The Internet of Things has evolved over the years to a greater extent, where objects communicate with each other over a network. Heterogenous communication between the nodes leads to a large amount of information sharing, and sensitive information could be shared over the network. It is important to maintain privacy and security during information sharing to protect devices from communicating with malicious nodes. The concept of trust was introduced to prevent nodes from communicating with malicious nodes. A trust computation model for the IoT based on machine learning concepts was designed, which evaluates trust based on the Trust Marks. There are three trust marks, out of which two are evaluated. The three trust marks are knowledge, experience, and reputation. Knowledge trust marks are evaluated separately based on their trust property mathematical formulations, and then based on these properties, machine learning-based algorithms are applied to train the model to classify the objects as trustworthy and untrustworthy. The effectiveness of the Knowledge Trust Mark is measured by a simulation and confusion matrix. The accuracy of the trained model is shown by the accuracy score. The trust computational model for IoT using machine learning shows higher accuracy in classifying the objects as trustworthy and untrustworthy. The experience trust mark is evaluated based on its properties, and the behaviour of the experience is shown over time graphically.
{"title":"Trust Computational Model For Iot Using Machine Learning","authors":"C. K. Marigowda, T. J, G. S, V. K. R., Muthyamala A","doi":"10.2174/2210327913666230525141053","DOIUrl":"https://doi.org/10.2174/2210327913666230525141053","url":null,"abstract":"\u0000\u0000The Internet of Things has evolved over the years to a greater extent, where objects communicate with each other over a network. Heterogenous communication between the nodes leads to a large amount of information sharing, and sensitive information could be shared over the network. It is important to maintain privacy and security during information sharing to protect devices from communicating with malicious nodes.\u0000\u0000\u0000\u0000The concept of trust was introduced to prevent nodes from communicating with malicious nodes. A trust computation model for the IoT based on machine learning concepts was designed, which evaluates trust based on the Trust Marks. There are three trust marks, out of which two are evaluated. The three trust marks are knowledge, experience, and reputation. Knowledge trust marks are evaluated separately based on their trust property mathematical formulations, and then based on these properties, machine learning-based algorithms are applied to train the model to classify the objects as trustworthy and untrustworthy.\u0000\u0000\u0000\u0000The effectiveness of the Knowledge Trust Mark is measured by a simulation and confusion matrix. The accuracy of the trained model is shown by the accuracy score. The trust computational model for IoT using machine learning shows higher accuracy in classifying the objects as trustworthy and untrustworthy.\u0000\u0000\u0000\u0000The experience trust mark is evaluated based on its properties, and the behaviour of the experience is shown over time graphically.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"239 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74901755","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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