Pub Date : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322121
An-An Lee, Yung-Shun Wang, Y. Hong
This work proposes a deep-learning (DL) based coordinated precoder design for multicell downlink systems with rate-limited exchange of channel state information (CSI) among base-stations (BSs). Two CSI compression techniques are proposed, one based on a binarized convolutional neural network (CNN) and one based on a learned vector-quantization (VQ) codebook. The former utilizes a CNN-based CSI feature extractor to directly compute the binary feature vector that is to be exchanged with other BSs. The latter utilizes a DL-based VQ codebook to encode the CSI feature vector that is obtained at the output of the feature extractor. In both cases, each BS takes the rate-limited CSI received from other BSs as input to a precoder network that produces the normalized precoding vectors and the transmit powers using a multitask learning architecture. By using solutions of the weighted minimum mean square error (WMMSE) algorithm as the output labels, end-to-end training of both the CSI compression and transmit precoder networks is performed jointly at all BSs. By doing so, the CSI compression networks will be able to extract the CSI features that are most effective for precoder computation at the BSs. Our simulation results show that the proposed schemes can achieve weighted sum rates close to that in the full CSI scenario, even when the number of exchanged bits is small, and outperform existing random VQ methods.
{"title":"Deep CSI Compression and Coordinated Precoding for Multicell Downlink Systems","authors":"An-An Lee, Yung-Shun Wang, Y. Hong","doi":"10.1109/GLOBECOM42002.2020.9322121","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322121","url":null,"abstract":"This work proposes a deep-learning (DL) based coordinated precoder design for multicell downlink systems with rate-limited exchange of channel state information (CSI) among base-stations (BSs). Two CSI compression techniques are proposed, one based on a binarized convolutional neural network (CNN) and one based on a learned vector-quantization (VQ) codebook. The former utilizes a CNN-based CSI feature extractor to directly compute the binary feature vector that is to be exchanged with other BSs. The latter utilizes a DL-based VQ codebook to encode the CSI feature vector that is obtained at the output of the feature extractor. In both cases, each BS takes the rate-limited CSI received from other BSs as input to a precoder network that produces the normalized precoding vectors and the transmit powers using a multitask learning architecture. By using solutions of the weighted minimum mean square error (WMMSE) algorithm as the output labels, end-to-end training of both the CSI compression and transmit precoder networks is performed jointly at all BSs. By doing so, the CSI compression networks will be able to extract the CSI features that are most effective for precoder computation at the BSs. Our simulation results show that the proposed schemes can achieve weighted sum rates close to that in the full CSI scenario, even when the number of exchanged bits is small, and outperform existing random VQ methods.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86926984","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348013
Rodolfo W. L. Coutinho, A. Boukerche
Internet of Underwater Things (IoUT) has gained increased attention as an envisioned technology for supporting smart ocean applications. However, the harsh aquatic environment and challenges of underwater acoustic communication still severely limit data collection in underwater networks and IoUT applications. In recent years, programmable physical layer and multi-modal communication for IoUT have been proposed to improve the performance of underwater networks. However, several fundamental challenges need yet to be investigated and tackled, in order to achieve efficient data collection in IoUT. One of the daunting fundamental challenge to be solved is the design of innovative routing protocols for multi-modal IoUT. In this paper, we propose a mathematical model for the study of opportunistic routing (OR) in multi-modal IoUT. The devised mathematical framework models the unique characteristics of OR in multi-modal IoUT scenarios, while considering the peculiar characteristics of the underwater environment and acoustic communication. Moreover, we propose a candidate set selection procedure of OR, which jointly selects the acoustic modem and next-hop forwarder candidate nodes at each hop, to increase data delivery. Numerical results showed the potential of multimodal communication for improving data delivery in the harsh environment of underwater acoustic communication.
{"title":"Stochastic Modeling of Opportunistic Routing in Multi-Modal Internet of Underwater Things","authors":"Rodolfo W. L. Coutinho, A. Boukerche","doi":"10.1109/GLOBECOM42002.2020.9348013","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348013","url":null,"abstract":"Internet of Underwater Things (IoUT) has gained increased attention as an envisioned technology for supporting smart ocean applications. However, the harsh aquatic environment and challenges of underwater acoustic communication still severely limit data collection in underwater networks and IoUT applications. In recent years, programmable physical layer and multi-modal communication for IoUT have been proposed to improve the performance of underwater networks. However, several fundamental challenges need yet to be investigated and tackled, in order to achieve efficient data collection in IoUT. One of the daunting fundamental challenge to be solved is the design of innovative routing protocols for multi-modal IoUT. In this paper, we propose a mathematical model for the study of opportunistic routing (OR) in multi-modal IoUT. The devised mathematical framework models the unique characteristics of OR in multi-modal IoUT scenarios, while considering the peculiar characteristics of the underwater environment and acoustic communication. Moreover, we propose a candidate set selection procedure of OR, which jointly selects the acoustic modem and next-hop forwarder candidate nodes at each hop, to increase data delivery. Numerical results showed the potential of multimodal communication for improving data delivery in the harsh environment of underwater acoustic communication.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"2015 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87139817","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322585
Yuhang Ye, Brian Lee, Yuansong Qiao
Content replication and name-based routing in Named Data Network (NDN) naturally lead to connectionless multi-source and multipath transmissions. Traditional congestion control designed for end-to-end connections cannot well fit this architecture. Explicit congestion notification (ECN) can better support NDN because congestion is detected where it occurs and ECN can timely notify the traffic initiator of congestion. NDN can be deployed as an overlay protocol (sharing the underlying devices with other protocols), which means the congestion may also occur at an underlying device (e.g. a switch). In this case, the NDN nodes cannot access the queue or other link status at a remote underlying device for congestion detection. A promising ECN scheme must be able to detect congestion happening anywhere (at an NDN node or an underlying device) without using underlying link information. This paper proposes Hop-byHop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) to enable detecting congestion and generating ECN at NDN nodes via monitoring the change of transmission delays. HbHCM measures the transmission delay at the hop level and PAQM converts the delay to ECN signals to notify consumers. We compared HbHCM + PAQM with two milestone solutions (router-label and ECN-based). The simulation results show that HbHCM + PAQM can accurately detect congestion, improve bandwidth utilisation and better support multipath transmission, no need to rely on route or link information.
{"title":"Hop-by-Hop Congestion Measurement and Practical Active Queue Management in NDN","authors":"Yuhang Ye, Brian Lee, Yuansong Qiao","doi":"10.1109/GLOBECOM42002.2020.9322585","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322585","url":null,"abstract":"Content replication and name-based routing in Named Data Network (NDN) naturally lead to connectionless multi-source and multipath transmissions. Traditional congestion control designed for end-to-end connections cannot well fit this architecture. Explicit congestion notification (ECN) can better support NDN because congestion is detected where it occurs and ECN can timely notify the traffic initiator of congestion. NDN can be deployed as an overlay protocol (sharing the underlying devices with other protocols), which means the congestion may also occur at an underlying device (e.g. a switch). In this case, the NDN nodes cannot access the queue or other link status at a remote underlying device for congestion detection. A promising ECN scheme must be able to detect congestion happening anywhere (at an NDN node or an underlying device) without using underlying link information. This paper proposes Hop-byHop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) to enable detecting congestion and generating ECN at NDN nodes via monitoring the change of transmission delays. HbHCM measures the transmission delay at the hop level and PAQM converts the delay to ECN signals to notify consumers. We compared HbHCM + PAQM with two milestone solutions (router-label and ECN-based). The simulation results show that HbHCM + PAQM can accurately detect congestion, improve bandwidth utilisation and better support multipath transmission, no need to rely on route or link information.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"22 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87907593","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322096
Zhangyu Li, Zhi Sun
Wireless power transfer (WPT) has been widely used in IoT applications, such as mobile device charging, biomedical implants communication, and RFID field. Maximizing the power transfer efficiency (PTE) becomes one of the most crucial problems for designing the WPT systems. Magnetic induction (MI) beamforming has been proposed recently to maximize the PTE for the near field MIMO WPT systems. However, conventional magnetic beamforming in WPT systems usually requires accurate magnetic channel estimation, both amplitude and phase control of the charging source, which can not be achieved in an extreme environment. In this paper, we propose a novel magnetic induction beamforming scheme in MIMO WPT system using a reconfigurable metasurface. Instead of controlling the source currents or voltages, the reconfigurable metasurface can achieve near field beamforming only by varying the capacitor and resistance in specific coil array units. The beamforming is modeled as a discrete optimization problem and solved by using the Simulate Anneal (SA) method. Through the analytical and COMSOL simulation results, our proposed beamforming scheme can achieve approximately two times PTE of the conventional beamforming method in a 40 cm charging distance.
{"title":"Enabling Magnetic Beamforming in MIMO Wireless Power Transfer Using Reconfigurable Metasurface","authors":"Zhangyu Li, Zhi Sun","doi":"10.1109/GLOBECOM42002.2020.9322096","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322096","url":null,"abstract":"Wireless power transfer (WPT) has been widely used in IoT applications, such as mobile device charging, biomedical implants communication, and RFID field. Maximizing the power transfer efficiency (PTE) becomes one of the most crucial problems for designing the WPT systems. Magnetic induction (MI) beamforming has been proposed recently to maximize the PTE for the near field MIMO WPT systems. However, conventional magnetic beamforming in WPT systems usually requires accurate magnetic channel estimation, both amplitude and phase control of the charging source, which can not be achieved in an extreme environment. In this paper, we propose a novel magnetic induction beamforming scheme in MIMO WPT system using a reconfigurable metasurface. Instead of controlling the source currents or voltages, the reconfigurable metasurface can achieve near field beamforming only by varying the capacitor and resistance in specific coil array units. The beamforming is modeled as a discrete optimization problem and solved by using the Simulate Anneal (SA) method. Through the analytical and COMSOL simulation results, our proposed beamforming scheme can achieve approximately two times PTE of the conventional beamforming method in a 40 cm charging distance.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"44 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88000123","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348216
Augusto F. S. Moura, S. S. L. Pereira, Mário W. L. Moreira, J. Rodrigues
Reductions in installation and storage costs have increased the demand for security systems, including video surveillance and digital authentication. The video surveillance systems, when monitored by humans, are subject to errors and are challenging to scale. Authentication systems can validate someone using a password or a card from another user. Facial recognition algorithms can solve this fault by the traffic monitoring of known individuals or intruders as well as for individual biometric authentication. Hence, this paper evaluates the FaceNet approach using the Labeled Faces in the Wild benchmark, as well as evaluates a machine learning technique known as support vector machine (SVM) for the classification of embedding generated using FaceNet. The suggested approach also models a real-time facial recognition system combining FaceNet and SVM, reaching 90% of accuracy using a medium webcam.
{"title":"Video Monitoring System using Facial Recognition: A Facenet-based Approach","authors":"Augusto F. S. Moura, S. S. L. Pereira, Mário W. L. Moreira, J. Rodrigues","doi":"10.1109/GLOBECOM42002.2020.9348216","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348216","url":null,"abstract":"Reductions in installation and storage costs have increased the demand for security systems, including video surveillance and digital authentication. The video surveillance systems, when monitored by humans, are subject to errors and are challenging to scale. Authentication systems can validate someone using a password or a card from another user. Facial recognition algorithms can solve this fault by the traffic monitoring of known individuals or intruders as well as for individual biometric authentication. Hence, this paper evaluates the FaceNet approach using the Labeled Faces in the Wild benchmark, as well as evaluates a machine learning technique known as support vector machine (SVM) for the classification of embedding generated using FaceNet. The suggested approach also models a real-time facial recognition system combining FaceNet and SVM, reaching 90% of accuracy using a medium webcam.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88208855","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322549
Asmaa Ali, H. Hassanein
Monitoring the climate is one of the most important and challenging practices by which to obtain optimum crop production in a greenhouse. In a smart greenhouse, a wireless sensor network (WSN) can be used to monitor the microclimate. Constant monitoring and sensing can result in excessive energy consumption. Prediction of the microclimate can be used to control the operation of sensors and hence lower the energy consumed by sensor nodes. We develop a Long Short-Term Memory (LSTM) based on time series for the prediction of the maximum, minimum, and mean values of the air temperature, relative humidity, pressure, wind, and dew point. Microclimate data inside and Macroclimate data outside the greenhouse are collected daily and used for the analysis of the best-fitting LSTM model. After determining the network structure and parameters, the network is then trained. The statistical criteria for measuring the network performance are the Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). A comparison is made between the measured and predicted values of temperature, relative humidity, pressure, dew point and wind. Results indicate the effectiveness of the predictive model performance LSTM in predicting the microclimate. Statistical analysis of the RMSE and MAE results demonstrate the prediction accuracy of our proposed LSTM model.
{"title":"Time-Series Prediction for Sensing in Smart Greenhouses","authors":"Asmaa Ali, H. Hassanein","doi":"10.1109/GLOBECOM42002.2020.9322549","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322549","url":null,"abstract":"Monitoring the climate is one of the most important and challenging practices by which to obtain optimum crop production in a greenhouse. In a smart greenhouse, a wireless sensor network (WSN) can be used to monitor the microclimate. Constant monitoring and sensing can result in excessive energy consumption. Prediction of the microclimate can be used to control the operation of sensors and hence lower the energy consumed by sensor nodes. We develop a Long Short-Term Memory (LSTM) based on time series for the prediction of the maximum, minimum, and mean values of the air temperature, relative humidity, pressure, wind, and dew point. Microclimate data inside and Macroclimate data outside the greenhouse are collected daily and used for the analysis of the best-fitting LSTM model. After determining the network structure and parameters, the network is then trained. The statistical criteria for measuring the network performance are the Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). A comparison is made between the measured and predicted values of temperature, relative humidity, pressure, dew point and wind. Results indicate the effectiveness of the predictive model performance LSTM in predicting the microclimate. Statistical analysis of the RMSE and MAE results demonstrate the prediction accuracy of our proposed LSTM model.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"93 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88278917","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322312
Dhaval K. Patel, Brijesh Soni, Y. Guan, Sumei Sun, Yoong Choon Chang, J. Lim
In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.
{"title":"Performance Analysis of Arbitrary Correlated Multiantenna Receiver for Mobile Cognitive User","authors":"Dhaval K. Patel, Brijesh Soni, Y. Guan, Sumei Sun, Yoong Choon Chang, J. Lim","doi":"10.1109/GLOBECOM42002.2020.9322312","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322312","url":null,"abstract":"In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"26 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86394387","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348120
Yaojiang Yu, Shumei Liu, Weina Yuan, Phee Lep Yeoh, B. Vucetic, Yonghui Li
In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.
{"title":"Robust Secure Beamforming for Multi-Receiver Multi-Eavesdropper MIMO SWIPT Systems","authors":"Yaojiang Yu, Shumei Liu, Weina Yuan, Phee Lep Yeoh, B. Vucetic, Yonghui Li","doi":"10.1109/GLOBECOM42002.2020.9348120","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348120","url":null,"abstract":"In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86197197","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348166
Huanzhuo Wu, Yunbin Sheri, Jiajing Zhang, H. Salah, I. Tsokalo, F. Fitzek
Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.
{"title":"Adaptive Extraction-Based Independent Component Analysis for Time-Sensitive Applications","authors":"Huanzhuo Wu, Yunbin Sheri, Jiajing Zhang, H. Salah, I. Tsokalo, F. Fitzek","doi":"10.1109/GLOBECOM42002.2020.9348166","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348166","url":null,"abstract":"Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"09 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86217967","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322388
Jun Zou, K. Shum, C. Sung
A novel non-orthogonal multiple access (NOMA) scheme with spherical code superposition and its user pairing strategy are proposed. A transmitter transmits the superposition of the signals of two users, each of which is selected from the high dimensional spherical code on the same time-frequency resource by power-domain multiplexing NOMA. Upper bounds of the word error probabilities of the two users are derived. Based on them, a power allocation scheme for the two users is proposed, which guarantees that their word error probabilities are below a certain threshold. Under our power allocation scheme, the optimal user pairing strategy that minimizes the total power consumption in a general multi-user system is analytically found. Numerical results show that our proposed system outperforms some benchmark methods.
{"title":"Spherical Code Superposition NOMA and Its User Pairing Strategy","authors":"Jun Zou, K. Shum, C. Sung","doi":"10.1109/GLOBECOM42002.2020.9322388","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322388","url":null,"abstract":"A novel non-orthogonal multiple access (NOMA) scheme with spherical code superposition and its user pairing strategy are proposed. A transmitter transmits the superposition of the signals of two users, each of which is selected from the high dimensional spherical code on the same time-frequency resource by power-domain multiplexing NOMA. Upper bounds of the word error probabilities of the two users are derived. Based on them, a power allocation scheme for the two users is proposed, which guarantees that their word error probabilities are below a certain threshold. Under our power allocation scheme, the optimal user pairing strategy that minimizes the total power consumption in a general multi-user system is analytically found. Numerical results show that our proposed system outperforms some benchmark methods.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"160 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86334760","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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