Pub Date : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114939
Wenyun Dai, Longbin Chen, A. Wu, M. Ali
Mobile apps in smartphones are over-collect users’ data, that harms users’ privacy. To overcome the coarse-grained access control offered by current mobile operating systems, we propose DASC to protect mobile users’ privacy by placing private data into the cloud storage. The cloud storage provides thorough and fine-grained access control, but it decreases the performance due to the extra network communication. DASC offers a cache mechanism to improve the access performance by remaining frequently used ordinary mobile data within the smartphones. We treat the storage of smartphones as the cache and the cloud storage as the memory. We design the PrefLRU algorithm based on the classic LRU algorithm but adding users’ preference about data type into consideration. The user preference vector is dynamically changing referring to the data access requests. We analyze the performance of DASC with different sets of workload. Evaluations on real smartphones show the performance improvements resulting from DASC.
{"title":"DASC: A Privacy-Protected Data Access System with Cache Mechanism for Smartphones","authors":"Wenyun Dai, Longbin Chen, A. Wu, M. Ali","doi":"10.1109/WOCC48579.2020.9114939","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114939","url":null,"abstract":"Mobile apps in smartphones are over-collect users’ data, that harms users’ privacy. To overcome the coarse-grained access control offered by current mobile operating systems, we propose DASC to protect mobile users’ privacy by placing private data into the cloud storage. The cloud storage provides thorough and fine-grained access control, but it decreases the performance due to the extra network communication. DASC offers a cache mechanism to improve the access performance by remaining frequently used ordinary mobile data within the smartphones. We treat the storage of smartphones as the cache and the cloud storage as the memory. We design the PrefLRU algorithm based on the classic LRU algorithm but adding users’ preference about data type into consideration. The user preference vector is dynamically changing referring to the data access requests. We analyze the performance of DASC with different sets of workload. Evaluations on real smartphones show the performance improvements resulting from DASC.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129037949","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114931
C. Sugama, V. Chandrasekar
Satellite communication system consolidation reduces the amount of shipboard topside and below deck systems required to perform radio frequency capabilities. Parabolic antenna stacking alleviates the need for multiple topside antennas to operate at commercial and military radio frequencies. This paper presents the design and simulated results of a dual splash plate parabolic stacked antenna that has the ability to operate at frequencies of four different Navy antenna variants (NMT Q/Ka, NMT X/Ka, CBSP FLV and CBSP ULV). The dual splash plate parabolic stacked antenna is focused on providing an L, C, K, Ka, Ku, X and Q band capable solution on a single pedestal to save space on the topside of shipboard platforms.
{"title":"Dual Splash Plate Parabolic Stacked Antenna for Satellite Communication System Consolidation","authors":"C. Sugama, V. Chandrasekar","doi":"10.1109/WOCC48579.2020.9114931","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114931","url":null,"abstract":"Satellite communication system consolidation reduces the amount of shipboard topside and below deck systems required to perform radio frequency capabilities. Parabolic antenna stacking alleviates the need for multiple topside antennas to operate at commercial and military radio frequencies. This paper presents the design and simulated results of a dual splash plate parabolic stacked antenna that has the ability to operate at frequencies of four different Navy antenna variants (NMT Q/Ka, NMT X/Ka, CBSP FLV and CBSP ULV). The dual splash plate parabolic stacked antenna is focused on providing an L, C, K, Ka, Ku, X and Q band capable solution on a single pedestal to save space on the topside of shipboard platforms.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122567896","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114911
Mingju He, Shengliang Peng, Huaxia Wang, Yu-dong Yao
Spectrum awareness is now becoming more and more important in recent years, which can be utilized in areas like spectrum resource allocation, spectrum management, inference control, and security protection. Deep learning (DL) models, including convolutional neural network models have been widely used for classification related tasks, such as modulation classification, medium access control protocol (MAC) classification, and spectrum sensing. In this paper, a pre-trained Inception V3 model (CNN-based) is used to classify industrial, scientific, and medical (ISM) radio band signals. Experimentation results demonstrate the effectiveness of deep learning in ISM band signal identification.
{"title":"Identification of ISM Band Signals Using Deep Learning","authors":"Mingju He, Shengliang Peng, Huaxia Wang, Yu-dong Yao","doi":"10.1109/WOCC48579.2020.9114911","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114911","url":null,"abstract":"Spectrum awareness is now becoming more and more important in recent years, which can be utilized in areas like spectrum resource allocation, spectrum management, inference control, and security protection. Deep learning (DL) models, including convolutional neural network models have been widely used for classification related tasks, such as modulation classification, medium access control protocol (MAC) classification, and spectrum sensing. In this paper, a pre-trained Inception V3 model (CNN-based) is used to classify industrial, scientific, and medical (ISM) radio band signals. Experimentation results demonstrate the effectiveness of deep learning in ISM band signal identification.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124428","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114944
M. Islam, M. Younis
The scarcity of the optical power is the main challenge for underwater visible light communication. It becomes worst for communication across the air-water interface because of the reflection of light from the air-water interface. Differential pulse position modulation (DPPM) is one of the power efficient modulation techniques. In L-DPPM a block of $M=log_{2}L$ input data is mapped into one of the L distinct waveforms containing only one on’ chip. The size of the DPPM packet is variable and depends on the value of input data and L, which makes error detection quite challenging. In this paper, we propose a frame structure that efficiently enables error detection within a packet for various symbol length, L, of DPPM. We also propose an algorithm using such a frame structure to enable effective detection of packet errors and for adaptively changing the value of L for optimal power efficiency while meeting a certain bound on the packet error rate (PER). We have named our proposed protocol as adaptive differential pulse position modulation (ADPPM). The Bit rate and PER have been studied for different signal-to-noise ratio (SNR) through simulation. A comparison between ADPPM and OOK, DPPM with fixed L is provided.
{"title":"An Adaptive DPPM for Efficient and Robust Visible Light Communication Across the Air-Water Interface","authors":"M. Islam, M. Younis","doi":"10.1109/WOCC48579.2020.9114944","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114944","url":null,"abstract":"The scarcity of the optical power is the main challenge for underwater visible light communication. It becomes worst for communication across the air-water interface because of the reflection of light from the air-water interface. Differential pulse position modulation (DPPM) is one of the power efficient modulation techniques. In L-DPPM a block of $M=log_{2}L$ input data is mapped into one of the L distinct waveforms containing only one on’ chip. The size of the DPPM packet is variable and depends on the value of input data and L, which makes error detection quite challenging. In this paper, we propose a frame structure that efficiently enables error detection within a packet for various symbol length, L, of DPPM. We also propose an algorithm using such a frame structure to enable effective detection of packet errors and for adaptively changing the value of L for optimal power efficiency while meeting a certain bound on the packet error rate (PER). We have named our proposed protocol as adaptive differential pulse position modulation (ADPPM). The Bit rate and PER have been studied for different signal-to-noise ratio (SNR) through simulation. A comparison between ADPPM and OOK, DPPM with fixed L is provided.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"10 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122953862","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114946
Umang Garg, N. K, B. Sainath
Visible Light Communication (VLC) is among one-of-the top emerging next-generation technology and a key-enabler for the future networks, utilizing the visible light spectrum to achieve nominal data rates of Gbps. The compatibility of VLC with the existing infrastructure proves to be a major thrust in this domain, but suffers from signal fading and considerable interference provided by the nearby illuminants. Thus, a cooperative gain stage becomes essential to boost the signal present in the Non-line-of-sight (NLOS) with minimum power overheads. This paper proposes an optimal solution for the spectral efficiency of the VLC link: by introducing relay-based adaptive power-sharing schemes for improvement in signal strength, under the constraints of LED linear radiation region and threshold power consumption. DCO-OFDM modulation technique has been used to achieve minimum inter-symbol interference owing to its circular convolution properties. The Analytic analysis, as well as simulation results, are presented to show the effect of the scheme over an uncontrolled relay link. Later, a generalized model is proposed, and analytic methodology is adopted to determine optimal power-sharing coefficients. As a Figure of merit, we observe 18.29 % improvement in spectral efficiency at 13 dB of signal to noise ratio (SNR) when compared to an unoptimized collaborative link.
{"title":"Spectrally Efficient Cooperative Visible Light Communication with Adaptive Power Sharing for a Generalized System","authors":"Umang Garg, N. K, B. Sainath","doi":"10.1109/WOCC48579.2020.9114946","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114946","url":null,"abstract":"Visible Light Communication (VLC) is among one-of-the top emerging next-generation technology and a key-enabler for the future networks, utilizing the visible light spectrum to achieve nominal data rates of Gbps. The compatibility of VLC with the existing infrastructure proves to be a major thrust in this domain, but suffers from signal fading and considerable interference provided by the nearby illuminants. Thus, a cooperative gain stage becomes essential to boost the signal present in the Non-line-of-sight (NLOS) with minimum power overheads. This paper proposes an optimal solution for the spectral efficiency of the VLC link: by introducing relay-based adaptive power-sharing schemes for improvement in signal strength, under the constraints of LED linear radiation region and threshold power consumption. DCO-OFDM modulation technique has been used to achieve minimum inter-symbol interference owing to its circular convolution properties. The Analytic analysis, as well as simulation results, are presented to show the effect of the scheme over an uncontrolled relay link. Later, a generalized model is proposed, and analytic methodology is adopted to determine optimal power-sharing coefficients. As a Figure of merit, we observe 18.29 % improvement in spectral efficiency at 13 dB of signal to noise ratio (SNR) when compared to an unoptimized collaborative link.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682035","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114934
Yaoqing Liu, L. Njilla, Anthony Dowling, W. Du
This work leverages Named Data Networks (NDN), an emerging information-centric network architecture, to interconnect diverse wireless links at the network layer and implements flexible routing and forwarding strategies for efficient information dissemination. The system focuses on implementing the interface between NDN and LoRaWAN, and interconnecting LoRaWAN and WiFi via the NDN Forwarding Daemon (NFD) into a ubiquitous ad hoc network, which bears very longrange and multi-hop capabilities for Device-to-Device (D2D) communication. Field experimental results show that the newly built ad hoc network can easily cover a radius of several kilometers and make full use of NDN features to maximize utilization of heterogeneous wireless links and efficiency of information dissemination.
{"title":"Empowering Named Data Networks for Ad-Hoc Long-Range Communication","authors":"Yaoqing Liu, L. Njilla, Anthony Dowling, W. Du","doi":"10.1109/WOCC48579.2020.9114934","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114934","url":null,"abstract":"This work leverages Named Data Networks (NDN), an emerging information-centric network architecture, to interconnect diverse wireless links at the network layer and implements flexible routing and forwarding strategies for efficient information dissemination. The system focuses on implementing the interface between NDN and LoRaWAN, and interconnecting LoRaWAN and WiFi via the NDN Forwarding Daemon (NFD) into a ubiquitous ad hoc network, which bears very longrange and multi-hop capabilities for Device-to-Device (D2D) communication. Field experimental results show that the newly built ad hoc network can easily cover a radius of several kilometers and make full use of NDN features to maximize utilization of heterogeneous wireless links and efficiency of information dissemination.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129052718","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114927
Bruce Hartpence, Andres Kwasinski
Increasingly researchers are turning to machine learning techniques such as artificial neural networks to address communication network research questions. At the heart of each challenge is the need to classify packets and improve visibility. To date, multi-layer perceptron neural networks have been used to successfully identify individual packets. This work utilizes convolutional neural networks to classify packets after their conversion to an image matrix. To help address network challenges and aid in visualization, packets are combined into larger images to provide greater insight into a particular time span. Applications of this research can use the surrounding temporal area to gain insight into conversations, exchanges, losses and threats. We demonstrate the use of this technique to identify potential latency problems. This approach of using contemporary network traffic and convolutional neural networks has success rate for individual packets exceeding 99%. Larger images providing a broader view achieve the same high level of accuracy.
{"title":"A Convolutional Neural Network Approach to Improving Network Visibility","authors":"Bruce Hartpence, Andres Kwasinski","doi":"10.1109/WOCC48579.2020.9114927","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114927","url":null,"abstract":"Increasingly researchers are turning to machine learning techniques such as artificial neural networks to address communication network research questions. At the heart of each challenge is the need to classify packets and improve visibility. To date, multi-layer perceptron neural networks have been used to successfully identify individual packets. This work utilizes convolutional neural networks to classify packets after their conversion to an image matrix. To help address network challenges and aid in visualization, packets are combined into larger images to provide greater insight into a particular time span. Applications of this research can use the surrounding temporal area to gain insight into conversations, exchanges, losses and threats. We demonstrate the use of this technique to identify potential latency problems. This approach of using contemporary network traffic and convolutional neural networks has success rate for individual packets exceeding 99%. Larger images providing a broader view achieve the same high level of accuracy.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115965995","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114921
Nagwa Ibrahim, A. Eltholth, M. El-Soudani
Cloud radio access network (C-RAN) architecture is actively considered as a major candidate for future wireless communications. The aerial communication network such as high altitude balloon (HAB) is used to transport the fronthaul among radio transceivers and processing units. Both free space optic (FSO) and millimeter wave (mmWave) are promising technologies, but each one has its impairments that affect its efficiency under different weather conditions. So, a hybrid channel is considered to match with the requirements of fronthaul networks. This paper aims to optimize the hand over process between FSO and mmWave channels to maximize the sum data rate for the fronthaul in C-RAN architecture. The problem is formulated as an integer linear programming (ILP) problem. The mathematical programming is applied on the hybrid transmission technology FSO/mmWave channel. The obtained numerical results indicate the potential of hybrid FSO/mmWave channel in counteracting the effect of different weather conditions.
{"title":"Hybrid FSO/mmWave based Fronthaul C-RAN Optimization for Future Wireless Communications","authors":"Nagwa Ibrahim, A. Eltholth, M. El-Soudani","doi":"10.1109/WOCC48579.2020.9114921","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114921","url":null,"abstract":"Cloud radio access network (C-RAN) architecture is actively considered as a major candidate for future wireless communications. The aerial communication network such as high altitude balloon (HAB) is used to transport the fronthaul among radio transceivers and processing units. Both free space optic (FSO) and millimeter wave (mmWave) are promising technologies, but each one has its impairments that affect its efficiency under different weather conditions. So, a hybrid channel is considered to match with the requirements of fronthaul networks. This paper aims to optimize the hand over process between FSO and mmWave channels to maximize the sum data rate for the fronthaul in C-RAN architecture. The problem is formulated as an integer linear programming (ILP) problem. The mathematical programming is applied on the hybrid transmission technology FSO/mmWave channel. The obtained numerical results indicate the potential of hybrid FSO/mmWave channel in counteracting the effect of different weather conditions.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133941189","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114928
Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Mofadal Alymani, Mohsen H. Alhazmi, Zikang Sheng, Yu-dong Yao
Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.
{"title":"Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning","authors":"Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Mofadal Alymani, Mohsen H. Alhazmi, Zikang Sheng, Yu-dong Yao","doi":"10.1109/WOCC48579.2020.9114928","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114928","url":null,"abstract":"Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132894694","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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