Pub Date : 2020-06-01DOI: 10.1109/ICC40277.2020.9149160
R. Lent, D. Brooks, G. Clark
The Cognitive Network Controller (CNC) defines a neuromorphic architecture where a spiking neural network can both encode network performance observations and select the optimal actions (e.g., routes) for the context of those observations. Because of these features, the CNC can quickly adapt to changes in the operational environment to either maintain or improve selected performance metrics. This behavior can be attractive for a space networking scenario with orbiting and ground-based assets that are either stationary or manned, bringing an elevated level of autonomy in network communication decisions. Using the SCaN testbed as a laboratory facility in orbit, we evaluated the adaptation abilities of the CNC applied to a space network routing application. Towards this end, the CNC design and the related neuromorphic processor were implemented in software and deployed on the flight computer of the SCaN testbed, and then applied to route bundles to a ground station over parallel links. This work likely constitutes the earliest demonstration of a space application for neuromorphic computing and a basic validation of the online adaptation capabilities of the CNC.
{"title":"Validating the Cognitive Network Controller on NASA’s SCaN Testbed","authors":"R. Lent, D. Brooks, G. Clark","doi":"10.1109/ICC40277.2020.9149160","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149160","url":null,"abstract":"The Cognitive Network Controller (CNC) defines a neuromorphic architecture where a spiking neural network can both encode network performance observations and select the optimal actions (e.g., routes) for the context of those observations. Because of these features, the CNC can quickly adapt to changes in the operational environment to either maintain or improve selected performance metrics. This behavior can be attractive for a space networking scenario with orbiting and ground-based assets that are either stationary or manned, bringing an elevated level of autonomy in network communication decisions. Using the SCaN testbed as a laboratory facility in orbit, we evaluated the adaptation abilities of the CNC applied to a space network routing application. Towards this end, the CNC design and the related neuromorphic processor were implemented in software and deployed on the flight computer of the SCaN testbed, and then applied to route bundles to a ground station over parallel links. This work likely constitutes the earliest demonstration of a space application for neuromorphic computing and a basic validation of the online adaptation capabilities of the CNC.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115364609","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 : 2020-06-01DOI: 10.1109/icc40277.2020.9149058
Yitu Wang, T. Nakachi
With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligent and efficient automation. Benefiting from discovering the sparse property of network traffic in temporal domain, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. For this purpose, we establish an analytical framework for network traffic prediction by extending traditional sparse representation to predictive sparse representation, and try to take the full advantage of such sparsity. Specifically, 1). To equip sparse representation with predictive capability, we divide the historical traffic records into two sets, and jointly train the representative/predictive dictionaries, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T+1 time slot behind counterpart. 2). To estimate the sparse code of the query point, we only have to decompose its counterpart into a sparse combination of the representative dictionary atoms by adopting iterative projection method, which provides extra flexibility and adaptability in determining the dependence range. After this, the prediction is performed based on the predictive dictionary. 3). To promote the capability of capturing the rapidly changing traffic, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and minimize the time averaged prediction error. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.
{"title":"The Learning and Prediction of Network Traffic: A Revisiting to Sparse Representation","authors":"Yitu Wang, T. Nakachi","doi":"10.1109/icc40277.2020.9149058","DOIUrl":"https://doi.org/10.1109/icc40277.2020.9149058","url":null,"abstract":"With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligent and efficient automation. Benefiting from discovering the sparse property of network traffic in temporal domain, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. For this purpose, we establish an analytical framework for network traffic prediction by extending traditional sparse representation to predictive sparse representation, and try to take the full advantage of such sparsity. Specifically, 1). To equip sparse representation with predictive capability, we divide the historical traffic records into two sets, and jointly train the representative/predictive dictionaries, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T+1 time slot behind counterpart. 2). To estimate the sparse code of the query point, we only have to decompose its counterpart into a sparse combination of the representative dictionary atoms by adopting iterative projection method, which provides extra flexibility and adaptability in determining the dependence range. After this, the prediction is performed based on the predictive dictionary. 3). To promote the capability of capturing the rapidly changing traffic, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and minimize the time averaged prediction error. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613170","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149354
Zhengyang Hu, J. Xue, Deyu Meng, Qian Zhao, Zongben Xu
In this paper, we study the channel state information (CSI) estimation by utilizing maximum entropy principle (MEP) and noise modeling method. The new model can not only represent the characters of the complex communication environment, but can also adjust itself according to the environment by using machine learning. In addition, a new iteration algorithm is presented to derive numerical results. Adaptive parameters learning and features choosing capability make the proposed method outperform the existing methods. The accuracy of estimation is verified by the Monte Carlo simulations.
{"title":"MEP-Based Channel Estimation under Complex Communication Environment","authors":"Zhengyang Hu, J. Xue, Deyu Meng, Qian Zhao, Zongben Xu","doi":"10.1109/ICC40277.2020.9149354","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149354","url":null,"abstract":"In this paper, we study the channel state information (CSI) estimation by utilizing maximum entropy principle (MEP) and noise modeling method. The new model can not only represent the characters of the complex communication environment, but can also adjust itself according to the environment by using machine learning. In addition, a new iteration algorithm is presented to derive numerical results. Adaptive parameters learning and features choosing capability make the proposed method outperform the existing methods. The accuracy of estimation is verified by the Monte Carlo simulations.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115710363","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148899
S. Misra, Saswati Pal, Shriya Kaneriya, S. Tanwar, Neeraj Kumar, J. Rodrigues
The development of nanomedical systems through the Internet of Bio-Nano Things (IoBNT) paradigm promotes designing of therapeutic models to facilitate drug transport and delivery. Such systems utilize microbial communities such as bacteria, which act as biosensors for molecular communication. We model the drug transport and delivery system by considering more realistic properties and characteristics of the biosensor community. We devise a Markov Decision Process (MDP) to model the biosensor lifecycle while considering division and death as parameters to regulate the model. This aids in estimating the required number of drug encapsulated biosensors. The proposed model indicates an increase in the number of instances of biosensor-target interactions that would be required for a better understanding of system dynamics. The proposed approach suggests a populace-aware coordination scheme with 3.5% increase in population, along with 20 -50% increase in information delivery. The solution proposed here can be harnessed in designing the number of optimum drug dosages. We show the effectiveness of our model with 90% increase in average biosensor lifetime, while highlighting the increase in the energy utilized in the network.
{"title":"Population Dynamics of Biosensors for Nano-therapeutic Applications in Internet of Bio-Nano Things","authors":"S. Misra, Saswati Pal, Shriya Kaneriya, S. Tanwar, Neeraj Kumar, J. Rodrigues","doi":"10.1109/ICC40277.2020.9148899","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148899","url":null,"abstract":"The development of nanomedical systems through the Internet of Bio-Nano Things (IoBNT) paradigm promotes designing of therapeutic models to facilitate drug transport and delivery. Such systems utilize microbial communities such as bacteria, which act as biosensors for molecular communication. We model the drug transport and delivery system by considering more realistic properties and characteristics of the biosensor community. We devise a Markov Decision Process (MDP) to model the biosensor lifecycle while considering division and death as parameters to regulate the model. This aids in estimating the required number of drug encapsulated biosensors. The proposed model indicates an increase in the number of instances of biosensor-target interactions that would be required for a better understanding of system dynamics. The proposed approach suggests a populace-aware coordination scheme with 3.5% increase in population, along with 20 -50% increase in information delivery. The solution proposed here can be harnessed in designing the number of optimum drug dosages. We show the effectiveness of our model with 90% increase in average biosensor lifetime, while highlighting the increase in the energy utilized in the network.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115728611","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148978
Wenchao Xu, Haibo Zhou, Tingting Yang, Huaqing Wu, Song Guo
By connecting the maritime users to Internet, e.g., boats, ships, etc., it is possible to operate maritime sensing and informatics across seas and oceans. Such marine Internet of things (MIoT) is urging intelligent maritime applications, e.g., real-time vessel tracking, navigation safety, autonomous shipping, etc. Due to the bandwidth limitation of conventional marine channels, broadband communication is desired for these emerging applications. In this paper, we consider operating the TV white space (TVWS) spectrum in 700MHz to support the near-sea surface communication for MIoT terminals. To better utilize the TV channel capacity, we propose a proactive and efficient link adaptation (LA) scheme based on nonlinear autoregressive neural network (NARNN) time series prediction. Specifically, the historical signal samplings are used to predict the near-sea-surface channel link status for the next transmission slot, which is then used to select a proper modulation and coding scheme (MCS) for the next egress frame. We have conducted extensive simulations, and show that the average channel utility can achieve almost 85% of the optimal capacity. The proposed LA scheme can provide useful inspirations for applying data analytics to efficient and adaptive LA schemes for mobile Internet of things.
{"title":"Proactive Link Adaptation for Marine Internet of Things in TV White Space","authors":"Wenchao Xu, Haibo Zhou, Tingting Yang, Huaqing Wu, Song Guo","doi":"10.1109/ICC40277.2020.9148978","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148978","url":null,"abstract":"By connecting the maritime users to Internet, e.g., boats, ships, etc., it is possible to operate maritime sensing and informatics across seas and oceans. Such marine Internet of things (MIoT) is urging intelligent maritime applications, e.g., real-time vessel tracking, navigation safety, autonomous shipping, etc. Due to the bandwidth limitation of conventional marine channels, broadband communication is desired for these emerging applications. In this paper, we consider operating the TV white space (TVWS) spectrum in 700MHz to support the near-sea surface communication for MIoT terminals. To better utilize the TV channel capacity, we propose a proactive and efficient link adaptation (LA) scheme based on nonlinear autoregressive neural network (NARNN) time series prediction. Specifically, the historical signal samplings are used to predict the near-sea-surface channel link status for the next transmission slot, which is then used to select a proper modulation and coding scheme (MCS) for the next egress frame. We have conducted extensive simulations, and show that the average channel utility can achieve almost 85% of the optimal capacity. The proposed LA scheme can provide useful inspirations for applying data analytics to efficient and adaptive LA schemes for mobile Internet of things.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121820666","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149402
Jielun Zhang, Fuhao Li, Feng Ye
Network intrusion detection is the fundamental of the Cybersecurity which plays an important role in preventing the systems away from malicious network traffic. Recent Artificial Intelligence (AI) based intrusion detection systems provide simple and accurate intrusion detection compared with the conventional intrusion detection schemes, however, the detection performance may not be reliable because the models in the AI algorithms must output a prediction result for each incoming instance even when the models are not confident. To tackle the issue, we propose to adopt Bayesian Deep Learning, specifically, Bayesian Convolutional Neural Network, to build intrusion detection models. Moreover, an ensemble-based detection scheme is further proposed to enhance the detection performance. Two open datasets (i.e., NSL-KDD and UNSW-NB15) are used to evaluate the proposed schemes. In comparison, Convolutional Neural Network and Support Vector Machine are implemented as baseline IDS (i.e., CNN-IDS and SVM-IDS). The evaluation results demonstrate that the proposed BCNN-IDS can significantly boost the detection accuracy and reduce the false alarm rate by adopting the proposed T-ensemble detection scheme.
{"title":"An Ensemble-based Network Intrusion Detection Scheme with Bayesian Deep Learning","authors":"Jielun Zhang, Fuhao Li, Feng Ye","doi":"10.1109/ICC40277.2020.9149402","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149402","url":null,"abstract":"Network intrusion detection is the fundamental of the Cybersecurity which plays an important role in preventing the systems away from malicious network traffic. Recent Artificial Intelligence (AI) based intrusion detection systems provide simple and accurate intrusion detection compared with the conventional intrusion detection schemes, however, the detection performance may not be reliable because the models in the AI algorithms must output a prediction result for each incoming instance even when the models are not confident. To tackle the issue, we propose to adopt Bayesian Deep Learning, specifically, Bayesian Convolutional Neural Network, to build intrusion detection models. Moreover, an ensemble-based detection scheme is further proposed to enhance the detection performance. Two open datasets (i.e., NSL-KDD and UNSW-NB15) are used to evaluate the proposed schemes. In comparison, Convolutional Neural Network and Support Vector Machine are implemented as baseline IDS (i.e., CNN-IDS and SVM-IDS). The evaluation results demonstrate that the proposed BCNN-IDS can significantly boost the detection accuracy and reduce the false alarm rate by adopting the proposed T-ensemble detection scheme.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776749","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148648
Dian Shi, Jixiang Lu, Jie Wang, Lixin Li, Kaikai Liu, M. Pan
According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people’s privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.
{"title":"No One Left Behind: Avoid Hot Car Deaths via WiFi Detection","authors":"Dian Shi, Jixiang Lu, Jie Wang, Lixin Li, Kaikai Liu, M. Pan","doi":"10.1109/ICC40277.2020.9148648","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148648","url":null,"abstract":"According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people’s privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"165 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120996350","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149428
Md Hasan Rahman, M. Ranjbar, N. Tran, K. Pham
This paper addresses the optimal signaling scheme and capacity of an additive Gaussian mixture (GM) noise channel using 1-bit output quantization. The considered GM distribution is a weighted sum Gaussian component densities with arbitrary means, and it can be used to represent any non-Gaussian channel of engineering interest. By first establishing a necessary and sufficient Kuhn-Tucker condition (KTC) for an input signal to be optimal, we demonstrate that the maximum number of mass points in the capacity-achieving signal is four. Our proof relies on novel bounds on the product of Q functions and Dubin’s theorem. By considering a special case of GM with zero mean Gaussian components, which is a realistic accurate model for co-channel interference in heterogeneous wireless networks and impulsive interference, it is shown that the optimal input is $pi$/2 circularly symmetric. As a result, in this case, the capacity-achieving signal has exactly four mass points forming a square centered at the origin. By further checking the first and second derivatives of the modified KTC, it is then shown that the phase of the optimal mass point located in the first quadrant is $pi$/4. Thus, with zero-mean GM, the capacity-achieving input signal is QPSK, and the channel capacity can be established in closed-form.
{"title":"Capacity-Achieving Signal and Capacity of Gaussian Mixture Channels with 1-bit Output Quantization","authors":"Md Hasan Rahman, M. Ranjbar, N. Tran, K. Pham","doi":"10.1109/ICC40277.2020.9149428","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149428","url":null,"abstract":"This paper addresses the optimal signaling scheme and capacity of an additive Gaussian mixture (GM) noise channel using 1-bit output quantization. The considered GM distribution is a weighted sum Gaussian component densities with arbitrary means, and it can be used to represent any non-Gaussian channel of engineering interest. By first establishing a necessary and sufficient Kuhn-Tucker condition (KTC) for an input signal to be optimal, we demonstrate that the maximum number of mass points in the capacity-achieving signal is four. Our proof relies on novel bounds on the product of Q functions and Dubin’s theorem. By considering a special case of GM with zero mean Gaussian components, which is a realistic accurate model for co-channel interference in heterogeneous wireless networks and impulsive interference, it is shown that the optimal input is $pi$/2 circularly symmetric. As a result, in this case, the capacity-achieving signal has exactly four mass points forming a square centered at the origin. By further checking the first and second derivatives of the modified KTC, it is then shown that the phase of the optimal mass point located in the first quadrant is $pi$/4. Thus, with zero-mean GM, the capacity-achieving input signal is QPSK, and the channel capacity can be established in closed-form.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121126024","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149115
G. Fragkos, Nicholas Kemp, Eirini-Eleni Tsiropoulou, S. Papavassiliou
The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs’ data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven by exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to an NE, and their tradeoffs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios.
{"title":"Artificial Intelligence Empowered UAVs Data Offloading in Mobile Edge Computing","authors":"G. Fragkos, Nicholas Kemp, Eirini-Eleni Tsiropoulou, S. Papavassiliou","doi":"10.1109/ICC40277.2020.9149115","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149115","url":null,"abstract":"The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs’ data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven by exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to an NE, and their tradeoffs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125053806","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 : 2020-06-01DOI: 10.1109/icc40277.2020.9149154
Bashar Tahir, Stefan Schwarz, M. Rupp
Uplink non-orthogonal multiple access (NOMA) has been proposed as an efficient technique to support massive connectivity and reduce access-latency. However, due to the inherent multiuser interference within such a system, iterative joint detection is required, which is of high-complexity. In this paper, we exploit the propagation properties of wireless channels to reduce the detection complexity. In particular, when neighboring spreading-blocks on the time-frequency grid experience similar channel conditions, then it is possible to reuse the calculated filter weights between them. We propose four detection strategies and compare them across a wide range of time- and frequency-selectively. Then, assuming the base station is equipped with a sufficient number of antennas, we replace the MMSE filter with a lower-complexity approximation using Neumann series expansion. The results show that our strategies incur only a small performance loss, while substantially cutting down complexity.
{"title":"Low-Complexity Detection of Uplink NOMA by Exploiting Properties of the Propagation Channel","authors":"Bashar Tahir, Stefan Schwarz, M. Rupp","doi":"10.1109/icc40277.2020.9149154","DOIUrl":"https://doi.org/10.1109/icc40277.2020.9149154","url":null,"abstract":"Uplink non-orthogonal multiple access (NOMA) has been proposed as an efficient technique to support massive connectivity and reduce access-latency. However, due to the inherent multiuser interference within such a system, iterative joint detection is required, which is of high-complexity. In this paper, we exploit the propagation properties of wireless channels to reduce the detection complexity. In particular, when neighboring spreading-blocks on the time-frequency grid experience similar channel conditions, then it is possible to reuse the calculated filter weights between them. We propose four detection strategies and compare them across a wide range of time- and frequency-selectively. Then, assuming the base station is equipped with a sufficient number of antennas, we replace the MMSE filter with a lower-complexity approximation using Neumann series expansion. The results show that our strategies incur only a small performance loss, while substantially cutting down complexity.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125129461","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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