Pub Date : 2017-10-01DOI: 10.1109/MILCOM.2017.8170741
D. Anderson
Software Defined Networking (SDN) is rapidly gaining acceptance and use in terrestrial networks but little research has been done to apply it to aerial networks. This paper describes an investigation into five open-source controllers using a specific set of criteria based on the characteristics of these networks. A preliminary qualitative investigation compares the controllers based on their state handling and failure recovery mechanisms, resulting in the selection of two controllers for further investigation. Further quantitative tests were performed on these controllers to determine which was more suitable for deployment in an airborne environment. Key aspects such as controller failure recovery and the resultant generated traffic were analyzed and quantified. Due to the much lower bandwidth in aerial networks when compared to terrestrial networks, a low-bandwidth solution with high recovery speed and adaptability is required. This investigation takes these factors into account and gives insight into which open-source controller would be best as a starting point for use in this highly constrained environment.
{"title":"An investigation into the use of software defined networking controllers in aerial networks","authors":"D. Anderson","doi":"10.1109/MILCOM.2017.8170741","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170741","url":null,"abstract":"Software Defined Networking (SDN) is rapidly gaining acceptance and use in terrestrial networks but little research has been done to apply it to aerial networks. This paper describes an investigation into five open-source controllers using a specific set of criteria based on the characteristics of these networks. A preliminary qualitative investigation compares the controllers based on their state handling and failure recovery mechanisms, resulting in the selection of two controllers for further investigation. Further quantitative tests were performed on these controllers to determine which was more suitable for deployment in an airborne environment. Key aspects such as controller failure recovery and the resultant generated traffic were analyzed and quantified. Due to the much lower bandwidth in aerial networks when compared to terrestrial networks, a low-bandwidth solution with high recovery speed and adaptability is required. This investigation takes these factors into account and gives insight into which open-source controller would be best as a starting point for use in this highly constrained environment.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131679860","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170860
Xiao Xu, Sattar Vakili, Qing Zhao, A. Swami
An online learning problem with side information is considered. The problem is formulated as a graph structured stochastic Multi-Armed Bandit (MAB). Each node in the graph represents an arm in the bandit problem and an edge between two arms indicates closeness in their mean rewards. It is shown that such side information induces a Unit Interval Graph and several graph properties can be leveraged to achieve a sublinear regret in the number of arms while preserving the optimal logarithmic regret in time. A lower bound on regret is established and a hierarchical learning policy that is order optimal in terms of both the number of arms and the learning horizon is developed.
{"title":"Online learning with side information","authors":"Xiao Xu, Sattar Vakili, Qing Zhao, A. Swami","doi":"10.1109/MILCOM.2017.8170860","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170860","url":null,"abstract":"An online learning problem with side information is considered. The problem is formulated as a graph structured stochastic Multi-Armed Bandit (MAB). Each node in the graph represents an arm in the bandit problem and an edge between two arms indicates closeness in their mean rewards. It is shown that such side information induces a Unit Interval Graph and several graph properties can be leveraged to achieve a sublinear regret in the number of arms while preserving the optimal logarithmic regret in time. A lower bound on regret is established and a hierarchical learning policy that is order optimal in terms of both the number of arms and the learning horizon is developed.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126556864","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170819
S. Soltani, Y. Sagduyu, Sean Scanlon, Yi Shi, Jason H. Li, Jared Feldman, J. Matyjas
This paper presents the joint design of network coding and backpressure algorithm for cognitive radio networks and its implementation with software-defined radios (SDRs) in a high fidelity network emulation testbed. The backpressure algorithm is known to provide throughput optimal solutions to joint routing and scheduling for dynamic packet traffic. This solution applies to cognitive radio networks with spectrum dynamics changing over time and space, and supports joint routing and spectrum access without any need for end-to-end path maintenance. The backpressure algorithm is extended to multicast traffic with network coding deployed over virtual queues that represent different flows per session and destination. This extension is supported with different methods to decode packets at destinations. In the absence of a common control channel, distributed coordination with local information exchange is used to support neighborhood discovery, spectrum sensing and channel estimation that are integrated with joint routing, channel access and network coding. Cognitive network functionalities are implemented with GNU Radio and Python modules for different network layers, and used with USRP N210 radios. Practical radio implementation issues are addressed in a distributed wireless network setting, where USRP N210 radios communicate with each other through RFnest, a high fidelity wireless network emulation tool. RFnest provides realistic physical channel environment by digitally controlling path loss, fading, delay, topology and mobility effects. Extensive emulation test results are provided to assess throughput, backlog and energy consumption and verify the SDR implementation of joint network coding and backpressure algorithm under realistic channel and radio hardware effects.
{"title":"Joint network coding and backpressure algorithm for cognitive radio networks","authors":"S. Soltani, Y. Sagduyu, Sean Scanlon, Yi Shi, Jason H. Li, Jared Feldman, J. Matyjas","doi":"10.1109/MILCOM.2017.8170819","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170819","url":null,"abstract":"This paper presents the joint design of network coding and backpressure algorithm for cognitive radio networks and its implementation with software-defined radios (SDRs) in a high fidelity network emulation testbed. The backpressure algorithm is known to provide throughput optimal solutions to joint routing and scheduling for dynamic packet traffic. This solution applies to cognitive radio networks with spectrum dynamics changing over time and space, and supports joint routing and spectrum access without any need for end-to-end path maintenance. The backpressure algorithm is extended to multicast traffic with network coding deployed over virtual queues that represent different flows per session and destination. This extension is supported with different methods to decode packets at destinations. In the absence of a common control channel, distributed coordination with local information exchange is used to support neighborhood discovery, spectrum sensing and channel estimation that are integrated with joint routing, channel access and network coding. Cognitive network functionalities are implemented with GNU Radio and Python modules for different network layers, and used with USRP N210 radios. Practical radio implementation issues are addressed in a distributed wireless network setting, where USRP N210 radios communicate with each other through RFnest, a high fidelity wireless network emulation tool. RFnest provides realistic physical channel environment by digitally controlling path loss, fading, delay, topology and mobility effects. Extensive emulation test results are provided to assess throughput, backlog and energy consumption and verify the SDR implementation of joint network coding and backpressure algorithm under realistic channel and radio hardware effects.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"360 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781633","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170768
Jaime C. Acosta, Joshua McKee, Alexander Fielder, S. Salamah
Empirical-based models for security technologies in the commercial and military domain, including those that focus on protection, detection, and broader risk analysis, leverage data captured from sensors on network-connected devices including gateways, routers, and host nodes. Lacking, however, are datasets that contain specific state observations and actions from the evaluator (red/blue teammer) workstation; we call this the inside-view. This is largely due to issues associated with data ownership, data classification, and the lack of integrated evaluator-centric data-collection mechanisms. To enable and promote creation of open datasets that capture the inside-view, we introduce a scalable platform that consists of two main elements. First, the emulation sandbox, or EmuBox, is an open-source and portable (i.e., it can execute on a laptop) solution for creating small-to medium-sized heterogeneous scenarios for evaluators to set up practice environments and competitions and to hone their skills. Second, the evaluatorcentric and extensible logger, ECEL, is a centralized management system that uses plugins for capturing and formatting evaluator data. We conclude the paper by providing a case study to demonstrate the setup and configuration of the platform along with a performance analysis.
{"title":"A platform for evaluator-centric cybersecurity training and data acquisition","authors":"Jaime C. Acosta, Joshua McKee, Alexander Fielder, S. Salamah","doi":"10.1109/MILCOM.2017.8170768","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170768","url":null,"abstract":"Empirical-based models for security technologies in the commercial and military domain, including those that focus on protection, detection, and broader risk analysis, leverage data captured from sensors on network-connected devices including gateways, routers, and host nodes. Lacking, however, are datasets that contain specific state observations and actions from the evaluator (red/blue teammer) workstation; we call this the inside-view. This is largely due to issues associated with data ownership, data classification, and the lack of integrated evaluator-centric data-collection mechanisms. To enable and promote creation of open datasets that capture the inside-view, we introduce a scalable platform that consists of two main elements. First, the emulation sandbox, or EmuBox, is an open-source and portable (i.e., it can execute on a laptop) solution for creating small-to medium-sized heterogeneous scenarios for evaluators to set up practice environments and competitions and to hone their skills. Second, the evaluatorcentric and extensible logger, ECEL, is a centralized management system that uses plugins for capturing and formatting evaluator data. We conclude the paper by providing a case study to demonstrate the setup and configuration of the platform along with a performance analysis.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122825227","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170720
Çağrı Göken, Onur Dizdar
Edge windowing is a windowing technique for Orthogonal Frequency Division Multiplexing (OFDM) signals based on the idea of using shorter cyclic prefix (CP) and longer window lengths at the edge subcarriers while keeping the symbol length fixed. In this study, we investigate the performance of OFDM signals with edge windowing under non-linear power amplifier (PA) effects by observing out-of-band (OOB) emission characteristics, average error vector magnitude (EVM) and coded block error rate (BLER) performance. We explore whether the possible gains over conventional windowing in the presence of PA is possible. We show that the edge windowing can still provide improvements over conventional windowing in terms of OOB emission suppression under various PA models at the expense of increased average EVM, whereas the channel coding substantially mitigates the performance loss due to inter-symbol and inter-carrier interference (ISI-ICI) effects arising as a result of shorter CP length at the edge subcarriers.
{"title":"Performance of edge windowing for OFDM under non-linear power amplifier effects","authors":"Çağrı Göken, Onur Dizdar","doi":"10.1109/MILCOM.2017.8170720","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170720","url":null,"abstract":"Edge windowing is a windowing technique for Orthogonal Frequency Division Multiplexing (OFDM) signals based on the idea of using shorter cyclic prefix (CP) and longer window lengths at the edge subcarriers while keeping the symbol length fixed. In this study, we investigate the performance of OFDM signals with edge windowing under non-linear power amplifier (PA) effects by observing out-of-band (OOB) emission characteristics, average error vector magnitude (EVM) and coded block error rate (BLER) performance. We explore whether the possible gains over conventional windowing in the presence of PA is possible. We show that the edge windowing can still provide improvements over conventional windowing in terms of OOB emission suppression under various PA models at the expense of increased average EVM, whereas the channel coding substantially mitigates the performance loss due to inter-symbol and inter-carrier interference (ISI-ICI) effects arising as a result of shorter CP length at the edge subcarriers.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114694308","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170840
Mahi Abdelbar, R. Buehrer
Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor localization of mobile users is presented by exploiting trajectory tracking techniques using neural networks (NNs). The motion trajectories of indoor mobile users are tracked using conventional positioning algorithms, then a NN is applied to identify the current room location of a mobile user based on the tracked motion trajectory. Simulation results show that the trajectory-based NN is able to provide indoor location information at the room level with much higher accuracy in different scenarios, with an enhancement of up to 49% in correct room identification, as compared to positioning techniques based only on a single-point location estimate. In addition, miss-classification of the NN system will result in selecting one of the immediate neighboring rooms instead with at least 30% probability.
{"title":"Indoor localization through trajectory tracking using neural networks","authors":"Mahi Abdelbar, R. Buehrer","doi":"10.1109/MILCOM.2017.8170840","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170840","url":null,"abstract":"Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor localization of mobile users is presented by exploiting trajectory tracking techniques using neural networks (NNs). The motion trajectories of indoor mobile users are tracked using conventional positioning algorithms, then a NN is applied to identify the current room location of a mobile user based on the tracked motion trajectory. Simulation results show that the trajectory-based NN is able to provide indoor location information at the room level with much higher accuracy in different scenarios, with an enhancement of up to 49% in correct room identification, as compared to positioning techniques based only on a single-point location estimate. In addition, miss-classification of the NN system will result in selecting one of the immediate neighboring rooms instead with at least 30% probability.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114423974","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170807
Yi Shi, Y. Sagduyu
This paper presents a novel approach to launch and defend against the causative and evasion attacks on machine learning classifiers. As the preliminary step, the adversary starts with an exploratory attack based on deep learning (DL) and builds a functionally equivalent classifier by polling the online target classifier with input data and observing the returned labels. Using this inferred classifier, the adversary can select samples according to their DL scores and feed them to the original classifier. In an evasion attack, the adversary feeds the target classifier with test data after selecting samples with DL scores that are close to the decision boundary to increase the chance that these samples are misclassified. In a causative attack, the adversary feeds the target classifier with training data after changing the labels of samples with DL scores that are far away from the decision boundary to reduce the reliability of the training process. Results obtained for text and image classification show that the proposed evasion and causative attacks can significantly increase the error during test and training phases, respectively. A defense strategy is presented to change a small number of labels of the original classifier to prevent its reliable inference by the adversary and its effective use in evasion and causative attacks. These findings identify new vulnerabilities of machine learning and demonstrate that a proactive defense mechanism can reduce the impact of the underlying attacks.
{"title":"Evasion and causative attacks with adversarial deep learning","authors":"Yi Shi, Y. Sagduyu","doi":"10.1109/MILCOM.2017.8170807","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170807","url":null,"abstract":"This paper presents a novel approach to launch and defend against the causative and evasion attacks on machine learning classifiers. As the preliminary step, the adversary starts with an exploratory attack based on deep learning (DL) and builds a functionally equivalent classifier by polling the online target classifier with input data and observing the returned labels. Using this inferred classifier, the adversary can select samples according to their DL scores and feed them to the original classifier. In an evasion attack, the adversary feeds the target classifier with test data after selecting samples with DL scores that are close to the decision boundary to increase the chance that these samples are misclassified. In a causative attack, the adversary feeds the target classifier with training data after changing the labels of samples with DL scores that are far away from the decision boundary to reduce the reliability of the training process. Results obtained for text and image classification show that the proposed evasion and causative attacks can significantly increase the error during test and training phases, respectively. A defense strategy is presented to change a small number of labels of the original classifier to prevent its reliable inference by the adversary and its effective use in evasion and causative attacks. These findings identify new vulnerabilities of machine learning and demonstrate that a proactive defense mechanism can reduce the impact of the underlying attacks.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"39 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933959","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170735
Ahmad A. I. Ibrahim, Andrew C. Marcum, D. Love, J. Krogmeier
A key focus of wireless communications research is on solutions that catalyze broadband access everywhere at anytime. Relaying has been introduced as a solution to enable communication between users who suffer from poor channel conditions. Distributed relay networks are a special case where spatial diversity is obtained by using a relay consisting of many geographically dispersed nodes. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit data simultaneously through the uplink to the relay nodes of the relay. Each node independently quantizes the observed signal to a few bits and broadcasts these bits through the band-limited downlink channel to the users. In this paper, we consider a multi-way quantized distributed relay network where the relay facilitates the communication between many users. For decoding purposes, we develop algorithms that can be employed by the users to estimate their uplink channels as well as the uplink channels observed by all other users when nodes perform simple sign quantization. A near maximum likelihood (nearML) channel estimator is derived. In addition, other sub-optimal estimators that are more computationally efficient than the nearML technique are also presented. Via simulation, we compare the performance of the proposed channel estimators.
{"title":"Channel estimation for multi-way quantized distributed wireless relaying","authors":"Ahmad A. I. Ibrahim, Andrew C. Marcum, D. Love, J. Krogmeier","doi":"10.1109/MILCOM.2017.8170735","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170735","url":null,"abstract":"A key focus of wireless communications research is on solutions that catalyze broadband access everywhere at anytime. Relaying has been introduced as a solution to enable communication between users who suffer from poor channel conditions. Distributed relay networks are a special case where spatial diversity is obtained by using a relay consisting of many geographically dispersed nodes. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit data simultaneously through the uplink to the relay nodes of the relay. Each node independently quantizes the observed signal to a few bits and broadcasts these bits through the band-limited downlink channel to the users. In this paper, we consider a multi-way quantized distributed relay network where the relay facilitates the communication between many users. For decoding purposes, we develop algorithms that can be employed by the users to estimate their uplink channels as well as the uplink channels observed by all other users when nodes perform simple sign quantization. A near maximum likelihood (nearML) channel estimator is derived. In addition, other sub-optimal estimators that are more computationally efficient than the nearML technique are also presented. Via simulation, we compare the performance of the proposed channel estimators.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122909728","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170864
W. Watson, Steve Huntsman, J. Dolan
The future of sensor systems is moving quickly to the realm of distributed multi-static RF Internet of Things (RIOTs). In this case, any single sensor is limited in its ability to generate useful information but the community of sensors, networked together, create the necessary information. Specifically, the ability to geolocate and track small Unmanned Aerial Systems (UASs) with a set of distributed multi-static mobile RIOTs, e.g. RIOTs on a swarm of UASs, will become more the norm than an anomaly. Optimally networked swarms of small, inexpensive, mobile, unmanned platforms outfitted with RIOTS into an effective sensor suite is a difficult task. In this paper, rather than treat each RIOT as an individual sensor managed individually by the local platform, we demonstrate techniques for creating Software Defined Networks (SDNs) of RIOTS organized at the RF pulse level providing the ability to perform novel methods of detecting, geolocating and tracking both active emitters and passive reflectors of RF signals in highly dynamic environments.
{"title":"Software Defined Networks (SDNs) of RF Internet of Things (RIOTs) on Unmanned Aerial Systems (UASs)","authors":"W. Watson, Steve Huntsman, J. Dolan","doi":"10.1109/MILCOM.2017.8170864","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170864","url":null,"abstract":"The future of sensor systems is moving quickly to the realm of distributed multi-static RF Internet of Things (RIOTs). In this case, any single sensor is limited in its ability to generate useful information but the community of sensors, networked together, create the necessary information. Specifically, the ability to geolocate and track small Unmanned Aerial Systems (UASs) with a set of distributed multi-static mobile RIOTs, e.g. RIOTs on a swarm of UASs, will become more the norm than an anomaly. Optimally networked swarms of small, inexpensive, mobile, unmanned platforms outfitted with RIOTS into an effective sensor suite is a difficult task. In this paper, rather than treat each RIOT as an individual sensor managed individually by the local platform, we demonstrate techniques for creating Software Defined Networks (SDNs) of RIOTS organized at the RF pulse level providing the ability to perform novel methods of detecting, geolocating and tracking both active emitters and passive reflectors of RF signals in highly dynamic environments.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548239","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 : 2017-10-01DOI: 10.1109/MILCOM.2017.8170856
M. Renzo
The emerging market of the Internet of Things (IoT) requires new energy-efficient and low-complexity Multiple-Input-Multiple-Output (MIMO-) aided radio access technologies. This trend will have a profound impact on both the theory and practice of future communication networks, which will not be purely optimized for approaching the attainable capacity anymore, but will explicitly include the energy efficiency for the design and optimization of the entire protocol stack. In this paper, we discuss a recently introduced modulation scheme for IoT applications, which leverages the concepts of Reconfigurable Antennas (RecAnts) and Spatial Modulation (SM). RecAnt-SM constitutes a promising new air interface in the context of MIMO-aided transmission, which can be beneficially invoked for the design of medium-throughput, low-complexity and energy-efficient communication systems by relying on a limited number of RF chains and the flexibility of simple RecAnt designs.
新兴的物联网（IoT）市场需要新型高能效、低复杂度的多输入多输出（MIMO）辅助无线接入技术。这一趋势将对未来通信网络的理论和实践产生深远影响，未来的通信网络将不再单纯为接近可达到的容量而优化，而是将能效明确纳入整个协议栈的设计和优化中。在本文中，我们将讨论最近针对物联网应用推出的一种调制方案，该方案利用了可重构天线（RecAnts）和空间调制（SM）的概念。RecAnt-SM 是 MIMO 辅助传输背景下一种前景广阔的新空中接口，通过依赖数量有限的射频链和简单 RecAnt 设计的灵活性，可用于设计中等吞吐量、低复杂度和高能效的通信系统。
{"title":"Spatial modulation based on reconfigurable antennas — A new air interface for the IoT","authors":"M. Renzo","doi":"10.1109/MILCOM.2017.8170856","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170856","url":null,"abstract":"The emerging market of the Internet of Things (IoT) requires new energy-efficient and low-complexity Multiple-Input-Multiple-Output (MIMO-) aided radio access technologies. This trend will have a profound impact on both the theory and practice of future communication networks, which will not be purely optimized for approaching the attainable capacity anymore, but will explicitly include the energy efficiency for the design and optimization of the entire protocol stack. In this paper, we discuss a recently introduced modulation scheme for IoT applications, which leverages the concepts of Reconfigurable Antennas (RecAnts) and Spatial Modulation (SM). RecAnt-SM constitutes a promising new air interface in the context of MIMO-aided transmission, which can be beneficially invoked for the design of medium-throughput, low-complexity and energy-efficient communication systems by relying on a limited number of RF chains and the flexibility of simple RecAnt designs.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126636585","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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