Pub Date : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020888
D. Adesina, Olutobi Adagunodo, Xishuang Dong, Lijun Qian
Localization of aircraft is important to control air traffic safely and effectively. Although the Automatic Dependent Surveillance Broadcast (ADS-B) has many advantages, the transfer of control over the reported location to the aircraft brings a number of safety and security issues. In order to mitigate these issues and determine the locations of the aircraft which do not have position reporting capabilities or may report wrong locations, complementary or redundant localization methods that are independent of the aircraft are needed. The goal of this paper is to study the feasibility to localize aircraft (estimate the longitude, latitude, and altitude of an aircraft) based on crowdsourced air traffic control communication data, specifically time of arrival and signal strength measurements reported by many different sensors. Specifically, we design and test a deep neural network model for aircraft location prediction using realworld data from OpenSky Network, a crowd-sourced receiver network that obtains volumes of air traffic data from thousands of sensors. It is demonstrated that the proposed deep neural network outperforms the time difference of arrival (TDOA) and support vector regressor (SVR) in terms of the mean absolute percentage error (MAPE), and the proposed deep learning based method using crowdsourced air traffic control communication data is an effective solution for accurate aircraft location prediction that are independent of the aircraft.
{"title":"Aircraft Location Prediction using Deep Learning","authors":"D. Adesina, Olutobi Adagunodo, Xishuang Dong, Lijun Qian","doi":"10.1109/MILCOM47813.2019.9020888","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020888","url":null,"abstract":"Localization of aircraft is important to control air traffic safely and effectively. Although the Automatic Dependent Surveillance Broadcast (ADS-B) has many advantages, the transfer of control over the reported location to the aircraft brings a number of safety and security issues. In order to mitigate these issues and determine the locations of the aircraft which do not have position reporting capabilities or may report wrong locations, complementary or redundant localization methods that are independent of the aircraft are needed. The goal of this paper is to study the feasibility to localize aircraft (estimate the longitude, latitude, and altitude of an aircraft) based on crowdsourced air traffic control communication data, specifically time of arrival and signal strength measurements reported by many different sensors. Specifically, we design and test a deep neural network model for aircraft location prediction using realworld data from OpenSky Network, a crowd-sourced receiver network that obtains volumes of air traffic data from thousands of sensors. It is demonstrated that the proposed deep neural network outperforms the time difference of arrival (TDOA) and support vector regressor (SVR) in terms of the mean absolute percentage error (MAPE), and the proposed deep learning based method using crowdsourced air traffic control communication data is an effective solution for accurate aircraft location prediction that are independent of the aircraft.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612810","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020919
Alan J. Michaels, Michael Fletcher
This paper presents a candidate arbitrary-phase spread spectrum modulation technique that offers similar performance to spread continuous phase modulation (CPM) waveforms, yet supports additional capabilities for programming a chosen frequency domain spectra into the resulting spread spectrum signal. The proposed frequency-selective high-order phase shift keying (PSK) signaling (FS-HOPS) waveform is derived from arbitrary-phase sequence-based spread spectrum signals, with multi-bit resolution chaos-based sequences defining incremental phase words, enabling real-time efficient generation of practically non-repeating waveforms. The result of the FS-HOPS formulation is a parameterized hybrid modulation capable of selectively mitigating narrowband interference. Adaptation in this modulation may be easily implemented as a time-varying evolution, increasing the security of the waveform against tone jammers, while retaining many efficiently implementable receiver design characteristics of standard PSK modulations.
{"title":"Frequency-Selective High-Order Phase Shift Keying","authors":"Alan J. Michaels, Michael Fletcher","doi":"10.1109/MILCOM47813.2019.9020919","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020919","url":null,"abstract":"This paper presents a candidate arbitrary-phase spread spectrum modulation technique that offers similar performance to spread continuous phase modulation (CPM) waveforms, yet supports additional capabilities for programming a chosen frequency domain spectra into the resulting spread spectrum signal. The proposed frequency-selective high-order phase shift keying (PSK) signaling (FS-HOPS) waveform is derived from arbitrary-phase sequence-based spread spectrum signals, with multi-bit resolution chaos-based sequences defining incremental phase words, enabling real-time efficient generation of practically non-repeating waveforms. The result of the FS-HOPS formulation is a parameterized hybrid modulation capable of selectively mitigating narrowband interference. Adaptation in this modulation may be easily implemented as a time-varying evolution, increasing the security of the waveform against tone jammers, while retaining many efficiently implementable receiver design characteristics of standard PSK modulations.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122833591","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020949
William H. Clark, Vanessa Arndorfer, Brook Tamir, Daniel Kim, Cristian Vives, Hunter Morris, Lauren J. Wong, W. Headley
The application of machine learning to Automatic Modulation Classification (AMC) has typically used transfer learning from architectures found in the image classification domain. This work examines deviations from the image classification architectures by drawing from traditional expert feature systems within the AMC domain. Two types of ‘expert architectures’ are contrasted against the traditional image processing architectures; the first utilizes a more traditional one-versus-all binary classification with decision fusion approach, while the second inherits a hierarchical decision tree structure that leverages expert knowledge of the classes. When compared with a typical image processing architecture there are marginal classifier performance gains associated with the structures taken from expert AMC systems; however, the expert architectures allow for greater intuition, adaptability, and future-proofing in general.
{"title":"Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study","authors":"William H. Clark, Vanessa Arndorfer, Brook Tamir, Daniel Kim, Cristian Vives, Hunter Morris, Lauren J. Wong, W. Headley","doi":"10.1109/MILCOM47813.2019.9020949","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020949","url":null,"abstract":"The application of machine learning to Automatic Modulation Classification (AMC) has typically used transfer learning from architectures found in the image classification domain. This work examines deviations from the image classification architectures by drawing from traditional expert feature systems within the AMC domain. Two types of ‘expert architectures’ are contrasted against the traditional image processing architectures; the first utilizes a more traditional one-versus-all binary classification with decision fusion approach, while the second inherits a hierarchical decision tree structure that leverages expert knowledge of the classes. When compared with a typical image processing architecture there are marginal classifier performance gains associated with the structures taken from expert AMC systems; however, the expert architectures allow for greater intuition, adaptability, and future-proofing in general.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129635830","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020859
Mike Johnson, T. May, S. R. Thornton, S. Boyd, Brent Merle
Tactical waveform software applications tend to suffer from inefficient implementations despite the availability of free and open source software (FOSS) profiling tools. It has been the overwhelming experience of the authors that tactical waveform developers rely too heavily on the optimization power of the compiler rather than on sound, high-performance software engineering practices including performance profiling. The suite of FOSS profiling tools is mature, vast, and growing. Valgrind, gprof, and perf are tools used to identify software deficiencies that diminish the performance and reliability of tactical waveforms and the radios that host them. These deficiencies can be categorized as application-level and microarchitectural deficiencies. Inefficient input/output (I/O), memory leaks, uninitialized variables, race conditions, and improperly prioritized threads are examples of application-level deficiencies. Inefficient instruction and data cache utilization, and severe branch prediction misses are examples of microarchitectural deficiencies. This paper presents a methodology to apply FOSS tools and techniques to improve the performance posture in both the application and microarchitectural domain. Furthermore, results of the approach are presented in a case study involving application of the proposed techniques against a real Department of Defense (DoD) tactical waveform application.
{"title":"Improving the Performance of Tactical Waveform Software using Free and Open-Source Tools","authors":"Mike Johnson, T. May, S. R. Thornton, S. Boyd, Brent Merle","doi":"10.1109/MILCOM47813.2019.9020859","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020859","url":null,"abstract":"Tactical waveform software applications tend to suffer from inefficient implementations despite the availability of free and open source software (FOSS) profiling tools. It has been the overwhelming experience of the authors that tactical waveform developers rely too heavily on the optimization power of the compiler rather than on sound, high-performance software engineering practices including performance profiling. The suite of FOSS profiling tools is mature, vast, and growing. Valgrind, gprof, and perf are tools used to identify software deficiencies that diminish the performance and reliability of tactical waveforms and the radios that host them. These deficiencies can be categorized as application-level and microarchitectural deficiencies. Inefficient input/output (I/O), memory leaks, uninitialized variables, race conditions, and improperly prioritized threads are examples of application-level deficiencies. Inefficient instruction and data cache utilization, and severe branch prediction misses are examples of microarchitectural deficiencies. This paper presents a methodology to apply FOSS tools and techniques to improve the performance posture in both the application and microarchitectural domain. Furthermore, results of the approach are presented in a case study involving application of the proposed techniques against a real Department of Defense (DoD) tactical waveform application.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130023800","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020811
Janusz Kusyk, M. U. Uyar, Kelvin Ma, Joseph Plishka, G. Bertoli, J. Boksiner
Uninterrupted communication is crucial for modern electromagnetic (EM) spectrum operations where successes of situational awareness, defensive and offensive missions necessitate continuous reliance on wireless transmission. Preventing an adversary from dominating cyberspace becomes challenging as rapid technological developments allow state and non-state actors to engage in a broad range of destructive cyber electromagnetic activities (CEMA). Digital threats to communication networks can range from eavesdropping and impersonation attempts to various forms of denial-of-service attacks. In this paper, we present bio-inspired and game theory based flight control algorithms for a swarm of autonomous UAVs. Each UAV considers MANET connectivity, overshadowed ground area coverage and signal strength from interfering mobile radio emitters. Our algorithms use 3D Voronoi tessellations and linear interpolation for EM mapping of local neighborhood as part of decision making process. Simulation experiments in OPNET show that autonomous UAVS require only limited near neighbor communications to maintain a high area coverage overshadowed by the swarm with uninterrupted MANET connectivity. By providing a lightweight solution for rapidly deployable swarm of autonomous UAVS, our flight control algorithms are good candidates for deployment in complex environments in presence of adaptive and mobile sources of EM interference.
{"title":"AI and Game Theory based Autonomous UAV Swarm for Cybersecurity","authors":"Janusz Kusyk, M. U. Uyar, Kelvin Ma, Joseph Plishka, G. Bertoli, J. Boksiner","doi":"10.1109/MILCOM47813.2019.9020811","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020811","url":null,"abstract":"Uninterrupted communication is crucial for modern electromagnetic (EM) spectrum operations where successes of situational awareness, defensive and offensive missions necessitate continuous reliance on wireless transmission. Preventing an adversary from dominating cyberspace becomes challenging as rapid technological developments allow state and non-state actors to engage in a broad range of destructive cyber electromagnetic activities (CEMA). Digital threats to communication networks can range from eavesdropping and impersonation attempts to various forms of denial-of-service attacks. In this paper, we present bio-inspired and game theory based flight control algorithms for a swarm of autonomous UAVs. Each UAV considers MANET connectivity, overshadowed ground area coverage and signal strength from interfering mobile radio emitters. Our algorithms use 3D Voronoi tessellations and linear interpolation for EM mapping of local neighborhood as part of decision making process. Simulation experiments in OPNET show that autonomous UAVS require only limited near neighbor communications to maintain a high area coverage overshadowed by the swarm with uninterrupted MANET connectivity. By providing a lightweight solution for rapidly deployable swarm of autonomous UAVS, our flight control algorithms are good candidates for deployment in complex environments in presence of adaptive and mobile sources of EM interference.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116704277","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020956
Davide Callegaro, S. Baidya, G. Ramachandran, B. Krishnamachari, M. Levorato
Making Unmanned Aerial Vehicles (UAV) fully autonomous faces many challenges, some of which are connected to the inherent limitations of their on-board resources, such as energy supply, sensing capabilities, wireless characteristics, and computational power. The sensing, communication, and computation Internet of Things (IoT) infrastructure surrounding the UAVs can mitigate such limitations. However, external traffic dynamics, signal propagation, and other poignant characteristics of the IoT infrastructure make it an extremely dynamic and incoherent environment, especially in urban scenarios, thus challenging the use of IoT resources for mission-critical UAV applications. Herein, the concept of information autonomy is introduced to extend autonomy to encompass how information-related tasks are handled in this challenging scenario. In this paper, we motivate the need for “Information Autonomy” based on our observations from real-world experiments and present a self-adaptive framework for edge-assisted UAV applications. Through our preliminary evaluation, we show that our “Information Autonomy” framework is capable of handling uncertainties autonomously during run-time.
{"title":"Information Autonomy: Self-Adaptive Information Management for Edge-Assisted Autonomous UAV Systems","authors":"Davide Callegaro, S. Baidya, G. Ramachandran, B. Krishnamachari, M. Levorato","doi":"10.1109/MILCOM47813.2019.9020956","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020956","url":null,"abstract":"Making Unmanned Aerial Vehicles (UAV) fully autonomous faces many challenges, some of which are connected to the inherent limitations of their on-board resources, such as energy supply, sensing capabilities, wireless characteristics, and computational power. The sensing, communication, and computation Internet of Things (IoT) infrastructure surrounding the UAVs can mitigate such limitations. However, external traffic dynamics, signal propagation, and other poignant characteristics of the IoT infrastructure make it an extremely dynamic and incoherent environment, especially in urban scenarios, thus challenging the use of IoT resources for mission-critical UAV applications. Herein, the concept of information autonomy is introduced to extend autonomy to encompass how information-related tasks are handled in this challenging scenario. In this paper, we motivate the need for “Information Autonomy” based on our observations from real-world experiments and present a self-adaptive framework for edge-assisted UAV applications. Through our preliminary evaluation, we show that our “Information Autonomy” framework is capable of handling uncertainties autonomously during run-time.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115761890","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020850
Christopher Sweet, Stephen Moskal, S. Yang
Cyber Intrusion alerts are commonly collected by corporations to analyze network traffic and glean information about attacks perpetrated against the network. However, datasets of true malignant alerts are rare and generally only show one potential attack scenario out of many possible ones. Furthermore, it is difficult to expand the analysis of these alerts through artificial means due to the complexity of feature dependencies within an alert and lack of rare yet critical samples. This work proposes the use of a Mutual Information constrained Generative Adversarial Network as a means to synthesize new alerts from historical data. Histogram Intersection and Conditional Entropy are used to show the performance of this model as well as it's ability to learn intricate feature dependencies. The proposed models are able to capture a much wider domain of alert feature values than standard Generative Adversarial Networks. Finally, we show that when looking at alerts from the perspective of attack stages, the proposed models are able to capture critical attacker behavior providing direct semantic meaning to generated samples.
{"title":"Synthetic Intrusion Alert Generation through Generative Adversarial Networks","authors":"Christopher Sweet, Stephen Moskal, S. Yang","doi":"10.1109/MILCOM47813.2019.9020850","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020850","url":null,"abstract":"Cyber Intrusion alerts are commonly collected by corporations to analyze network traffic and glean information about attacks perpetrated against the network. However, datasets of true malignant alerts are rare and generally only show one potential attack scenario out of many possible ones. Furthermore, it is difficult to expand the analysis of these alerts through artificial means due to the complexity of feature dependencies within an alert and lack of rare yet critical samples. This work proposes the use of a Mutual Information constrained Generative Adversarial Network as a means to synthesize new alerts from historical data. Histogram Intersection and Conditional Entropy are used to show the performance of this model as well as it's ability to learn intricate feature dependencies. The proposed models are able to capture a much wider domain of alert feature values than standard Generative Adversarial Networks. Finally, we show that when looking at alerts from the perspective of attack stages, the proposed models are able to capture critical attacker behavior providing direct semantic meaning to generated samples.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126578","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020889
Imtiaz Nasim, Seungmo Kim
Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).
{"title":"Human EMF Exposure in Wearable Networks for Internet of Battlefield Things","authors":"Imtiaz Nasim, Seungmo Kim","doi":"10.1109/MILCOM47813.2019.9020889","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020889","url":null,"abstract":"Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157482","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020989
Chad Josephson, E. Perrins, M. Rice
This paper shows that burst-based orthogonal spacetime block-coded ARTM CPM is capable of solving the two-antenna problem in aeronautical telemetry, but detection requires a prohibitively complex trellis detector. In single-input, single-output (SISO) applications, pulse truncation and state-space partitioning reduce the computational complexity of the trellis detector with only modest bit error rate (BER) performance penalties. In this paper it is shown that layering pulse truncation and state-space partition complexity-reducing techniques with a burst-based orthogonal space-time block-code does not introduce additional BER performance losses relative to the SISO case.
{"title":"Space-Time Coded ARTM CPM for Aeronautical Telemetry","authors":"Chad Josephson, E. Perrins, M. Rice","doi":"10.1109/MILCOM47813.2019.9020989","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020989","url":null,"abstract":"This paper shows that burst-based orthogonal spacetime block-coded ARTM CPM is capable of solving the two-antenna problem in aeronautical telemetry, but detection requires a prohibitively complex trellis detector. In single-input, single-output (SISO) applications, pulse truncation and state-space partitioning reduce the computational complexity of the trellis detector with only modest bit error rate (BER) performance penalties. In this paper it is shown that layering pulse truncation and state-space partition complexity-reducing techniques with a burst-based orthogonal space-time block-code does not introduce additional BER performance losses relative to the SISO case.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128214807","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 : 2019-11-01DOI: 10.1109/MILCOM47813.2019.9020991
W. Leonard, Alex Saunders, Michael Calabro, Katherine Olsen
We describe a machine learning enabled architecture and design for a multi-waveform radio receiver in the pursuit of a truly cognitive radio with more functionality and adaptability than current software defined radio implementations. This machine learning approach brings closer to reality the vision of cognitive radios proposed by Joseph Mitola III and Gerald Q. Maguire, Jr. Cognitive radios make decisions about their communications regime about where (in spectrum), and how (waveform parameters) to transmit and receive information [1]. And, such radios should be able to self-optimize their communications to most efficiently maximize data capacity in power and spectrum constrained environments. To achieve these goals, the software in the radio must control more of the functionality, including functions in the physical layer. Building on Tim O'Shea's and Jakob Hoydis' ideas [2], we have developed a generalized architecture, in which the physical layer functions: Frequency correction, timing correction, demodulation, and bit error correction are performed by an artificial neural network capable of processing several signal types and waveforms.
{"title":"A Multi-waveform Radio Receiver, an Example of Machine Learning Enabled Radio Architecture and Design","authors":"W. Leonard, Alex Saunders, Michael Calabro, Katherine Olsen","doi":"10.1109/MILCOM47813.2019.9020991","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020991","url":null,"abstract":"We describe a machine learning enabled architecture and design for a multi-waveform radio receiver in the pursuit of a truly cognitive radio with more functionality and adaptability than current software defined radio implementations. This machine learning approach brings closer to reality the vision of cognitive radios proposed by Joseph Mitola III and Gerald Q. Maguire, Jr. Cognitive radios make decisions about their communications regime about where (in spectrum), and how (waveform parameters) to transmit and receive information [1]. And, such radios should be able to self-optimize their communications to most efficiently maximize data capacity in power and spectrum constrained environments. To achieve these goals, the software in the radio must control more of the functionality, including functions in the physical layer. Building on Tim O'Shea's and Jakob Hoydis' ideas [2], we have developed a generalized architecture, in which the physical layer functions: Frequency correction, timing correction, demodulation, and bit error correction are performed by an artificial neural network capable of processing several signal types and waveforms.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218782","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：