2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)最新文献

Machine Learning based Adaptive Predistorter for High Power Amplifier Linearization 基于机器学习的高功率放大器线性化自适应预失真器

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904896

Jingyang Lu, Lun Li, John Nguyen, Dan Shen, X. Tian, Genshe Chen, K. Pham

{"title":"Machine Learning based Adaptive Predistorter for High Power Amplifier Linearization","authors":"Jingyang Lu, Lun Li, John Nguyen, Dan Shen, X. Tian, Genshe Chen, K. Pham","doi":"10.1109/CCAAW.2019.8904896","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904896","url":null,"abstract":"In this paper, we have developed a machine learning based adaptive predistorter for high power amplifier linearization approach under dynamically changing environment. In the “bent-pipe” transponder in satellite communication (SATCOM) system, the High Power Amplifiers (HPAs), which are similar to other amplifiers in the communication system, can cause nonlinear distortions to transmitted signals, deteriorating the system transmission performance. The traditional model based processing techniques such as the Extended Saleh's Model (ESM) based predistortion design can be applied to maximize transponder throughput along with HPA power efficiency but sensitive to dynamically changing environment. In this paper, the compensated HPA linearity characterized through Amplitude Modulation-to-Amplitude Modulation (AM-AM) and Amplitude Modulation-to-Phase Modulation (AM-PM) effects is used as the system reward, we leveraged reinforcement learning approach to dynamically optimize the parameter set for the ESM based PD to improve system performance in various environmental conditions. Finally, simulation results are provided to evaluate and verify Bit Error Rate (BER) improvement for the considered SATCOM system by applying our proposed PD technique.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"25 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114114235","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}

引用次数: 4

Reinforcement Learning Applied to Cognitive Space Communications 强化学习在认知空间通信中的应用

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904912

Carson D. Schubert, Rigoberto Roche', J. Briones

{"title":"Reinforcement Learning Applied to Cognitive Space Communications","authors":"Carson D. Schubert, Rigoberto Roche', J. Briones","doi":"10.1109/CCAAW.2019.8904912","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904912","url":null,"abstract":"The future of space exploration depends on robust, reliable communication systems. As the number of such communication systems increase, automation is fast becoming a requirement to achieve this goal. A reinforcement learning solution can be employed as a possible automation method for such systems. The goal of this study is to build a reinforcement learning algorithm which optimizes data throughput of a single actor. A training environment was created to simulate a link within the NASA Space Communication and Navigation (SCaN) infrastructure, using state of the art simulation tools developed by the SCaN Center for Engineering, Networks, Integration, and Communications (SCENIC) laboratory at NASA Glenn Research Center to obtain the closest possible representation of the real operating environment. Reinforcement learning was then used to train an agent inside this environment to maximize data throughput. The simulation environment contained a single actor in low earth orbit capable of communicating with twenty-five ground stations that compose the Near-Earth Network (NEN). Initial experiments showed promising training results, so additional complexity was added by augmenting simulation data with link fading profiles obtained from real communication events with the International Space Station. A grid search was performed to find the optimal hyperparameters and model architecture for the agent. Using the results of the grid search, an agent was trained on the augmented training data. Testing shows that the agent performs well inside the training environment and can be used as a foundation for future studies with added complexity and eventually tested in the real space environment.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127722936","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}

引用次数: 0

Testing a Neural Network Accelerator on a High-Altitude Balloon 在高空气球上测试神经网络加速器

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904886

G. Clark, G. Landis, Ethan Barnes, Blake LaFuente, Kristina Collins

{"title":"Testing a Neural Network Accelerator on a High-Altitude Balloon","authors":"G. Clark, G. Landis, Ethan Barnes, Blake LaFuente, Kristina Collins","doi":"10.1109/CCAAW.2019.8904886","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904886","url":null,"abstract":"The cognitive communications project has been working to refine artificial intelligence and machine learning approaches to support their deployment and sustained use in space environments. It has historically been difficult to implement such techniques on space platforms, however, due to the computational requirements they levy onto general-purpose avionics hardware. While technologies exist to accelerate the computation of aspects of neural networks, such platforms have not historically been deployed in space environments. Given that testing payloads in such environments can be both cost-and time-prohibitive, high-altitude balloons can be used as a way to approximate a space environment at a much lower cost, thus providing a cost-effective way in which to test newer approaches to hardware acceleration for artificial intelligence which may be deployed onto spacecraft more directly. This paper describes a successful test of a commercial off-the-shelf neural network accelerator on a high-altitude balloon. It begins by explaining our selection criteria when evaluating different commercial neural network acceleration techniques: primary considerations include size, weight, and power (SWaP) as well as ease of integration. Next, the paper describes the development and implementation of an experimental flight test platform: flight and ground components are discussed. Afterward, the paper discusses the experimental payload itself: this includes the experimental procedure as well as the specific image and method used for testing. Finally, the paper concludes with an evaluation of both the experimental device tested at altitude as well as the flight test framework itself, identifying how the existing platform can be used to continue testing commercial off-the-shelf (COTS) solutions for acceleration.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128133471","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}

引用次数: 1

Evaluation of Classifier Complexity for Delay Tolerant Network Routing 时延容忍网络路由的分类器复杂度评估

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904898

R. Dudukovich, G. Clark, C. Papachristou

{"title":"Evaluation of Classifier Complexity for Delay Tolerant Network Routing","authors":"R. Dudukovich, G. Clark, C. Papachristou","doi":"10.1109/CCAAW.2019.8904898","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904898","url":null,"abstract":"The growing popularity of small cost-effective satellites (SmallSats, CubeSats, etc.) creates the potential for a variety of new science applications involving multiple nodes functioning together to achieve a task, such as swarms and constellations. As this technology develops and is deployed for missions in Low Earth Orbit and beyond, the use of delay tolerant networking (DTN) techniques may improve communication capabilities within the network. In this paper, a network hierarchy is developed from heterogeneous networks of SmallSats, surface vehicles, relay satellites and ground stations which form an integrated network. There is a trade-off between complexity, flexibility, and scalability of user defined schedules versus autonomous routing as the number of nodes in the network increases. To address these issues, this work proposes a machine learning classifier based on DTN routing metrics. A framework is developed which will allow for the use of several categories of machine learning algorithms (decision tree, random forest, and deep learning) to be applied to a dataset of historical network statistics, which allows for the evaluation of algorithm complexity versus performance to be explored. We develop the emulation of a hierarchical network, consisting of tens of nodes which form a cognitive network architecture. CORE (Common Open Research Emulator) is used to emulate the network using bundle protocol and DTN IP neighbor discovery.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786459","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}

引用次数: 6

Cognitive Scheduling and Resource Allocation for Space to Ground Communication 地空通信的认知调度与资源分配

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904914

M. Koets, Justin Blount, Jarred Blount

{"title":"Cognitive Scheduling and Resource Allocation for Space to Ground Communication","authors":"M. Koets, Justin Blount, Jarred Blount","doi":"10.1109/CCAAW.2019.8904914","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904914","url":null,"abstract":"This paper presents a mathematical model for a cognitive communication network applicable to satellite communications with ground stations. The model employs abstract elements to describe a communications network, allowing the approach to be applied to a wide range of real-world communications systems and problems. The model includes representation of communications paths, spacecraft capabilities, time-varying demand for data transfer, changes in visibility due to satellite motion, time-varying availability of channels, and regulatory constraints on the use of radio communication bands. These model elements permit the detailed description of the structure and constraints of a communications problem. The model establishes a formal definition for a communication schedule which assigns communications resources to specific communicators at specific times. The model also formalizes constraints on the interactions between communicators, establishing the definition of a valid schedule in which communications conflicts do not occur and the definition of a good schedule in which communications resources are used efficiently. The paper also presents a dynamic reasoning methodology which uses the model to allocate communications resources in response to changing network conditions and communications loads. Implementation of the reasoning process using Answer Set Programming is demonstrated, providing illustration of the practicality of the approach. The application of the model and methodology to an example satellite communication network is presented. Using this approach significantly improved performance with respect to static resource allocation is demonstrated.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"401 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130179151","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}

引用次数: 0

A Communication Channel Density Estimating Generative Adversarial Network 一种通信信道密度估计生成对抗网络

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904907

Aaron Smith, J. Downey

{"title":"A Communication Channel Density Estimating Generative Adversarial Network","authors":"Aaron Smith, J. Downey","doi":"10.1109/CCAAW.2019.8904907","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904907","url":null,"abstract":"Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks-and more generally the autoencoder systems-should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115893086","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}

引用次数: 11

Robust Deep Reinforcement Learning for Interference Avoidance in Wideband Spectrum 基于鲁棒深度强化学习的宽带干扰避免

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904887

Mohamed A. Aref, S. Jayaweera

引用次数: 5

Smart Communications in Heterogeneous Spacecraft Networks: A Blockchain Based Secure Auction Approach 异构航天器网络中的智能通信:基于区块链的安全拍卖方法

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904902

Lixing Yu, Jinlong Ji, Y. Guo, Qianlong Wang, Tianxi Ji, P. Li

引用次数: 3

Artificial Intelligence-based Cognitive Cross-layer Decision Engine for Next-Generation Space Mission 基于人工智能的下一代航天任务认知跨层决策引擎

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904895

Anu Jagannath, Jithin Jagannath, A. Drozd

{"title":"Artificial Intelligence-based Cognitive Cross-layer Decision Engine for Next-Generation Space Mission","authors":"Anu Jagannath, Jithin Jagannath, A. Drozd","doi":"10.1109/CCAAW.2019.8904895","DOIUrl":"https://doi.org/10.1109/CCAAW.2019.8904895","url":null,"abstract":"In this position paper, the authors argue the need for a novel framework that provides flexibility, autonomy and optimizes the use of scarce resources to ensure reliable communication during next-generation space missions. To this end, the authors present the shortcomings of existing space architectures and the challenges in realizing adaptive autonomous space-networking. In this regard, the authors aim to jointly exploit the immense capabilities of deep reinforcement learning (DRL) and cross-layer optimization by proposing an artificial intelligence-based cognitive cross-layer decision engine to bolster next-generation space missions. The presented software-defined cognitive cross-layer decision engine is designed for the resource-constrained Internet-of-Space-Things. The framework is designed to be flexible to accommodate varying (with time and location) requirements of multiple space missions such as reliability, throughput, delay, energy-efficiency among others. In this work, the authors present the formulation of the cross-layer optimization for multiple mission objectives that forms the basis of the presented framework. The cross-layer optimization problem is then modeled as a Markov Decision Process to be solved using deep reinforcement learning (DRL). Subsequently, the authors elucidate the DRL model and concisely explain the deep neural network architecture to perform the DRL. This position paper concludes by providing the different phases of the evaluation plan for the proposed cognitive framework.","PeriodicalId":196580,"journal":{"name":"2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126049882","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}

引用次数: 4

Deep Reinforcement Learning for Continuous Power Allocation in Flexible High Throughput Satellites 柔性高通量卫星连续功率分配的深度强化学习

2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) Pub Date : 2019-06-01 DOI: 10.1109/CCAAW.2019.8904901

J. Luis, Markus Guerster, Iñigo Del Portillo, E. Crawley, B. Cameron

引用次数: 20