Pub Date : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653007
Shakil Ahmed, A. Kamal, Mohamed Y. Selim
Reconfigurable intelligent surface (RIS) panels with passive and active elements significantly enhance Internet of Things (IoT) systems performance by, respectively, reflecting and amplifying incident signals to receiving entities. However, RIS panel active elements consume more energy than passive elements due to the signals reflection property of passive elements and the signals reflection and amplification properties of active elements. In addition, IoT devices may require harvesting energy from radio frequency (RF) signals from a nearby base station (BS) when they do not have enough operational energy. This paper investigates a trade-off between RIS panels containing active and passive elements energy consumption and energy harvested from RF signals of a nearby BS by a power-hungry IoT device. We consider all possible links via the RIS panel between transmitting and receiving nodes. In our model, the RIS panel is powered by harvesting energy from BS RF signals. We consider a fixed-length time frame that is divided into two optimal time slots. In the first time slot, the IoT device harvests energy from the BS RF signals with the help of the RIS. Using harvested energy from the BS RF signal, the IoT device transmits bits to the BS in the second time slot, also with the help of the RIS. We achieve the optimal number of RIS active and passive elements, therefore, reducing the RIS energy consumption for both time slots subject to RF energy harvesting and bits transmission. An optimization problem is formulated as a non-convex mixed-integer nonlinear problem. We propose a robust iterative algorithm to solve the problem. Finally, we present results to show the improved performance of our proposed model.
{"title":"Adding Active Elements to Reconfigurable Intelligent Surfaces to Enhance Energy Harvesting for IoT Devices","authors":"Shakil Ahmed, A. Kamal, Mohamed Y. Selim","doi":"10.1109/MILCOM52596.2021.9653007","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653007","url":null,"abstract":"Reconfigurable intelligent surface (RIS) panels with passive and active elements significantly enhance Internet of Things (IoT) systems performance by, respectively, reflecting and amplifying incident signals to receiving entities. However, RIS panel active elements consume more energy than passive elements due to the signals reflection property of passive elements and the signals reflection and amplification properties of active elements. In addition, IoT devices may require harvesting energy from radio frequency (RF) signals from a nearby base station (BS) when they do not have enough operational energy. This paper investigates a trade-off between RIS panels containing active and passive elements energy consumption and energy harvested from RF signals of a nearby BS by a power-hungry IoT device. We consider all possible links via the RIS panel between transmitting and receiving nodes. In our model, the RIS panel is powered by harvesting energy from BS RF signals. We consider a fixed-length time frame that is divided into two optimal time slots. In the first time slot, the IoT device harvests energy from the BS RF signals with the help of the RIS. Using harvested energy from the BS RF signal, the IoT device transmits bits to the BS in the second time slot, also with the help of the RIS. We achieve the optimal number of RIS active and passive elements, therefore, reducing the RIS energy consumption for both time slots subject to RF energy harvesting and bits transmission. An optimization problem is formulated as a non-convex mixed-integer nonlinear problem. We propose a robust iterative algorithm to solve the problem. Finally, we present results to show the improved performance of our proposed model.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860287","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9652987
William H. Clark, Alan J. Michaels
Given the significance of data within machine learning systems, quantifying how the quality of the available data affects the final performance is a vital component in development. Examining the relationship between a dataset's quantity and the trained system's performance by parametrically varying the available amount of data, new insights can be learned and used to answer questions more efficiently. Having a metric of quality will better enable the developer to ask questions about what one dataset is considering within it and how it improves or hurts the performance of the trained network, further allowing a deeper investigation and understanding of the unknowns that must be considered by the system. This work establishes the approach to regress the relationship between data quantity and system performance in a way that enables a quantitative comparison of quality for different datasets against a known good test set. Further, this approach allows for an impartial means of comparing the value of data, generated or otherwise acquired, toward the end system's final performance.
{"title":"Quantifying Dataset Quality in Radio Frequency Machine Learning","authors":"William H. Clark, Alan J. Michaels","doi":"10.1109/MILCOM52596.2021.9652987","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9652987","url":null,"abstract":"Given the significance of data within machine learning systems, quantifying how the quality of the available data affects the final performance is a vital component in development. Examining the relationship between a dataset's quantity and the trained system's performance by parametrically varying the available amount of data, new insights can be learned and used to answer questions more efficiently. Having a metric of quality will better enable the developer to ask questions about what one dataset is considering within it and how it improves or hurts the performance of the trained network, further allowing a deeper investigation and understanding of the unknowns that must be considered by the system. This work establishes the approach to regress the relationship between data quantity and system performance in a way that enables a quantitative comparison of quality for different datasets against a known good test set. Further, this approach allows for an impartial means of comparing the value of data, generated or otherwise acquired, toward the end system's final performance.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125966647","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9652982
Sunitha Safavat, D. Rawat
Unmanned Aerial Vehicles (UAVs) in Flying Ad-Hoc Networks (FANET) are increasing rapidly and being employed for many civilian and military applications. It is crucial to authenticate UAVs' identities before they begin to communicate with each other. However, the traditional authentication methods based on a dynamic key or username/password encompass a low secure ability. The other certification method needs a large session key that cannot meet the requirement of lightweight authentication in the FANET. In this paper, we propose a modified Elliptic Curve Cryptography (ECC) based lightweight identity authentication method which has two main steps: i) the Certificate Authority (CA) which maps UAV's unique identifier information with cryptographic keys using the ECC algorithm; ii) detection of malicious UAV (MUAV) using received periodic status information of UAVs. These steps make sure no malicious UAVs present in the FANET. We compared the proposed approach with a traditional authentication method in FANET and noticed that the proposed approach provides a shorter key and lower computing utilization. Considering the security, this approach addresses the malicious UAV attack issues that can ensure the UAV identity authentication secure, and only legitimate UAVs can participate in communications. We evaluate the performance of our proposed approach using numerical results and found that our approach outperforms the other related approaches.
{"title":"Securing Unmanned Aerial Vehicular Networks Using Modified Elliptic Curve Cryptography","authors":"Sunitha Safavat, D. Rawat","doi":"10.1109/MILCOM52596.2021.9652982","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9652982","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) in Flying Ad-Hoc Networks (FANET) are increasing rapidly and being employed for many civilian and military applications. It is crucial to authenticate UAVs' identities before they begin to communicate with each other. However, the traditional authentication methods based on a dynamic key or username/password encompass a low secure ability. The other certification method needs a large session key that cannot meet the requirement of lightweight authentication in the FANET. In this paper, we propose a modified Elliptic Curve Cryptography (ECC) based lightweight identity authentication method which has two main steps: i) the Certificate Authority (CA) which maps UAV's unique identifier information with cryptographic keys using the ECC algorithm; ii) detection of malicious UAV (MUAV) using received periodic status information of UAVs. These steps make sure no malicious UAVs present in the FANET. We compared the proposed approach with a traditional authentication method in FANET and noticed that the proposed approach provides a shorter key and lower computing utilization. Considering the security, this approach addresses the malicious UAV attack issues that can ensure the UAV identity authentication secure, and only legitimate UAVs can participate in communications. We evaluate the performance of our proposed approach using numerical results and found that our approach outperforms the other related approaches.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126558277","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653112
J. Jiménez, Hope Hong, Patrick Seipel
Effective spectrum awareness is critical to a large number of wireless communication systems. Malicious actors increasingly use the spectrum for their own purposes, such as to disrupt systems via jamming and/or spoofing. Radio anomaly detection approaches have been leveraged somewhat in wireless sensor networks, but most of these prior works have focused on detecting changes in sensor data (e.g., temperature and pressure), or in expert features rather than on anomalies occurring in the physical layer. This paper is focused on the detection of anomalous Zigbee transmissions using features extracted from the in-phase and quadrature components and network traffic data. We evaluated the performance of five supervised machine learning algorithms (i.e., Random Forest, J48, JRip, Naive Bayes, and PART) for anomalous RF detection and identified the best learner. Furthermore, we experimented with training sets of different sizes. The main findings include: (1) Adding network flow-based features improved the performance of most of the supervised machine learning algorithms for the detection of anomalous Zigbee transmissions; (2) Random Forest was the best performing learner with the highest F-score and G-score values when using feature-level fusion; and (3) The learners performed similarly across the different training set sizes for all supervised machine learning algorithms.
{"title":"Detection of Anomalous Zigbee Transmissions Using Machine Learning","authors":"J. Jiménez, Hope Hong, Patrick Seipel","doi":"10.1109/MILCOM52596.2021.9653112","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653112","url":null,"abstract":"Effective spectrum awareness is critical to a large number of wireless communication systems. Malicious actors increasingly use the spectrum for their own purposes, such as to disrupt systems via jamming and/or spoofing. Radio anomaly detection approaches have been leveraged somewhat in wireless sensor networks, but most of these prior works have focused on detecting changes in sensor data (e.g., temperature and pressure), or in expert features rather than on anomalies occurring in the physical layer. This paper is focused on the detection of anomalous Zigbee transmissions using features extracted from the in-phase and quadrature components and network traffic data. We evaluated the performance of five supervised machine learning algorithms (i.e., Random Forest, J48, JRip, Naive Bayes, and PART) for anomalous RF detection and identified the best learner. Furthermore, we experimented with training sets of different sizes. The main findings include: (1) Adding network flow-based features improved the performance of most of the supervised machine learning algorithms for the detection of anomalous Zigbee transmissions; (2) Random Forest was the best performing learner with the highest F-score and G-score values when using feature-level fusion; and (3) The learners performed similarly across the different training set sizes for all supervised machine learning algorithms.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050718","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653130
A. F. M. S. Haq, Murat Yuksel
Free-space optical communication presents a significant opportunity for next generation wireless communication and networking with high modulation speed, broad bandwidth, secure and direct line-of-sight link, and unlicensed spectrum. Multielement free-space optical transceivers can be used to improve the overall optical link performance as they offer spatial reuse, beam steering, and tolerance to mobility. In this paper, we explore the design and analysis of a fixed effective focal length lens system and the optical coupling efficiency that can be maximized by defocusing the beam footprint on the receiver side for a full-duplex free-space optical communication link. We propose a lens system with effective focal length of 49.5 mm, F/2, and field-of-view of 28° to collimate the transmit beam onto the receiver plane. We further present how to maximize the optical coupling efficiency and vibration tolerance by introducing small defocusing length between transmitter and lens assembly.
自由空间光通信为下一代无线通信和网络提供了重要的机会，具有高调制速度、宽带宽、安全和直接视距链路以及免许可频谱。多元件自由空间光收发器可用于改善整体光链路性能，因为它们提供空间重用、波束导向和对移动的容忍。本文探讨了一种全双工自由空间光通信链路的固定有效焦距透镜系统的设计和分析，以及通过在接收端散焦光束足迹来最大化光耦合效率。我们提出了一个有效焦距为49.5 mm, F/2，视场为28°的透镜系统，用于将发射光束对准接收平面。我们进一步介绍了如何通过在发射器和透镜组件之间引入较小的离焦长度来最大化光耦合效率和振动容忍度。
{"title":"Defocal Lens Assembly for Multi-Element Full-Duplex Free Space Optical Transceiver","authors":"A. F. M. S. Haq, Murat Yuksel","doi":"10.1109/MILCOM52596.2021.9653130","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653130","url":null,"abstract":"Free-space optical communication presents a significant opportunity for next generation wireless communication and networking with high modulation speed, broad bandwidth, secure and direct line-of-sight link, and unlicensed spectrum. Multielement free-space optical transceivers can be used to improve the overall optical link performance as they offer spatial reuse, beam steering, and tolerance to mobility. In this paper, we explore the design and analysis of a fixed effective focal length lens system and the optical coupling efficiency that can be maximized by defocusing the beam footprint on the receiver side for a full-duplex free-space optical communication link. We propose a lens system with effective focal length of 49.5 mm, F/2, and field-of-view of 28° to collimate the transmit beam onto the receiver plane. We further present how to maximize the optical coupling efficiency and vibration tolerance by introducing small defocusing length between transmitter and lens assembly.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122145","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653034
Michelle Sherman, Sihua Shao, Xiang Sun, Jun Zheng
Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the $epsilon$-greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs.
{"title":"UAV Assisted Cellular Networks With Renewable Energy Charging Infrastructure: A Reinforcement Learning Approach","authors":"Michelle Sherman, Sihua Shao, Xiang Sun, Jun Zheng","doi":"10.1109/MILCOM52596.2021.9653034","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653034","url":null,"abstract":"Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the $epsilon$-greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132590411","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653019
John J. Kelly, Daniel L. Stevens
As the number of radio frequency (RF) systems in use continues to increase, the need to monitor and securely share limited spectrum continues to correspondingly grow. Tracking and analyzing spectrum usage over time is pivotal to secure dynamic spectrum sharing. This paper presents a novel unsupervised, information-based approach to identifying and characterizing the complexity and quality of an RF signal's time-frequency (TF) characteristics. The proposed method draws on tools from information geometry and utilizes the set of correlation matrices. In particular, the informativeness is a recently developed measure of the homogeneity of a data set. The informativeness provides a two-parameter characterization of multi-dimensional data that can be used to assess TF grids for homogeneity. This intrinsic consistency can be used to assess the quality or complexity of recorded data at a single sensor, and to assess consistency between pairs of sensor network nodes.
{"title":"Novel RF Spectrum Characterization Using Information Measures","authors":"John J. Kelly, Daniel L. Stevens","doi":"10.1109/MILCOM52596.2021.9653019","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653019","url":null,"abstract":"As the number of radio frequency (RF) systems in use continues to increase, the need to monitor and securely share limited spectrum continues to correspondingly grow. Tracking and analyzing spectrum usage over time is pivotal to secure dynamic spectrum sharing. This paper presents a novel unsupervised, information-based approach to identifying and characterizing the complexity and quality of an RF signal's time-frequency (TF) characteristics. The proposed method draws on tools from information geometry and utilizes the set of correlation matrices. In particular, the informativeness is a recently developed measure of the homogeneity of a data set. The informativeness provides a two-parameter characterization of multi-dimensional data that can be used to assess TF grids for homogeneity. This intrinsic consistency can be used to assess the quality or complexity of recorded data at a single sensor, and to assess consistency between pairs of sensor network nodes.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115747484","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9652944
L. Nguyen, D. Nguyen, N. Tran, Clayton Bosler, David Brunnenmeyer
This paper presents a new framework for SATCOM jamming resiliency in the presence of a smart adversary jammer that can prioritize specific channels to attack with a non-uniform probability of distribution. We first develop a model and a defense action strategy based on a Markov decision process (MDP). We propose a greedy algorithm for the MDP-based defense algorithm's policy to optimize the expected user's immediate and future discounted rewards. Next, we remove the assumption that the user has specific information about the attacker's pattern and model. We develop a Q-learning algorithm-a reinforcement learning (RL) approach-to optimize the user's policy. We show that the Q-learning method provides an attractive defense strategy solution without explicit knowledge of the jammer's strategy. Computer simulation results show that the MDP-based defense strategies are very efficient; they offer a significant data rate advantage over the simple random hopping approach. Also, the proposed Q-learning performance can achieve close to the MDP approach without explicit knowledge of the jammer's strategy or attacking model.
{"title":"SATCOM Jamming Resiliency under Non-Uniform Probability of Attacks","authors":"L. Nguyen, D. Nguyen, N. Tran, Clayton Bosler, David Brunnenmeyer","doi":"10.1109/MILCOM52596.2021.9652944","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9652944","url":null,"abstract":"This paper presents a new framework for SATCOM jamming resiliency in the presence of a smart adversary jammer that can prioritize specific channels to attack with a non-uniform probability of distribution. We first develop a model and a defense action strategy based on a Markov decision process (MDP). We propose a greedy algorithm for the MDP-based defense algorithm's policy to optimize the expected user's immediate and future discounted rewards. Next, we remove the assumption that the user has specific information about the attacker's pattern and model. We develop a Q-learning algorithm-a reinforcement learning (RL) approach-to optimize the user's policy. We show that the Q-learning method provides an attractive defense strategy solution without explicit knowledge of the jammer's strategy. Computer simulation results show that the MDP-based defense strategies are very efficient; they offer a significant data rate advantage over the simple random hopping approach. Also, the proposed Q-learning performance can achieve close to the MDP approach without explicit knowledge of the jammer's strategy or attacking model.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123080178","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9653103
Bertram Schütz, Stefanie Thieme, Christoph Fuchs, Daniel Weber, N. Aschenbruck
This paper formalizes and evaluates a promising technique to overcome packet loss in tactical scenarios, called Network Coding-based Multi-Path Forward Erasure Correction (CoMPEC). Thereby, encoded redundancy packets are sent over a secondary path to correct packet loss on the main path without the usage of feedback or retransmissions. Formal equations are presented to calculate the benefits in terms of packet loss rate after decoding and coding gain. To evaluate the potential for tactical scenarios, a simulation was conducted, which is based on the Anglova path loss data. The presented evaluation verifies CoMPEC's ability to significantly reduce the packet loss rate at the receiver, if the scheme is applicable.
{"title":"Network Coding-based Multi-Path Forward Erasure Correction for Tactical Scenarios","authors":"Bertram Schütz, Stefanie Thieme, Christoph Fuchs, Daniel Weber, N. Aschenbruck","doi":"10.1109/MILCOM52596.2021.9653103","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653103","url":null,"abstract":"This paper formalizes and evaluates a promising technique to overcome packet loss in tactical scenarios, called Network Coding-based Multi-Path Forward Erasure Correction (CoMPEC). Thereby, encoded redundancy packets are sent over a secondary path to correct packet loss on the main path without the usage of feedback or retransmissions. Formal equations are presented to calculate the benefits in terms of packet loss rate after decoding and coding gain. To evaluate the potential for tactical scenarios, a simulation was conducted, which is based on the Anglova path loss data. The presented evaluation verifies CoMPEC's ability to significantly reduce the packet loss rate at the receiver, if the scheme is applicable.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122083182","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 : 2021-11-29DOI: 10.1109/MILCOM52596.2021.9652946
C. A. Gómez-Vega, Flavio Morselli, M. Win, A. Conti
Location awareness is essential for numerous civil, industrial, and military applications. The efficient design and operation of location-aware networks may benefit from models that describe the quality of the range information (RI) according to the properties of the transmitted signal and wireless environment. This paper presents a statistical model for the RI as a function of transmitting resources, nodes deployment, and wireless environment with application to ultra-wideband localization. Case studies based on IEEE 802.15.4a and IEEE 802.15.4z standards are presented to validate the proposed model for the RI.
{"title":"A Statistical Range Information Model with Application to UWB Localization","authors":"C. A. Gómez-Vega, Flavio Morselli, M. Win, A. Conti","doi":"10.1109/MILCOM52596.2021.9652946","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9652946","url":null,"abstract":"Location awareness is essential for numerous civil, industrial, and military applications. The efficient design and operation of location-aware networks may benefit from models that describe the quality of the range information (RI) according to the properties of the transmitted signal and wireless environment. This paper presents a statistical model for the RI as a function of transmitting resources, nodes deployment, and wireless environment with application to ultra-wideband localization. Case studies based on IEEE 802.15.4a and IEEE 802.15.4z standards are presented to validate the proposed model for the RI.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121082748","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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