C. Barz, Eelco Cramer, Roberto Fronteddu, M. Hauge, K. Marcus, J. Nilsson, Filippo Poltronieri, M. Tortonesi, Niranjan Suri, Mattia Zaccarini
{"title":"Enabling Adaptive Communications at the Tactical Edge","authors":"C. Barz, Eelco Cramer, Roberto Fronteddu, M. Hauge, K. Marcus, J. Nilsson, Filippo Poltronieri, M. Tortonesi, Niranjan Suri, Mattia Zaccarini","doi":"10.1109/MILCOM55135.2022.10017459","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017459","url":null,"abstract":"One common characteristic of all tactical networks is the challenging and varied environment they have to operate in, resulting in vastly different performance characteristics for these networks. A wide variety of networking technologies may be deployed, and the overall network could be composed of many different heterogeneous elements. Thus, a flexible communications infrastructure that is composed of all these elements is necessary. Such a complex heterogeneous tactical network infrastructure requires new innovative solutions in terms of smart management and adaptive control, as well as interaction between the different networking levels and indeed the applications. Complex heterogeneous mobile tactical networks need to leverage new technologies (such as UAV networks, future mobile core networks, SDN and 5G networks) in combination with traditional military networking technologies. The adaptive control and optimization also need to extend to and consider other infrastructural services such as storage and processing. Furthermore, novel solutions to link network adaptations with analytics to enable the network to maximize 'information-flows' vs 'bit-flows' also requires investigation. This would include the joint orchestration of network, processing, and Artificial Intelligence (AI) to improve information service capacity, resilience, and speed and accuracy of decision-making. This paper describes the vision and objectives for the NATO IST-194 Research Task Group on adaptive communications at the tactical edge.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"46 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130972278","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}
Xiang Cheng, Hanchao Yang, D. Jakubisin, N. Tripathi, G. Anderson, A. Wang, Y. Yang, Jeffrey H. Reed
{"title":"5G Physical Layer Resiliency Enhancements with NB-IoT Use Case Study","authors":"Xiang Cheng, Hanchao Yang, D. Jakubisin, N. Tripathi, G. Anderson, A. Wang, Y. Yang, Jeffrey H. Reed","doi":"10.1109/MILCOM55135.2022.10017487","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017487","url":null,"abstract":"5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108139","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}
{"title":"Adversarial Sybil attacks against Deep RL based drone trajectory planning","authors":"Adhitya Bantwal Bhandarkar, S. Jayaweera, S. Lane","doi":"10.1109/MILCOM55135.2022.10017870","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017870","url":null,"abstract":"A deep reinforcement learning (DRL) approach is proposed for simultaneously maximizing distinct user coverage and data throughput in the presence of Sybil attacks. Adversaries are assumed to be capable of PHY layer spoofing and their own reinforcement learning (RL) to cause disruption to the drone network. The distribution of users is modelled as a Gaussian Mixture Model (GMM) with a fixed mean to model a stationary user population and a time varying mean to model movement of users. The drone communication is assumed to be over the millimeter-Wave (mmWave) frequency band and the presence of users are determined based on the beacon signals transmitted by them. Adversaries are assumed to attack the drone network by transmitting spoofed beacon signals. The simulation results indicate that DRL based approaches for UAV path planning is able to cover a significant number of users after a few training epochs. They also indicate that the presence of spoofed transmissions by the adversaries results in 10 to 22 percent reduction in the amount of data handled and the number of distinct users covered.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808705","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}
{"title":"An Intelligent and Private 6G Air Interface Using Physical Layer Security","authors":"B. Kelley, Israt Ara","doi":"10.1109/MILCOM55135.2022.10017638","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017638","url":null,"abstract":"This paper lays the foundations for a fundamentally new style of intelligent 6G security that combines deep learning AI/ML with Physical Layer Security. The joint combination of AI and Physical Layer Security is proposed as natively secure privacy layer that conceals sensitive information exchange across the 6G air interface. The framework supports a zero trust model of security for air interface signals connecting the 6G Access Node (AN) to mobile devices. Simulation results for intelligent Physical Layer Security are shown to outperform linear detection models in Rayleigh fading and noise for multiple MIMO antenna sizes and across codebook modulation order.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132299715","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}
Zheng Wang, Lingjia Liu, Y. Yi, R. Calderbank, Jianzhong Zhang
{"title":"Low-Complexity Channel Matrix Calculation for OTFS Systems with Fractional Delay and Doppler","authors":"Zheng Wang, Lingjia Liu, Y. Yi, R. Calderbank, Jianzhong Zhang","doi":"10.1109/MILCOM55135.2022.10017980","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017980","url":null,"abstract":"Orthogonal Time Frequency Space (OTFS) modulation has been introduced to manage channel induced high Doppler shifts in mobile communication networks. In an OTFS system, the information carrying symbols are placed in the data frames in the delay-Doppler (DD) domain before transformed into the time domain for transmission. Accordingly, the DD domain channel matrix can be estimated using the corresponding pilots in the DD domain. For OTFS systems with integer delay and Doppler values, the underlying channel matrix in the DD domain is sparse. However, the counterpart channel matrix is no longer sparse when the delay and Doppler values are fractional. In fact, even for the integer case calculating the sparse channel matrix is still time-consuming due to its large size. To significantly reduce the computational complexity, in this paper, we introduce a low complexity algorithm for calculating the channel matrix of OTFS systems with fractional delay and Doppler. The introduced algorithm leverages the circulant property of the underlying channel matrix in the DD domain and calculates a small portion of elements in one initial block. Through some simple operation, the elements in the initial block can be replicated into other parts of the channel matrix. Both theoretical complexity analysis and simulation results demonstrate that our method can significantly reduce the computation complexity when computing the channel matrix. Since DD domain channel matrix is crucial for OTFS receive processing, we believe this is an important step to bring OTFS towards practical communication systems.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020657","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}
Jason E. Ellis, Travis W. Parker, J. Vandekerckhove, B. Murphy, Sidney C. Smith, A. Kott, Michael J. Weisman
{"title":"An Experimentation Infrastructure for Quantitative Measurements of Cyber Resilience","authors":"Jason E. Ellis, Travis W. Parker, J. Vandekerckhove, B. Murphy, Sidney C. Smith, A. Kott, Michael J. Weisman","doi":"10.1109/MILCOM55135.2022.10017529","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017529","url":null,"abstract":"The vulnerability of cyber-physical systems to cyber attack is well known, and the requirement to build cyber resilience into these systems has been firmly established. The key challenge this paper addresses is that maturing this discipline requires the development of techniques, tools, and processes for objectively, rigorously, and quantitatively measuring the attributes of cyber resilience. Researchers and program managers need to be able to determine if the implementation of a resilience solution actually increases the resilience of the system. In previous work, a table top exercise was conducted using a notional heavy vehicle on a fictitious military mission while under a cyber attack. While this exercise provided some useful data, more and higher fidelity data is required to refine the measurement methodology. This paper details the efforts made to construct a cost-effective experimentation infrastructure to provide such data. It also presents a case study using some of the data generated by the infrastructure.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"407 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116386704","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}
{"title":"Practical Methods for Joint Time and Carrier Synchronization in LPI/LPD Communications","authors":"Haotian Zhai, Bernd-Peter Paris","doi":"10.1109/MILCOM55135.2022.10017848","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017848","url":null,"abstract":"To operate at low SNR, LPI/LPD communication systems rely on coherent processing which requires that the receiver is precisely synchronized in time, frequency and phase. To that end, this paper proposes a family of data-aided joint frame and carrier synchronization algorithms. A sequential detection algorithm based on the generalized likelihood ratio test (GLRT) is used to detect an embedded preamble signal in the received data stream. Novel algorithms for low-complexity, coarse carrier synchronization at low SNR provide carrier estimates during sequential detection. After detection, the coarse carrier estimate is refined for use in coherent demodulation. The proposed family of algorithms can be scaled to support operation over a wide range of SNR, including SNR below 0 dB. Algorithms are validated through simulation. The practicality of our approach is demonstrated by real-time operation on a standard SDR platform with sample rates approaching 10MHz.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116496507","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}
{"title":"Toward Congestion Control in Lossy Networks via Local Queue-Management Policies","authors":"P. Forero, Peng Zhang, D. Radosevic","doi":"10.1109/MILCOM55135.2022.10017987","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017987","url":null,"abstract":"Congestion control in intermittently connected lossy networks (ICLNs) is critical to enable efficient use of network resources, and mitigate congestion-induced packet losses and delays. Unfortunately, well-known protocols for end-to-end congestion control, such as the celebrated Transmission Control Protocol (TCP), are not well-suited for use in ICLNs. This work builds on the idea of hop-by-hop flow control and dynamic buffer-space allocation to develop a congestion-control approach able to react quickly to the onset of congestion with local congestion-control actions, and propagate direct and indirect congestion-control indicators toward the traffic sources. The local flow-control policy is developed using a reinforcement learning framework that captures queue-occupancy and sojourn-time indicators, and is coupled with a dynamic buffer-space allocation policy developed based on the Markowitz portfolio selection framework. The buffer-space allocation policy is responsible for allocating unused buffer space across all active flows in the node based on the relative traffic loads. The performance of the proposed framework when handling congestion locally is explored via numerical tests on a simulated environment.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128568530","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}
{"title":"Threat framework for 5G cellular communications","authors":"M. Vanderveen","doi":"10.1109/MILCOM55135.2022.10017976","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017976","url":null,"abstract":"5G networks are rapidly gaining traction as the technology of choice for not only traditional cellular networks, but also new verticals such as business, industrial and military deployments. The complexity of 5G as a system of systems poses new security challenges. Even though 5G is generally viewed as more secure than previous generations such as 4G (LTE), 5G incorporates Internet technologies (e.g., service-based architectures, cloud services, virtualization), which increase the attack surface. In addition, the much larger number of devices connecting, and the new types of such devices (e.g., affecting human safety) also raise new security concerns. The urgency of protecting these networks is increased as 5G is incorporated into human safety critical infrastructure and military establishments. This paper introduces FiGHT-Five G Hierarchy of Threats, a framework of adversarial tactics and techniques applicable to the 5G system, similar to that of MITRE ATT&CK.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211700","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}
{"title":"Tactical Topology Optimization Methodology for Slice Aware and Reconfigurable Battlefield Networks","authors":"A. Castañares, Deepak K. Tosh","doi":"10.1109/MILCOM55135.2022.10017781","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017781","url":null,"abstract":"Current and next generation tactical battlefield networks are congested with an overwhelming number of network devices that vie for bandwidth in an effort to push video, voice, and encrypted traffic from the tactical edge back to commanders to support the execution of mission objectives. The problem of tactical network over-saturation is real [1], and is only getting worse with projected network demands by 2030 [2] and beyond. There is a critical need for automated, fast, and trustworthy network design optimization techniques to address situations where there are too many devices to add to the network, and intelligent trade-offs must be made to determine which devices are added to the network from a mission-centric point of view. In this paper we describe our contribution to solving that problem with a methodology that can be used by Warfighters in the Joint All Domain Command & Control (JADC2) construct to automatically generate an optimized tactical battlefield network. We formulate an optimization problem based upon the fractional knapsack algorithm to maximize the number of devices associated to a slice-aware tactical network under the constraints of quality of service (QoS) requirements and available network bandwidth. Our goal is to generate components for our larger tactical network optimization framework [3], and as such we present a proof of optimal network resource allocation to substantiate the algorithm's correctness. Our experiments and analysis show that this component will contribute to increased resiliency, robustness, and cyber security of JADC2 tactical networks, and is designed to be applied in use cases where Warfighter network planners can easily employ this methodology to quickly design, deploy, or reconfigure tactical networks.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133035874","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}