Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry Article Swipe
Shobha Sundar Ram
,
Gourab Ghatak
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.03221
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.03221
Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite-based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular search space and identify mobile users/radar targets. Subsequently, directional communication links are established with these mobile users. Consequently, JRC parameters such as the time trade-off between the radar exploration and communication service tasks have direct implications on the network throughput. Using tools from stochastic geometry (SG), we derive several system design and planning insights for deploying such networks and demonstrate how efficient radar detection can augment the communication throughput in a JRC system. Specifically, we provide a generalized analytical framework to maximize the network throughput by optimizing JRC parameters such as the exploration/exploitation duty cycle, the radar bandwidth, the transmit power, and the pulse repetition interval. The analysis is further extended to monostatic radar conditions, which is a special case in our framework. The theoretical results are experimentally validated through Monte Carlo simulations. Our analysis highlights that for a larger bistatic range, lower operating bandwidth and a higher duty cycle must be employed to maximize the network throughput. Furthermore, we demonstrate how a reduced success in radar detection due to higher clutter density deteriorates the overall network throughput. Finally, we show peak reliability of 70% of the JRC link metrics for a single bistatic transceiver configuration.
Related Topics
Concepts
Computer science
Bistatic radar
Radar
Throughput
Duty cycle
Real-time computing
Radar engineering details
Bandwidth (computing)
Remote sensing
Computer network
Electronic engineering
Radar imaging
Wireless
Telecommunications
Engineering
Geography
Electrical engineering
Voltage
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.03221
- https://arxiv.org/pdf/2201.03221
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221157807
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221157807Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2201.03221Digital Object Identifier
- Title
-
Optimization of Network Throughput of Joint Radar Communication System Using Stochastic GeometryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-10Full publication date if available
- Authors
-
Shobha Sundar Ram, Gourab GhatakList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.03221Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.03221Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2201.03221Direct OA link when available
- Concepts
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Computer science, Bistatic radar, Radar, Throughput, Duty cycle, Real-time computing, Radar engineering details, Bandwidth (computing), Remote sensing, Computer network, Electronic engineering, Radar imaging, Wireless, Telecommunications, Engineering, Geography, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.systems | 5 |
| abstract_inverted_index.through | 170 |
| abstract_inverted_index.Finally, | 219 |
| abstract_inverted_index.Recently | 0 |
| abstract_inverted_index.activity | 20 |
| abstract_inverted_index.analysis | 148, 175 |
| abstract_inverted_index.bistatic | 181, 234 |
| abstract_inverted_index.employed | 193 |
| abstract_inverted_index.extended | 151 |
| abstract_inverted_index.geometry | 85 |
| abstract_inverted_index.identify | 44 |
| abstract_inverted_index.insights | 94 |
| abstract_inverted_index.interest | 9 |
| abstract_inverted_index.maximize | 122, 195 |
| abstract_inverted_index.military | 23 |
| abstract_inverted_index.networks | 98 |
| abstract_inverted_index.planning | 93 |
| abstract_inverted_index.sensing. | 28 |
| abstract_inverted_index.targets. | 47 |
| abstract_inverted_index.transmit | 140 |
| abstract_inverted_index.bandwidth | 185 |
| abstract_inverted_index.deploying | 96 |
| abstract_inverted_index.detection | 104, 208 |
| abstract_inverted_index.efficient | 102 |
| abstract_inverted_index.framework | 120 |
| abstract_inverted_index.interval. | 146 |
| abstract_inverted_index.operating | 184 |
| abstract_inverted_index.trade-off | 65 |
| abstract_inverted_index.validated | 169 |
| abstract_inverted_index.vehicular | 15 |
| abstract_inverted_index.analytical | 119 |
| abstract_inverted_index.bandwidth, | 138 |
| abstract_inverted_index.framework. | 163 |
| abstract_inverted_index.highlights | 176 |
| abstract_inverted_index.monostatic | 153 |
| abstract_inverted_index.optimizing | 127 |
| abstract_inverted_index.parameters | 60, 129 |
| abstract_inverted_index.repetition | 145 |
| abstract_inverted_index.stochastic | 84 |
| abstract_inverted_index.throughput | 109, 125 |
| abstract_inverted_index.conditions, | 155 |
| abstract_inverted_index.demonstrate | 100, 201 |
| abstract_inverted_index.deployments | 34 |
| abstract_inverted_index.directional | 36, 49 |
| abstract_inverted_index.established | 53 |
| abstract_inverted_index.exploration | 69 |
| abstract_inverted_index.frameworks, | 31 |
| abstract_inverted_index.generalized | 118 |
| abstract_inverted_index.reliability | 223 |
| abstract_inverted_index.theoretical | 165 |
| abstract_inverted_index.throughput. | 80, 198, 218 |
| abstract_inverted_index.transceiver | 235 |
| abstract_inverted_index.users/radar | 46 |
| abstract_inverted_index.Furthermore, | 199 |
| abstract_inverted_index.applications | 12 |
| abstract_inverted_index.considerable | 8 |
| abstract_inverted_index.deteriorates | 214 |
| abstract_inverted_index.implications | 76 |
| abstract_inverted_index.localization | 18 |
| abstract_inverted_index.recognition, | 21 |
| abstract_inverted_index.simulations. | 173 |
| abstract_inverted_index.Consequently, | 58 |
| abstract_inverted_index.Specifically, | 114 |
| abstract_inverted_index.Subsequently, | 48 |
| abstract_inverted_index.communication | 3, 50, 71, 108 |
| abstract_inverted_index.configuration. | 236 |
| abstract_inverted_index.experimentally | 168 |
| abstract_inverted_index.communications, | 16, 24 |
| abstract_inverted_index.satellite-based | 26 |
| abstract_inverted_index.bistatic/passive | 32 |
| abstract_inverted_index.exploration/exploitation | 133 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |