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ConSenT: Connected Sensing Techniques: Cooperative Radar Networks Using Joint Radar and Communication Waveforms

ConSenT: Connected Sensing Techniques: Cooperative Radar Networks Using Joint Radar and Communication Waveforms
ConSenT:互联传感技术:使用联合雷达和通信波形的协作雷达网络
批准号:
EP/Y035933/1
负责人:
Christos Masouros
金额:
$23.84万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --

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中文摘要
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英文摘要
Currently, information and communication technology (ICT) accounts for 4% of global greenhouse gas (GHG) emissions. Although EU's decarbonization planrequires 40% reduction of GHG emissions by 2030, those evoked by ICT are predicted to increase due to the growing number of mobile devices and wirelessnetwork facilities. In this context, Integrated sensing and communication (ISAC) technology has been gaining attention as a potential solution, enabling thereuse of wireless hardware for both sensing and communication functionalities. While recent progresses of ISAC open up new opportunities for wirelesssystems, co-existence and cooperation of multiple ISAC systems to realize extended wireless networks with reduced power consumption have not beenexplored to date. Therefore, research project aims to design and develop connected sensing techniques (ConSenT), which provide unique opportunities forupcoming ISAC networks. Enhanced sensing capabilities achieved by cooperatively integrating adjacent and/or distributed networks contribute to build powerefficientISAC networks. We set two objectives to deliver ConSenT in different connected sensing scenarios. Non-coherent connected sensing approach allowsmultiple ISAC nodes cooperating by exploiting multi-static reflections. Joint transceiver design of ISAC is explored to establish the foundation of networkedISACs. Also, coherent cooperation of the synchronized ISAC nodes is investigated to achieve the optimal communication and sensing metrics of the multipleISAC nodes. ConSenT approaches focus on developing energy-efficient networks by exploiting interdisciplinary methodologies, including communication andradar signal processing, transmit/receive beamforming optimization, modeling of stochastic geometry, and machine learning. The research project will result
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Learning to Communicate: Deep Learning based solutions for the Physical Layer of Machine Type Communications [LeanCom]
  • 批准号:
    EP/S028455/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $109.4万
  • 财政年份:
    2019
  • 负责人:
    Christos Masouros
  • 依托单位:
Exploiting interference for physical layer security in 5G networks [CI-PHY] (EPSRC-FNR)
  • 批准号:
    EP/R007934/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $79.78万
  • 财政年份:
    2018
  • 负责人:
    Christos Masouros
  • 依托单位:
Large Scale Antenna Systems Made Practical: Advanced Signal Processing for Compact Deployments [LSAS-SP]
  • 批准号:
    EP/M014150/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $34.03万
  • 财政年份:
    2015
  • 负责人:
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  • 依托单位:
海外基金