Learning to Communicate: Deep Learning based solutions for the Physical Layer of Machine Type Communications [LeanCom]

学习通信:基于深度学习的机器类型通信物理层解决方案 [LeanCom]

基本信息

  • 批准号:
    EP/S028455/1
  • 负责人:
  • 金额:
    $ 109.4万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2019
  • 资助国家:
    英国
  • 起止时间:
    2019 至 无数据
  • 项目状态:
    已结题

项目摘要

With the advent of the Internet of Things (IoT), machine type communications (MTC), cloud computing and many other applications, the wireless network will become far more complex, while at the same time far more essential than ever before. Given the above exponential growth in both connectivity and complexity of the wireless systems and the unprecedented demands on latency, capacity, ultra-reliability and security, the network is becoming analytically intractable. Naturally, human-driven physical layer (PHY) design approaches rooted on mathematical models of communications systems and networks which drive today's network architectures are being surmounted by the sheer complexity of the emerging network paradigms. Hardware imperfections, that are inevitable with the employment of low-cost MTC sensors and transmitters, will drastically increase the volatility of the network, and theoretically driven solutions typically relying on generic and highly inaccurate models cannot address this as they are highly sub-optimal in practice. The above challenges necessitate new data-driven approaches to the design of communications systems, as opposed to traditional system-model driven designs that are becoming obsolete.Towards the diverse communication paradigms of MTC of the future, there is an urgent need to address reliable and adaptive links detached from mathematical models, and instead based on data-driven approaches. This visionary project will address these fundamental challenges by developing new Neural Netowrk architectures tailored for wireless communications, and new transceiver architectures based on data-driven training. Our research will address the development of a) a communications specific DL framework, b) DL-inspired PHY solutions and, c) proof-of-concept verification of the proposed solutions.LeanCom will be performed with Huawei, NEC Europe, Duke University, The Digital Catapult and CommNet and aspires to kick-start an innovative ecosystem for high-impact players among the infrastructure and service providers of ICT to develop and commercialize a new generation of learning-based networks. The implementation, experimentation and testing (within WP3) of the proposed solutions serves as a platform towards commercialisation of the results of LeanCom, aiming towards an impact of a foundational nature for the UK's digital economy.
随着物联网(IoT)、机器类型通信(MTC)、云计算和许多其他应用的出现,无线网络将变得更加复杂,同时也比以往任何时候都更加重要。 鉴于无线系统的连接性和复杂性的上述指数增长以及对延迟、容量、超可靠性和安全性的前所未有的需求,网络在分析上变得难以处理。自然地,基于驱动当今网络架构的通信系统和网络的数学模型的人为驱动的物理层(PHY)设计方法正被新兴网络范例的纯粹复杂性所超越。使用低成本MTC传感器和发射器不可避免的硬件缺陷将大大增加网络的波动性,并且通常依赖于通用和高度不准确模型的理论驱动的解决方案无法解决这个问题,因为它们在实践中是高度次优的。上述挑战需要新的数据驱动的方法来设计通信系统,而不是传统的系统模型驱动的设计,正变得过时。朝着未来的MTC的多样化的通信范例,迫切需要解决可靠的和自适应的链接脱离数学模型,而不是基于数据驱动的方法。这个富有远见的项目将通过开发为无线通信量身定制的新神经网络架构和基于数据驱动训练的新收发器架构来解决这些基本挑战。我们的研究将致力于a)通信特定DL框架的开发,B)DL启发的PHY解决方案,以及c)所提出的解决方案的概念验证。LeanCom将与华为,NEC欧洲,杜克大学,数字弹射器和CommNet,并渴望启动一个创新的生态系统,(c)鼓励信通技术基础设施和服务提供者中的有影响力的参与者开发新一代学习型网络并使之商业化。拟议解决方案的实施,实验和测试(在WP3内)作为LeanCom成果商业化的平台,旨在对英国数字经济的基础性影响。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Energy-Efficient Trajectory Design of a Multi-IRS Assisted Portable Access Point
  • DOI:
    10.1109/tvt.2022.3202953
  • 发表时间:
    2022-09
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    N. Babu;M. Virgili;M. Al-jarrah;Xiaoye Jing;E. Alsusa;P. Popovski;Andrew J. Forsyth;C. Masouros;C. Papadias
  • 通讯作者:
    N. Babu;M. Virgili;M. Al-jarrah;Xiaoye Jing;E. Alsusa;P. Popovski;Andrew J. Forsyth;C. Masouros;C. Papadias
A Unified Performance Framework for Integrated Sensing-Communications Based on KL-Divergence
Low-PAPR DFRC MIMO-OFDM Waveform Design for Integrated Sensing and Communications
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
  • DOI:
    10.1109/twc.2022.3219890
  • 发表时间:
    2021-11
  • 期刊:
  • 影响因子:
    10.4
  • 作者:
    Zhen Du;Fan Liu;W. Yuan;C. Masouros;Zenghui Zhang;G. Caire
  • 通讯作者:
    Zhen Du;Fan Liu;W. Yuan;C. Masouros;Zenghui Zhang;G. Caire
Energy Efficient Dual-Functional Radar-Communication: Rate-Splitting Multiple Access, Low-Resolution DACs, and RF Chain Selection
  • DOI:
    10.1109/ojcoms.2022.3183950
  • 发表时间:
    2022-02
  • 期刊:
  • 影响因子:
    7.9
  • 作者:
    Onur Dizdar;Aryan Kaushik;B. Clerckx;C. Masouros
  • 通讯作者:
    Onur Dizdar;Aryan Kaushik;B. Clerckx;C. Masouros
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Christos Masouros其他文献

Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges
集成传感与通信遇到智能传播工程:机遇与挑战
  • DOI:
    10.48550/arxiv.2402.18683
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kaitao Meng;Christos Masouros;Kai;A. Petropulu;Lajos Hanzo
  • 通讯作者:
    Lajos Hanzo
Relay Hybrid Precoding Design in Millimeter-Wave Massive MIMO Systems
毫米波大规模 MIMO 系统中的中继混合预编码设计
  • DOI:
    10.1109/tsp.2018.2799201
  • 发表时间:
    2018-04
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    薛瑄;王勇超;戴凌龙;Christos Masouros
  • 通讯作者:
    Christos Masouros
Intelligent Surface Empowered Integrated Sensing and Communication: From Coexistence to Reciprocity
智能表面赋能集成传感与通信:从共存到互惠
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kaitao Meng;Qingqing Wu;Christos Masouros;Wen Chen;Deshi Li
  • 通讯作者:
    Deshi Li
Interfering Channel Estimation in Radar-Cellular Coexistence: How Much Information Do We Need?
雷达-蜂窝共存中的干扰信道估计:我们需要多少信息?
  • DOI:
    10.1109/twc.2019.2921556
  • 发表时间:
    2018-07
  • 期刊:
  • 影响因子:
    10.4
  • 作者:
    Fan Liu;Adrian Garcia-Rodriguez;Christos Masouros;Giovanni Geraci
  • 通讯作者:
    Giovanni Geraci

Christos Masouros的其他文献

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{{ truncateString('Christos Masouros', 18)}}的其他基金

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
  • 财政年份:
    2024
  • 资助金额:
    $ 109.4万
  • 项目类别:
    Fellowship
Exploiting interference for physical layer security in 5G networks [CI-PHY] (EPSRC-FNR)
利用干扰实现 5G 网络中的物理层安全 [CI-PHY] (EPSRC-FNR)
  • 批准号:
    EP/R007934/1
  • 财政年份:
    2018
  • 资助金额:
    $ 109.4万
  • 项目类别:
    Research Grant
Large Scale Antenna Systems Made Practical: Advanced Signal Processing for Compact Deployments [LSAS-SP]
大规模天线系统变得实用:用于紧凑部署的高级信号处理 [LSAS-SP]
  • 批准号:
    EP/M014150/1
  • 财政年份:
    2015
  • 资助金额:
    $ 109.4万
  • 项目类别:
    Research Grant

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