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Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
基于通信信号处理的大规模机器对机器网络 (M3NET) 解决方案
批准号:
EP/R006377/1
负责人:
Jonathon Chambers
金额:
$35.11万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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项目成果

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中文摘要
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英文摘要
Future wireless networks should have the capability of serving a wide range of personal wireless devices and appliances with stringent end-to-end delay requirements. These appliances will be equipped with the capabilities to sense various real time events and be able to self-configure via network connections, thereby paving the way for many emerging applications including e-health, intelligent transportation and smart cities. The most important enabling technologies for these applications is seamless machine-to-machine (M2M) wireless communications, which is the key to sustaining large-scale massive interconnections between things. The number of M2M devices has been growing exponentially, and is expected to reach up to 50 billion by 2020. This trend in the market growth for both M2M devices and M2M connectivity segments will further accelerate in the future. Along with it, by 2020, M2M connections will generate 6.7 percent of total mobile traffic-up from 2.7 percent in 2015. As such, M2M communications is envisioned as one of the five disruptive technology directions for fifth generation (5G) wireless networks and beyond.Despite the importance of machine-type communications, there are many critical challenges that need to be addressed in terms of network congestion and overload due to presence of massive M2M devices with heterogeneous traffic patterns, unprecedented level of inter and intra interference among M2M and human-to-human (H2M) communications, complex resource management due to irregular traffic patterns and energy constraints. The focus of this project is on tackling these critical challenges, by advancing aspects of communications signal processing, stochastic geometry, convex optimizations and game theory. In particular, we will contribute in terms of characterising heterogeneous traffic patterns associated with massive M2M communications, development of distributed random access channel protocols, proposal of convex and game theoretic resource allocation methods and design of energy harvesting constraint based cross-layer optimisation algorithms and protocols. All the concepts and algorithms developed will be integrated and the radio link layer performance will be assessed using a simulation reference system based on LTE-Advanced standards and its evolution towards 5G. Industrial partners will be engaged throughout the project to ensure industrial relevance of our work.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/tvt.2020.2966565
发表时间: 2020-01
期刊: IEEE Transactions on Vehicular Technology
影响因子: 6.8
作者: [Zaid Abdullah;C. Tsimenidis;Gaojie Chen;M. Johnston;J. Chambers]
通讯作者: Zaid Abdullah;C. Tsimenidis;Gaojie Chen;M. Johnston;J. Chambers
DOI: 10.1109/ssp49050.2021.9513798
发表时间: 2021-07
期刊: 2021 IEEE Statistical Signal Processing Workshop (SSP)
影响因子: --
作者: [Zaid Abdullah;Gaojie Chen;J. Chambers]
通讯作者: Zaid Abdullah;Gaojie Chen;J. Chambers
DOI: 10.1109/glocomw.2018.8644158
发表时间: 2018-12
期刊: 2018 IEEE Globecom Workshops (GC Wkshps)
影响因子: --
作者: [Mohammed A. M. Abdullah;Gaojie Chen;Mahmoud Alageli;A. Ikhlef;J. Chambers]
通讯作者: Mohammed A. M. Abdullah;Gaojie Chen;Mahmoud Alageli;A. Ikhlef;J. Chambers
Cell-Edge-Aware Antenna Selection and Power Allocation in Massive MIMO Systems
大规模 MIMO 系统中的小区边缘感知天线选择和功率分配
DOI: --
发表时间: 2019
期刊:
影响因子: --
作者: [Abdullah Z]
通讯作者: Abdullah Z
8
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