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Details of Grant 

EPSRC Reference: EP/S028455/1
Title: Learning to Communicate: Deep Learning based solutions for the Physical Layer of Machine Type Communications [LeanCom]
Principal Investigator: Masouros, Dr C
Other Investigators:
Darwazeh, Professor I Rodrigues, Dr M Andreopoulos, Professor Y
Researcher Co-Investigators:
Project Partners:
CommNet2 Digital Catapult Duke University
Huawei Group NEC
Department: Electronic and Electrical Engineering
Organisation: UCL
Scheme: Standard Research
Starts: 01 September 2019 Ends: 31 August 2022 Value (£): 858,612
EPSRC Research Topic Classifications:
Artificial Intelligence RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
05 Mar 2019 EPSRC ICT Prioritisation Panel March 2019 Announced
Summary on Grant Application Form
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.

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