Institute of Communications and Connected Systems


LeanCom: Deep Learning based solutions for the Physical Layer of Machine Type Communications

Network vector concept, with inlay showing icons resembling IoT network

1 September 2019

Designing physical layer solutions for wireless communications based on deep learning

Funder EPSRC
Amount £ 858 612

Project website gow.epsrc.ukri.org

Research theme logos - Intelligent High Capacity Networks, Ubiquitous Connectivity, Infrastructures for Smart Services and Applications
Research topics Deep Learning | Artificial Intelligence | Machine Type Communications


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:

  1. a communications specific DL framework,
  2. DL-inspired PHY solutions and,
  3. 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 of the proposed solutions serve as a platform towards commercialisation of the results of LeanCom, aiming towards an impact of a foundational nature for the UK's digital economy.



Publications will be updated during the project - the most recent publications will be available within the Principal Investigators listed publications.