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

Lead Research Organisation: University of Leicester
Department Name: Engineering


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.

Planned Impact

Mobile and ubiquitous computing is one of the greatest innovations in the history of technology, as we live in an increasingly connected, automated, and globalised society of smart environments where the physical world is connected with the information world. In such an exciting era of smart environments that are extensively equipped with sensors, actuators, and computing components, unprecedented challenges are brought to the global wireless service providers and regulators. Today, in the Internet of Things (IoT), the number of connected devices is growing at an astonishing rate, resulting in a massive increase in the average number of connections per household. Particularly, the use of the Internet has evolved. Industry analysts predict that 50 billion devices will be connected to mobile networks worldwide by 2020. While the current wireless networks are mainly designed for human type communications, massive machine-to-machine (M2M) with its various device types, traffic patterns, and performance requirements, ranging low power/small data to high power/big data, will result in radically new network structures. With 2020 approaching, enhancing the existing wireless access networks for massive M2M is becoming critical. Evidently, one of the most important areas and challenges of 5G networks is to provide excellent wireless connectivity to machine-type communications.

This project is proposed at a time when massive M2M communications is envisioned to bring a fundamental shift to the design of future smart environments. As a key driver for the UK's digital economy, the potential beneficiaries of the massive M2M type communications solutions developed in this proposal are industries specializing in: 1) intelligent transportation systems, for example autonomous vehicle industries 2) physical environments embedded with smart devices of sensors, tags, and controllers, and 3) humans environments accompanied with smart devices including wearables such as healthcare and well-being monitoring devices and high definition body-to-body communications.

This project will use a number of strategies to maximize both the academic and industrial impacts of the research. This project involves three academic partners, Newcastle University, King's College London and Loughborough University, who will meet regularly to review the progress of the project and to ensure that the deliverables from each work package are fully exploited and integrated with other work packages. The guidance of the industrial partners, mobile VCE and the strategy group will help steering the research directions to have industrial relevance. The project partners will also facilitate routes for potential commercial exploitation of important outcomes and breakthroughs achieved in the project.

The project will provide wider dissemination of the results through several means. In addition to publications in international conferences and journals, a project website will be setup to describe the research and to record major findings from each work package. A workshop will be organised in the final year of the programme to exchange knowledge with other major UK and international researchers. The investigators current involvement with multi-million funded EPSRC/DSTL University Defence Research Collaboration in Signal Processing for the Networked Battlespace will also be used as a vehicle for wider dissemination of results.


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Yang Z (2021) Performance Study of Cognitive Relay NOMA Networks With Dynamic Power Transmission in IEEE Transactions on Vehicular Technology

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Tang J (2019) Energy Efficiency Optimization for NOMA With SWIPT in IEEE Journal of Selected Topics in Signal Processing

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Tang J (2019) Energy Efficiency Optimization for NOMA With SWIPT in IEEE Journal of Selected Topics in Signal Processing

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Pei X (2020) Hybrid Multicast/Unicast Design in NOMA-Based Vehicular Caching System in IEEE Transactions on Vehicular Technology

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Jiang N (2019) Reinforcement Learning for Real-Time Optimization in NB-IoT Networks in IEEE Journal on Selected Areas in Communications

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Huang C (2021) Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks in IEEE Transactions on Information Forensics and Security

Description This project is still on-going
Exploitation Route This project is still on-going
Sectors Digital/Communication/Information Technologies (including Software)

Description The methods developed in this project so far are published in international conferences and journal papers.
First Year Of Impact 2018
Sector Digital/Communication/Information Technologies (including Software)