Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Lead Research Organisation:
University of Leicester
Department Name: Engineering
Abstract
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.
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.
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.
Publications
Hedges D
(2020)
A Continuum Model for Route Optimization in Large-Scale Inhomogeneous Multi-Hop Wireless Networks
in IEEE Transactions on Communications
Abdullah Z
(2020)
A Hybrid Relay and Intelligent Reflecting Surface Network and Its Ergodic Performance Analysis
in IEEE Wireless Communications Letters
Miao P
(2020)
Adaptive Nonlinear Equalization Combining Sparse Bayesian Learning and Kalman Filtering for Visible Light Communications
in Journal of Lightwave Technology
Huang C
(2021)
Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning
in IEEE Journal on Selected Areas in Communications
Alkhawatrah M
(2019)
Buffer-Aided Relay Selection for Cooperative NOMA in the Internet of Things
in IEEE Internet of Things Journal
Xu P
(2020)
Buffer-State-Based Probabilistic Relay Selection for Cooperative Networks With Delay Constraints
in IEEE Wireless Communications Letters
Cho S
(2021)
Cooperative Beamforming and Jamming for Secure VLC System in the Presence of Active and Passive Eavesdroppers
in IEEE Transactions on Green Communications and Networking
Huang C
(2021)
Deep Reinforcement Learning-Based Relay Selection in Intelligent Reflecting Surface Assisted Cooperative Networks
in IEEE Wireless Communications Letters
Huang C
(2021)
Delay-Constrained Buffer-Aided Relay Selection in the Internet of Things With Decision-Assisted Reinforcement Learning
in IEEE Internet of Things Journal
Xu P
(2021)
Design and Evaluation of Buffer-Aided Cooperative NOMA With Direct Transmission in IoT
in IEEE Internet of Things Journal
Alsifiany F
(2019)
Differential Downlink Transmission in Massive MU-MIMO Systems
in IEEE Access
Abdullah Z
(2020)
Efficient Low-Complexity Antenna Selection Algorithms in Multi-User Massive MIMO Systems With Matched Filter Precoding
in IEEE Transactions on Vehicular Technology
Tang J
(2019)
Energy Efficiency Optimization for NOMA With SWIPT
in IEEE Journal of Selected Topics in Signal Processing
Abdullah Z
(2021)
Enhanced Secrecy Performance of Multihop IoT Networks With Cooperative Hybrid-Duplex Jamming
in IEEE Transactions on Information Forensics and Security
Xu P
(2021)
Ergodic Secrecy Rate of RIS-Assisted Communication Systems in the Presence of Discrete Phase Shifts and Multiple Eavesdroppers
in IEEE Wireless Communications Letters
Pei X
(2020)
Hybrid Multicast/Unicast Design in NOMA-Based Vehicular Caching System
in IEEE Transactions on Vehicular Technology
Huang C
(2021)
Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks With Double Deep Q-Network
in IEEE Transactions on Cognitive Communications and Networking
Tang J
(2018)
Joint Coverage Enhancement by Power Allocation in Poisson Clustered Out-of-Band D2D Networks
in IEEE Transactions on Vehicular Technology
Fang S
(2021)
Joint Optimization for Secure Intelligent Reflecting Surface Assisted UAV Networks
in IEEE Wireless Communications Letters
Abdullah Z
(2022)
Low-Complexity Antenna Selection and Discrete Phase-Shifts Design in IRS-Assisted Multiuser Massive MIMO Networks
in IEEE Transactions on Vehicular Technology
Fang S
(2020)
Millimeter-Wave Coordinated Beamforming Enabled Cooperative Network: A Stochastic Geometry Approach
in IEEE Transactions on Communications
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
Huang C
(2020)
Novel deep reinforcement learning-based delay-constrained buffer-aided relay selection in cognitive cooperative networks
in Electronics Letters
Alageli M
(2020)
Optimal Downlink Transmission for Cell-Free SWIPT Massive MIMO Systems With Active Eavesdropping
in IEEE Transactions on Information Forensics and Security
Chen G
(2018)
Optimal Routing for Multihop Social-Based D2D Communications in the Internet of Things
in IEEE Internet of Things Journal
Abdullah Z
(2021)
Optimization of Intelligent Reflecting Surface Assisted Full-Duplex Relay Networks
in IEEE Wireless Communications Letters
Ge L
(2020)
Performance Analysis for Multihop Cognitive Radio Networks With Energy Harvesting by Using Stochastic Geometry
in IEEE Internet of Things Journal
Chen G
(2019)
Performance Analysis for Multihop Full-Duplex IoT Networks Subject to Poisson Distributed Interferers
in IEEE Internet of Things Journal
Xu P
(2019)
Performance Analysis of Buffer-Aided Hybrid NOMA/OMA in Cooperative Uplink System
in IEEE Access
Abdullah M
(2019)
Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection
in IEEE Access
Yang Z
(2021)
Performance Study of Cognitive Relay NOMA Networks With Dynamic Power Transmission
in IEEE Transactions on Vehicular Technology
Xu P
(2021)
Reconfigurable Intelligent Surfaces-Assisted Communications With Discrete Phase Shifts: How Many Quantization Levels Are Required to Achieve Full Diversity?
in IEEE Wireless Communications Letters
Jiang N
(2019)
Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
in IEEE Journal on Selected Areas in Communications
Chen G
(2018)
Secure Routing for Multihop Ad Hoc Networks With Inhomogeneous Eavesdropper Clusters
in IEEE Transactions on Vehicular Technology
Li X
(2021)
Sum-Rate Maximization in IRS-Assisted Wireless Power Communication Networks
in IEEE Internet of Things Journal
Alageli M
(2019)
SWIPT Massive MIMO Systems With Active Eavesdropping
in IEEE Journal on Selected Areas in Communications
Khan A
(2020)
Trusted UAV Network Coverage Using Blockchain, Machine Learning, and Auction Mechanisms
in IEEE Access
| Description | The project led to the publication of a large number of high-quality international conference and journal papers. State-of-the-art technologies were thoroughly investigated in this project, and novel network architectures and algorithms were designed. |
| Exploitation Route | The proposed network architectures and algorithms designed during this period of this project will add a large value to the research community. The novel results obtained through this project were disseminated through international conference and journal papers. |
| Sectors | Digital/Communication/Information Technologies (including Software) |
| Description | In terms of impact on research community, the disseminated research outcome through international conference and journal papers is attracting much interest from researchers around the globe, with many articles having large citations within a short period from their publication date. In addition, the PDRA (Dr. Zaid Abdullah) who worked on this project at the University of Leicester between 2018 and 2021, has secured a job position in a world-leading international lab at the University of Luxembourg. The project at the University of Leicester has helped the PDRA in establishing strong international collaborations, and also enhanced the profile and CV of the PDRA. |
| Sector | Digital/Communication/Information Technologies (including Software) |