Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Lead Research Organisation:
Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Ahsan W
(2022)
A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks
in IEEE Transactions on Wireless Communications
Ahsan W
(2021)
Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach
in IEEE Transactions on Wireless Communications
Ahsan W
(2021)
Reliable Reinforcement Learning Based NOMA Schemes for URLLC
Bai T
(2021)
Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems
in IEEE Transactions on Wireless Communications
Bai T
(2020)
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing
in IEEE Journal on Selected Areas in Communications
| Description | NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based radio access technology, which offers a range of flexible configurations for different coverage enhancement (CE) groups to provide reliable uplink connections for massive IoT devices with diverse data traffic. To optimize the number of served IoT devices, the uplink resource configurations need to be adjusted in real-time according to the dynamic traffic, this brings the challenge of how to select the configurations at the Evolved Node B (eNB) in the multiple CE groups scenario with high-dimension and interdependency. We developed deep reinforcement learning based dynamic optimization schemes for NB-IoT. The developed schemes can solve the current main problem of random access under bursty traffic condition. These findings highly useful for the next generation artificial intelligence enabled wireless systems. Further more, in Framed-ALOHA networks, assuming no prior information about the traffic model, apart from a bound on its temporal memory, we developed an online learning-based adaptive traffic load prediction method that is based on Recurrent Neural Networks (RNN) and specifically on the Long Short-Term Memory (LSTM) architecture. Along with the line of 3GPP standard (Release 16), we developed machine learning based Grant-Free Non Orthogonal Multiple Access (NOMA) with Multiple Configured-Grants for massive Ultra Reliable Low Latency Communications (mURLLC). Developed machine learning based multiple configured-Grants mURLLC is a potential solution to meet the latency constraint of 6G new use case under bursty traffic scenario. |
| Exploitation Route | We planned to organize a workshop on artificial intelligence in Industrial IoT such that these findings can be disseminated to industries. But, we were unable to organize due to Covid-19 pandemic. We extended this work for UAV enabled IoT networks. We have been able to secure two new grants on machine learning enabled cellular connected UAV networks and federated learning based resource optimisation for 6G wireless communications. |
| Sectors | Agriculture Food and Drink Creative Economy Digital/Communication/Information Technologies (including Software) Environment Leisure Activities including Sports Recreation and Tourism Manufacturing including Industrial Biotechology Transport |
| Description | NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based radio access technology in 5G and Beyond, which offers a range of flexible configurations for different coverage enhancement (CE) groups to provide reliable uplink connections for massive IoT devices with diverse data traffic. To optimize the number of served IoT devices, the uplink resource configurations need to be adjusted in real-time according to the dynamic traffic, this brings the challenge of how to select the configurations at the Evolved Node B (eNB) in the multiple CE groups scenario with high-dimension and interdependency. We developed deep reinforcement learning based dynamic optimization schemes for NB-IoT. The developed schemes can solve the current main problem of random access under bursty traffic condition. These findings highly useful for the next generation artificial intelligence enabled wireless systems. We have also developed resource allocation algorithms for Ultra Reliable Low Latency Communications (URLLC) enabled Industrial Internet of Things (IIoT) Networks. We extended this work for UAV enabled IoT networks as well as for new 6G use case of mURLLC. Our findings in this project have been disseminated in numerous top IEEE journals and IEEE Flagship conferences and attracted the interest of non-academic industries. Very recently, we incorporated distributed federated learning approaches as well to enhance the privacy of users. In addition, we considered cellular connected UAV to increase the coverage of massive IoT devices. These finding also used to secure four follow up EPSRC standard grants as well as one Industrial project on machine learning enabled 6G. |
| First Year Of Impact | 2018 |
| Sector | Digital/Communication/Information Technologies (including Software),Education |
| Impact Types | Economic |
| Description | Scalable Hybrid Architecture for Wireless Collaborative Federated Learning (SHAFT) |
| Amount | £445,428 (GBP) |
| Funding ID | EP/W034786/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2023 |
| End | 03/2026 |
| Description | Smart Solutions Towards Cellular-Connected Unmanned Aerial Vehicles System (AUTONOMY) |
| Amount | £409,171 (GBP) |
| Funding ID | EP/W004100/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2022 |
| End | 03/2025 |
| Description | Collaborative Research on Federated Learning |
| Organisation | University of Cambridge |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Proposed two mini projects for the TITAN Extension EPSRC Project (EP/Y037243/1), lead by University of Cambridge. |
| Collaborator Contribution | Two newly proposed mini projects are 1. AI-aided Pareto Optimization for UAV Swarm Trajectory Planning in NTNs Problem statement and brief state-of-the art summary (include an illustration if possible): multi-objective optimization (MOO) plays a pivotal role in the realm of airport flight planning, and its relevance extends to the domain of UAV Swarm Trajectory Planning in NTNs. In addition to conventional objectives, e.g., communication quality, energy consumption, and collision avoidance, UAV swarm introduces a new optimisation domain, i.e., swarm structures. During the planning phase, it is important to acknowledge the full potential of the considered system. The Pareto front (PF) becomes a pivotal indicator, with each point along this front representing an optimal solution. At the execution stage, we can choose the desired solution based on changeable constraints. Traditional MOO solution transforms the problem into a weighted single-objective optimisation problem, which only achieves one point on the PF. Some recent works use deep deterministic policy gradient (DDPG) algorithm to optimize this MOO problem, but the swarm structure optimisation and Pareto front are not included. Proposed new approach and methodology, provide a list of tasks and milestones: The first task is to design UAV swarm structures, where multi-agent reinforcement learning (MA-RL) can be developed to dynamically control the swarm by interacting with the environment. Based on the proposed swarm solution, the second task is to propose the PF via MO-soft actor and critic (SAC) algorithms. Milestones: 1) Design MA-RL algorithms for optimising UAV swarm structures; 2) Design MO-SAC algorithms for obtaining PF of the successful data packet rate and energy consumption under the fixed UAV swarm structure; and 3) Design multi-phase MO-SAC algorithms for obtaining PF for hybrid (centric + distributed) UAV swarm structure. 2. Federated Multi-modal Learning for Sensing and Communications Problem statement and brief state-of-the art summary (include an illustration if possible): Sensor fusion via wireless channels is important for sensing and communication systems as radiofrequency (RF) sensing has relatively low range resolution. However, different sensor belongs to different companies. The sensor fusion needs to consider both the multi-modal and privacy-protection problems. Existing works uses auto-encoder and federated average algorithms to solve this issue, but the auto-encoder structure design needs to manually access the local dataset on different agent and the federated average algorithm costs huge communication resources. Proposed new approach and methodology, provide a list of tasks and milestones: This project will first design a semi-supervised auto-encoder via variational Autoencoders to avoid leaking users' privacy. Then, this project will integrate the proposed auto-encoder algorithm into a new federated learning structure, where communication load via wireless channels will be minimized by considering the pruning and partial model transmitting. Milestones: 1) Semi-supervised auto-encoder algorithm for abstracting the common feature of multi-modal datasets; and 2) Federated multi-modal learning algorithm for the improve the performance of sensing and communications. |
| Impact | This collaboration started very recently. Research outcomes yet to come. |
| Start Year | 2023 |