6G Mitola Radio: Cognitive Brain That Has Collective Intelligence

Lead Research Organisation: University College London
Department Name: Electronic and Electrical Engineering

Abstract

While 5G is being launched worldwide, discussion for 6G is already taking shape. One unanimous view is that 6G mobile radios should be empowered by great intelligence, the kind of intelligence that allows each radio to make wise decisions that optimise its quality-of-experience over time and impact the network in a constructive way. In addition, 6G mobile radios will be more than just communication devices, providing also computation, security, energy services and etc. when appropriate. 'Intelligent' radio is not a new concept. In fact, back in 1998, Mitola formalised this concept and coined it cognitive radio, (also known as Mitola radio by many). This concept refers to a futuristic mobile communication device that goes beyond the possession of any hardware flexibility and is gifted the intelligence to access the spectrum anytime anywhere according to the environment and its need. The notion is general in that the term 'need' can include beyond-communication capability, such as computing, security and etc. in today's scenarios.

After 20 years of effort, however, progress has been limited. For dynamic spectrum sharing, 5G has shared spectrum technologies such as LAA, LWA, etc, but the intelligence remains at a very basic listen-before-talk (LBT) level. The deadlock for a genuine Mitola radio appears to be the need to make decisions based on very limited local information (local observations and actions) that should not only benefit itself but the entire network as a whole (global influence), without the overhead of one form of cooperation or another. In other words, the key is collective intelligence (as opposed to individual intelligence), one that enables each radio to evaluate and optimise its action and policy collectively with other coexisting radios without talking to them directly.

There will be several step changes if the ideal Mitola radio is successfully realised in 6G. First, spectrum utilisation will always be at the maximum with abundant spectrum resources available, and resource allocation is literally done in a self-organising fashion without any overhead for coordination. Latency for managing the resources will be significantly reduced as a result. Hidden terminal problem will also be eliminated because Mitola radio should possess the intelligence to identify them through interacting with the radio environment and optimise its action to avoid them. Furthermore, it will also be possible for Mitola radios to share not only the spectrum efficiently but also assist the network as service providers using their energy and computing resources.

Without coordination or cooperation, collective intelligence demands each radio establishing global intelligence of the network by itself. To achieve this, artificial intelligence (AI) may come as a convenient idea but the fact that the best action of one radio (i.e., a learning agent) is dependent on the action of another radio (another learning agent) troubles the state-of-the-art AI algorithms, making them highly ineffective. Different from the entire literature, this project's novelty is to develop an intelligence gathering mechanism that takes the game-theoretic perspective to enrich deep reinforcement learning. Such integration will equip Mitola radio the brain power of collective intelligence (from local action to global influence), and result in a holistic approach to optimise the parameters and essential functionalities for Mitola radio enabled multi-function wireless communications and services networks.

The project team includes BT and Toshiba, both of which have been active in the development of 5G and are keen to lead the research of 6G technologies. They will play an instrumental role in ensuring that the project outcomes are of great relevance, and their expertise will be crucial in the development of the testbed demonstrators of this project. They will also host the PDRAs to carry out tests of the proposed algorithms using their facilities.

Planned Impact

We anticipate that the impact of this project embraces diverse ways in which research-related knowledge and skills will benefit individuals, organisation and nations (e.g., (i) researchers in AI/machine learning and wireless communications, (ii) our industrial partners, including BT, and Toshiba, and (iii) broadband, mobile and Internet of Things (IoTs) customers), by fostering the economic competitiveness of the UK, and enhancing quality of life. Impact will be measured in the number of quality publications, consultancies and patents filed, commercialisation activities, follow-up industry funded collaborations and career development of the staff involved.

In summary, our activities have the following impact:
(1) Significant advances in wireless communications engineering and its applications arising from novel developments in modelling, optimisation, performance analysis and data and signal processing.
(2) New understanding of the operations of user-centric wireless networking.
(3) Expedited development in new communication technologies towards 6G.
Our overarching strategy for maximising the impact of our work is to ensure exposure and facilitate uptake at each stage of the analysis-methods-development pipeline.

Communications and Engagement are key. Journal (e.g., TCOM, TWC, TSP, etc.) and conferences dissemination as technical papers and tutorials at suitable conferences (e.g., ICC, GLOBECOM, ICML, etc.) will be one main mechanism. Researchers in machine learning, computing, wireless communications and other related areas, will be naturally engaged through research publications in established international journals and conferences. In addition, the PI has been very active in organising Special Sessions at major conferences and he is recently co-organising the 6G Workshop in the coming GLOBECOM 2019. This kind of workshop will provide a unique platform for focussed groups of researchers with strong interest in our work from academia and industry to engage and exchange ideas. Seminars and tutorials to industry in the UK and abroad will also be organised to present key results of this project. We will use CommNet2 as the hub to inform the members to the ICT community at large as well as the media and industry, and get them engaged whenever appropriate. UCL is a member of the WWRF forum and Cambridge Wireless. We will use this opportunity as a vehicle to engage with the members of the above fora. This project holds support for research visits and collaboration in in-kind contributions from BT, and Toshiba, to accelerate the research and maximise the exposure of the project's results. The research visits for the PDRAs at BT and Toshiba will provide them invaluable training opportunities to gain hands-on experience of the real-world wireless systems.

The exploitation plan with the industrial partners is clear and specific. BT will aid in WP3 for developing intelligent radios with MIMO technologies, WP4 for integrating the radios with other communication enhancements using AI and WP8 for the development of testbeds in UCL. On the other hand, Toshiba will support our WPs by giving industrial input on WP5 and WP8. We will make full use of Toshiba's expertise in estimation of realistic content popularity profile and tracking its time-varying nature for edge caching in WP5. Both partners BT, and Toshiba will be engaged to contribute to the setting up and demonstration of the testbed demonstrator and its potentials.

Publications

10 25 50
publication icon
Chai Z (2022) Port Selection for Fluid Antenna Systems in IEEE Communications Letters

publication icon
Wang S (2022) Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems in IEEE Journal of Selected Topics in Signal Processing

publication icon
Wong K (2022) Bruce Lee-Inspired Fluid Antenna System: Six Research Topics and the Potentials for 6G in Frontiers in Communications and Networks

publication icon
Wong K (2022) Fluid Antenna Multiple Access in IEEE Transactions on Wireless Communications

publication icon
Wong K (2023) Opportunistic Fluid Antenna Multiple Access in IEEE Transactions on Wireless Communications

publication icon
Xia W (2022) Multiagent Collaborative Learning for UAV Enabled Wireless Networks in IEEE Journal on Selected Areas in Communications

publication icon
Xie X (2022) Massive Unsourced Random Access: Exploiting Angular Domain Sparsity in IEEE Transactions on Communications

publication icon
Xu C (2021) Learning Rate Optimization for Federated Learning Exploiting Over-the-Air Computation in IEEE Journal on Selected Areas in Communications

publication icon
Yuan Y (2021) Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications in IEEE Transactions on Vehicular Technology

publication icon
Zhang J (2023) Deep Learning Based Predictive Beamforming Design in IEEE Transactions on Vehicular Technology

publication icon
Zhang J (2022) Embedding Model-Based Fast Meta Learning for Downlink Beamforming Adaptation in IEEE Transactions on Wireless Communications

publication icon
Zhang Y (2022) Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control in IEEE Internet of Things Journal

publication icon
Zheng T (2022) Physical-Layer Security of Uplink mmWave Transmissions in Cellular V2X Networks in IEEE Transactions on Wireless Communications

 
Description This project has produced a number of deep learning approaches that can be applied to various important optimisation problems for mobile communications. In particular, we have applied the methods for fast beamforming optimisation for vehicular-to-everything (V2X) applications. We also addressed the multi-agent learning problem and applied it to optimising UAV wireless networks. Distributed reinforcement learning has also been investigated and we were able to apply it to IoT and multicell multiuser wireless networks. Some of the research work also led to a serious study for federated learning which then led to the publication of a survey article that summarises the state-of-the-art and presents our vision on the potential of federated learning and why this may impact engineering. The federated learning study also led to the application to over-the-air computation for wireless networks. Meta learning was also studied which resulted in fast optimisation for beamforming in wireless networks. The fact that machine learning is a general optimisation approach means that it can be applied to a vast different kinds of optimisation problems in many different areas. We also applied machine learning to a new wireless communication system, namely fluid antenna system which has never been done before.
Exploitation Route We are able to publish our results in the top international journals and conference proceedings. The results might then be adopted by others in their engineering problems.
Sectors Digital/Communication/Information Technologies (including Software)