6G Mitola Radio: Cognitive Brain That Has Collective Intelligence

6G Mitola Radio：具有集体智慧的认知大脑

• 批准号: EP/T015985/1
• 负责人: Kai-Kit Wong
• 金额: $ 59.96万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT015985%2F1
• 关键词: 6G; Mitola; Radio; Cognitive; Brain

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.

虽然5G正在全球范围内推出，但6G的讨论已经形成。一个一致的观点是，6G移动的无线电应该拥有强大的智能，这种智能允许每个无线电做出明智的决策，随着时间的推移优化其体验质量，并以建设性的方式影响网络。此外，6G移动的无线电将不仅仅是通信设备，在适当的时候还提供计算、安全、能源服务等。“智能”广播并不是一个新概念。事实上，早在1998年，Mitola就正式提出了这个概念，并将其称为认知无线电（也被许多人称为Mitola无线电）。这个概念指的是一种未来的移动的通信设备，它超越了任何硬件灵活性的拥有，并被赋予了根据环境及其需要随时随地访问频谱的智能。这个概念是笼统的，因为“需求”一词可以包括当今场景中的通信能力之外的能力，例如计算、安全等。然而，经过20年的努力，进展有限。对于动态频谱共享，5G已经共享了诸如LAA、LWA等频谱技术，但智能仍然处于非常基本的先说先试（LBT）级别。真正的Mitola电台的僵局似乎是需要根据非常有限的地方信息（地方观察和行动）做出决定，这不仅有利于自己，而且有利于整个网络（全球影响力），而不需要这种或那种形式的合作。换句话说，关键是集体智慧（而不是个体智慧），它使每个无线电能够与其他共存的无线电共同评估和优化其行动和政策，而无需直接与它们交谈。如果理想的Mitola无线电在6G中成功实现，将有几个步骤的变化。首先，频谱利用率将始终处于最大，具有丰富的可用频谱资源，并且资源分配实际上以自组织方式进行，而没有任何协调开销。因此，管理资源的延迟将大大减少。隐藏终端问题也将被消除，因为Mitola无线电应该拥有通过与无线电环境交互来识别它们的智能，并优化其行动以避免它们。此外，Mitola无线电不仅可以有效地共享频谱，还可以作为服务提供商使用其能源和计算资源来帮助网络。如果没有协调或合作，集体智慧需要每个无线电自己建立网络的全球智能。为了实现这一目标，人工智能（AI）可能是一个方便的想法，但事实上，一个无线电的最佳动作（即，学习代理）依赖于另一个无线电（另一个学习代理）的动作，这给最先进的AI算法带来了麻烦，使它们非常无效。与整个文献不同的是，该项目的新奇在于开发一种情报收集机制，采用博弈论的视角来丰富深度强化学习。这样的整合将使米托拉电台具备集体智慧的脑力（从本地行动到全球影响），并最终形成一种整体方法，以优化Mitola无线电启用的多功能无线通信和服务网络的参数和基本功能。该项目团队包括英国电信和东芝，两家公司都积极参与5G的开发，并热衷于领导6G技术的研究。他们将在确保项目成果具有重要意义方面发挥重要作用，他们的专业知识将在该项目的试验台演示器的开发中发挥关键作用。他们还将主持PDRA，使用他们的设施对拟议的算法进行测试。

项目成果

Massive Unsourced Random Access: Exploiting Angular Domain Sparsity

• DOI: 10.1109/tcomm.2022.3153957
• 发表时间: 2022-02
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Xinyu Xie;Yongpeng Wu;Jianping An;Junyuan Gao;Wenjun Zhang;C. Xing;Kai‐Kit Wong;Chengshan Xiao

Truly Distributed Multicell Multi-Band Multiuser MIMO by Synergizing Game Theory and Deep Learning

• DOI: 10.1109/access.2021.3059587
• 发表时间: 2021
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Kai‐Kit Wong;Guochen Liu;Wenjing Cun;Wenkai Zhang;Mingming Zhao;Zhongbin Zheng

Port Selection for Fluid Antenna Systems

• DOI: 10.1109/lcomm.2022.3152451
• 发表时间: 2022-01
• 期刊: IEEE Communications Letters
• 作者: Zhi Chai;Kai‐Kit Wong;K. Tong;Yu Chen;Yangyang Zhang

Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback

• DOI: 10.1109/twc.2023.3250422
• 发表时间: 2023-11
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zi-Yuan Chen;Zhaoyang Zhang;Zhu Xiao;Zhaohui Yang;Kai‐Kit Wong

Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

• DOI: 10.1109/jstsp.2022.3144874
• 发表时间: 2021-04
• 期刊: IEEE Journal of Selected Topics in Signal Processing
• 影响因子: 7.5
• 作者: Sihua Wang;Mingzhe Chen;Zhaohui Yang;Changchuan Yin;W. Saad;Shuguang Cui;H. Poor