To develop indoor localisation and navigation algorithms and software for fire rescue and smart phone based applications.

Digital Twins in smart cities are an innovative and breakthrough concept for managing the city logistics infrastructure and improving people's quality of life. Wireless Network Digital Twins (WNDT) as one of the most important components for achieving the above vision, have attached huge attentions from both industry and academia. However, it faces several technical challenges (TCs): 1) The deployment of WNDT in 5G/6G use cases (e.g., last-mile smart logistics) faces huge challenges, as the key components and key performance indicators (KPI) are largely un-known. 2) The last-mile logistics scenarios are complex. Currently, there is no 3D true multiple-path ray-based radio propagation tool to predict how radio waves propagate in these scenarios, especially in the sub-Terahertz band identified for 6G. 3) There lacks digital models of high-resolution last-mile logistics environment and 6G devices such as Reconfigurable Intelligent Surfaces (RIS), and extremely large MIMO antenna arrays, which could be extremely useful for last-mile logistics use case. 4) Radio access network (RAN) planning and optimization algorithms for last-mile logistics use cases are complex and computationally intensive, it is challenging to find fast and accurate algorithms to facilitate off-line network planning and online real-time network optimisation. To address the above challenges, COMET (Communications Enabled, AI/ML based Digital Twins for Smart city Logistics and Last Mile applications) project team has been assembled comprising world-class expertise in wireless communications, system modelling, performance analysis, software development, AI, and smart city related areas to address the above challenges and seize the large commercial opportunity. The success of the project will enable the provisioning of an innovative and comprehensive, digital twins-based solution for managing wireless infrastructure and the smart city last-mile logistics use case, enabled by 5G/6G communications. The project will contribute to the improvements in the digital economy and creating new jobs, which will have a huge impact in a wide range of vertical industries, e.g., smart living, and health care, etc. The project will also help strengthen the UK's position in the future wireless communications domains and smart city areas.

The marine sector is a critical component of the global economy. Over 80% of the volume of the world's international trade is carried by sea. There is increasing interest from governments and corporations to transition from fossil fuel powered vessels to electric ships to reduce the carbon footprint. Globally, the Electric Ship Market is projected to grow from USD 4.9B in 2021 to USD 12.8B by 2030\. There is an urgent need for solutions to monitor and manage the operation of electric vessels. Reliable communication networks are a critical foundation for electrification of the marine sector. As of today, the communication technology used for near-shore vessels is cellular (4G/5G), while ocean-going vessels use satellite communications. Ensuring adequate coastal 5G coverage is a challenge that all governments and maritime operators will have to confront. Efforts are starting now in Singapore to deploy a coastal 5G network that will be fully implemented in 2025\. This project brings together unique expertise from companies in the energy management (Ampotech, Singapore) and network planning (Ranplan, UK) industries, together with leading academic experts developing new network optimisation technologies. The main outcomes of the project are: 1) An Internet of Thing gateway to collect electric vessel data, 2) fleet management software, 3) costal and vessel network planning software tools, and costal and vessel network optimisation solution powered by reconfigurable intelligent surface (RIS).

Artificial intelligence (AI) is expected to have a major impact on future wireless communications and diverse vertical domains. New business opportunities and emerging use cases would be offered by integrating AI techniques into wireless networks. To realize the benefits of AI-empowered the sixth generation (6G) in practice, this project aims to devise effective AI-based solutions that can dynamically adapt to the changes in network environments with limited volumes of data. Our approach is to integrate AI techniques into passive reconfigurable intelligent surfaces-assisted communication, which is a potential energy/spectral-efficient substitute for active communication components employed in most current wireless systems. We believe that AI-empowered 6G communication will open exciting opportunities for mobile industry worldwide, as it helps to create a more personal approach for customers.

Artificial intelligence (AI) is expected to have a major impact on future wireless communications and diverse vertical domains. New business opportunities and emerging use cases would be offered by integrating AI techniques into wireless networks. To realize the benefits of AI-empowered the sixth generation (6G) in practice, this project aims to devise effective AI-based solutions that can dynamically adapt to the changes in network environments with limited volumes of data. Our approach is to integrate AI techniques into passive reconfigurable intelligent surfaces-assisted communication, which is a potential energy/spectral-efficient substitute for active communication components employed in most current wireless systems. We believe that AI-empowered 6G communication will open exciting opportunities for mobile industry worldwide, as it helps to create a more personal approach for customers.

COVID-19 has had a profound impact on mobile network operations: 1) network loads have surged far beyond capacity; 2) network energy consumption has increased, resulting in a higher CO2 emission; and 3) engineers have limited access to sites, in particular, for in-building wireless networks, which has caused significant problems on network deployment and maintenance. The above points emphasise that: 1) mobile networks must be able to adapt to changes; 2) the planning, commissioning, and operations of networks, need to be automated so that the number of physical site visits, can be minimised. To this end, many what-if scenarios need to be carefully evaluated before changes of network parameters are commissioned. This calls for the development of 'perfect computer replicas of the real world' -- aka as a digital twin - reflecting the environment, the network components, and radio network simulation. In this project, Ranplan will develop a digital twin of environment, network device and radio network simulation based on a microservice architecture to enable automatic network planning and optimisation in the cloud. Mobile networks are a key enabler for economy, social connectivity, remote working/ teaching. As the use of network increase, so does energy consumption. Recent studies suggest that mobile networks will produce 320 million Tonnes of CO2 by the end of the current decade. Ranplan believes that the proposed project can enable 30% savings on mobile network CAPEX/OPEX, resulting in a substantial reduction of use of equipment and CO2 emissions.

COVID-19 has had a profound impact on mobile network operations: 1) network loads have surged far beyond capacity; 2) network energy consumption has increased, resulting in a higher CO2 emission; and 3) engineers have limited access to sites, in particular, for in-building wireless networks, which has caused significant problems on network deployment and maintenance. The above points emphasise that: 1) mobile networks must be able to adapt to changes; 2) the planning, commissioning, and operations of radio access networks, need to be automated so that the number of physical site visits, can be minimised. To this end, many what-if scenarios need to be carefully evaluated before changes of network parameters are commissioned, e.g., how the changes of network parameters will impact the network coverage. Radio propagation models play a central role in evaluating these what-if scenarios. Furthermore, network automation calls for interactions between radio signal prediction with an eco-system of data analytics, optimisation, and operations support system (OSS) software tools, which makes a migration to a cloud computing platform based on microservices, imperative. In this project, we will enhance Ranplan's **world first** combined indoor-outdoor radio propagation engine with machine learning algorithms and implement the new radio propagation engine as a **microservice** in a cloud-computing platform. The first part of this innovative approach will lead to a universal radio signal (or interference) prediction engine that works in **all scenarios** (indoor-outdoor, millimetre waves, etc), while the second part will make it **universally available** so that it can easily interact with other services and make use of the vast computing power on the cloud. develop a stochastic radio propagation model based on Mobile networks are a key enabler for economy, social connectivity, remote working/ teaching. As the use of network increase, so does energy consumption. Recent studies suggest that mobile networks will produce 320 million Tonnes of CO2 by the end of the current decade. Ranplan believes that the proposed project forms a foundation to enable network operation automation, resulting in a substantial reduction of use of equipment and CO2 emissions.

no public description

This project will curate geospatial social media data of wireless blackspots to enable 5G service rollout to improve both urban and rural coverage. The future wireless broadband on our digital economy is critical to ensuring connectivity across a wide range of social and industrial sectors, including driverless cars and manufacturing. Understanding consumer experience in real-time using context rich geospatial social media data is critical and the machine learning and data gathering innovations will transform UK businesses and empower UK to lead the AI and 5G revolution.

The rapid urbanisation and wide adoption of motor vehicles in Guangdong, China has increased traffic and resulted in congestions, loss of productivity, and negative effects to the environment. The project aims to respond to these challenges by using big data analytics to characterise and predict the spatial-temporal (ST) traveller mobility and traffic patterns, to develop data analytics platform and applications to enhance smart mobility (SM), e.g., public transportation scheduling, and seamless connection between public transportation and shared bikes. The improved efficiency/ optimised scheduling of public transports, using SM solutions, will benefit the working class who rely on such transports by reducing their cost on travel and trip time. This project will also provide environmental benefits such as reducing congestions and therefore CO2 emission, resulting in better air quality and improving health of the residents in large cities.

To leverage on the increasing availability of geo-tagged online social data to guide wireless network infrastructure deployment. To enable the development of holistic traffic and service aware deployment optimisation and planning solutions.

Awaiting Public Project Summary