Internet of Radio Light (IoRL): Research for the IoRL project interweaves digital design with the architectural interior design of spaces and the electronic design of the light roses. Further research within IoRL will integrate digital media (mobile phones, laptop/tablet PCs, SHD/UHD TVs and Augmented/Virtual Reality (AR/VR) headsets) in the radio-light internet of small family homes (BRE home demonstrator), multi-occupancy high rise homes, buildings with public access (museum demonstrator) and public places (train station and supermarket demonstrators).
5G for Tactile Internet: Redesigning the core network using Software Defined Networks (SDN) so that Tactile Internet traffic gets priority route through it thereby obtaining low end-to-end latencies.
Digital Media for Interactive and Tactile Internet: Research in the development of digital media for the remote driving of cars and tele-control of robots for tele-surgery.
5G Network Design: Redesigning mobile phone base stations so that the radio signal can be steered towards individual or groups of user equipment whilst minimising the noise induced to other user equipment due to the effect of side lobes.
Health and Safety Product Monitoring: Developing an information system to monitor the usage of Internet of Things (IoT) enabled health and safety products, such as first aid boxes, ear plug dispensers and fire extinguishers, in hospitals and companies and to organise the management of their replenishment using an Artificial Intelligence (AI) system that predicts their usage trends.
Digital TV Transmission Optimisation: Developing channel estimation and equalisation technologies and MIMO diversity technologies to optimise the transmission of TV RF signals to homes.
Radio Network Planning: Formulating the deployment of small cells as a multi-objective problem for cost, coverage and network throughput in order to minimise cost (number of deployed small cells) while maximising service area coverage and network throughput simultaneously.
Antenna Design and Coverage Optimisation: Developing accurate geographical coverage prediction maps for FM and UHF for channel and frequency allocations and to avoid unwanted interferences and optimising the design of antennas to obtain desired coverage areas.
PhD projects for research students
Network Function Virtualisation requires a Management and Orchestration system to manage the allocation of computer, storage and communication resources between tenant of the cloud network. As the demand for computer, storage and communication resources changes the parameters of the Virtual Network Functions Instances need to be modified. This can be done from the network administrator's Openstack Horizon interface or from Command Line Interface but this is time-consuming.
This research project uses an Openstack API to autonomously change the autonomously change computer, storage and communication resources allocated to tenant based on existing big data of user behaviour and artificial intelligence predicted future demand. This demand must be modelled in order to emulated the behaviour of for example vehicles on the road network or trains on the rail network or automated guided vehicles in a factory or warehouse.
Suitable for Communication Network students with an aptitude for IP networking and software engineering.
5G Networks are expected to support many use cases ranging from autonomous vehicles, e-health, industry 4.0, entertainment, transport, smart cities etc. which will place a wide range of technical requirements 5G Network. The 5G network is expected to consist of a mix of Macro, Micro and Pico cells within which communications (link and slice capacities), computational and storage resources will require to be located. Managing the deployment of mix of Macro, Micro and Pico cells and the communications, computational and storage resources to support these use cases will be require solving the set of all NP decision problems using a non-deterministic algorithm in polynomial time. The objective of this research is to: 1. Study a range of use cases that will be required to co-exist and develop user, functional and technical requirements of 5G technical resources. 2. Develop the Problem model: A problem model is an abstract mathematical representation that captures the main characteristics of the problem to be optimised. Usually, models are intelligent simplifications of reality. It involves approximations/assumptions and sometimes may skip processes that are complex to represent mathematically but can easily be modified and is still able to provide useful insights to the modelled problem. 3. Develop the Problem formulation: Identify a set of decision variables, objective(s) and constraints that characterise the problem. 4. Develop the Optimisation Method: Once the optimisation problem is formulated, the next step is to solve the model, which involves finding the optimal values of the decision variable(s) to the model based on the objectives(s) and respecting the constraint(s) of the problem. Typically, efficient algorithms are developed to solve the model, either to optimality or approximately. 5. Regardless of the meta-heuristic algorithm considered to solve a given optimisation problem, there are three core design questions common to all meta-heuristics in approaching an optimisation problem; the, definition of the objective (or fitness) function that will guide the search, and the definition of variation operators that move the algorithm from one point in the search space to another.
This research project will investigate how to learn from the behaviour of the radio channel in different parts of the network using big data from channel behaviour, experienced by existing user equipment to build an evolving database of behaviour changing with time. It will then use this big data of channel behaviour and artificial intelligence to predict the channel behaviour that would be experienced by new user terminals admitted to the network to set the Radio Access Network's (RAN) and user equipment's (UE) radio transmission parameters. A model of the radio environment will be built and used to interact with a Mobile Edge Cloud Virtual Network Function that will hold the big data database and the artificial intelligence algorithms to deduce decisions on how to parameterise the RAN and UE in the call admission control process. Comparisons of the performance of the proposed admission control system will be compared with more conventional approaches to ascertain performance gains obtained using big data and artificial intelligence.
Communication Network students with a strong aptitude to software programming and IP networking are suitable.