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Research projects

The current network infrastructure secures data transmissions of network subscribers by distributing cryptographic keys present in SIM cards. 


These keys are used to authenticate network subscribers, provide access to network resources, and establish a secure channel between each mobile device and the network infrastructure. 


The network infrastructure is used as a router to deliver data to communicating network subscribers. To establish secure D2D communications, mobile devices require cryptographic keys which are shared among them. These keys would require updating mechanisms in order to guarantee security over an extended period of time, and revocation mechanisms in the event that a mobile device has been maliciously compromised to the extent that it could no longer correctly authenticate the identity of its owner. 


Providing secure communication in an ad hoc D2D network therefore requires its own key management scheme.


Traditionally, a key management scheme relies on an online centralized trusted third party (TTP). This TTP is considered trustworthy and secure by every user inside the network. It can therefore distribute cryptographic keys between a set of network devices which would consequently be used to set up a secure communications channel. 


However, through the analysis of security and privacy challenges inherent in future emerging unsupervised 5G small cells, we conclude that this network architecture is unable to support an online centralized TTP to provide security. 


The design of a novel key management scheme is required to provide security exploiting a decentralized TTP.


This research is funded by the H2020 MCSA-ETN SECRET (722424) project


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The Fifth Generation (5G) mobile network standard has recently been concluded. When compared with legacy Long-Term Evolution (LTE) technology, the new 5G air interface introduces additional radio parameters that must be properly configured. 


Additionally, at the network level, the virtualization paradigm allows an agile creation and (re)configuration of multiple network slices on the same infrastructure that can be independently managed. In the horizon, one can also envisage the existence of private networks with limited geographical coverage targeting verticals, for example manufacturing industry, warehouse and logistics, mining and maritime ports, among others. Network slicing, along with such private networks, entails the coexistence of a large number of independent networks, either logical or physical, existing over a common coverage area for a certain time duration.


The optimized management of radio spectrum resorting to traditional rule-based techniques faces serious challenges. It can hardly consider all network and slice combinations in 5G in order to fully exploit all available degrees of freedom. 


To cope with this problem, our research proposes an Artificial Intelligence-based framework to help automate spectrum management and adapt to the dynamically varying network conditions.


This research is funded by the H2020 GENESIS (815178) project


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According to a recent United Nations urbanization study, urban areas are projected to grow rapidly in population and density between now and 2050, with about two-thirds of the world’s population located in urban areas by that time. 


The implications on the communication environment of these two trends will be profound: more and more devices will be used in complicated urban environments such as skyrise buildings. In this context, the beyond 5G landscape aims to build on the initial 5G deployment phase (2020) envisaging an ultra-dense network of THz-based small cells, that go beyond legacy 2D (two dimensional) deployment and aims to provide 5G services to skyrise buildings and high altitude platforms. This will require mobile stakeholders to review the way current mobile networks are modelled and deployed for optimsing B5G coverage.


Stochastic geometry has already provided some viable evidence that Point Processes can indeed model so called “repulsion and attraction” for transmitter deployments within a bounded space, reflecting the multi-tier deployment of regular macro cells coverage zones complemented by small cell hotspots. 


This research track aims to capitalise on stochastic geometry by extending these models to cater for 3D deployments, validated through practical experimentation methodologies.


Under the framework of a Marie Curie Fellowship action, we aim to propose new networking models for characterizing small cell (THz) deployment in 3D space for B5G networks.


This research is funded by the H2020 MCSA-IF-5GACE (839573) project


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Services requiring high data rates create the need for high speed wireless communications links. For example, the distribution and broadcast of ultra-high definition television (UHD) is expected to increase the data rate requirements of near-future satellite communication systems. Other services include the use of hyper-spectral or high-resolution sensors in Earth observation missions or for the monitoring of natural disasters. As allocated spectrum is limited and spectral efficiency becomes a dominant factor in such systems, both academic and industrial interests focus on the optimisation of signal constellations for higher order modulation formats.


This project seeks to develop a multidimensional adaptive bandwidth-efficient RF waveform coupled with adaptive spatio-temporal signal processing (or space-time block coding) that enables the realisation of ultra-reliable high data rate satellite links operating at Ka-band frequencies and above. 


Many of the investigated techniques have been separately or individually exploited in recent years. The project takes the promising next step of combining these methods to develop new signal designs that enable near outage-free link operation at optimum bandwidth and energy efficiencies under varying transmission channel conditions (including rain attenuation in particular).


This research is funded by the Ministry of Science and Technology of Thailand.


Ministry of Science and Technology in Thailand


Demand for high capacity and high-speed communication applications and services have led to the standardization of the fifth generation (5G) system. Satellite communication technologies complement terrestrial wireless provision by injecting significant capacity and extending services to areas such as mid-ocean, mid-air and remote locations that are not covered by terrestrial wireless infrastructure. 


The digital video broadcast (DVB) family of standards is an important enabler of this satellite technology and the extension of its second-generation, dubbed the DVB-S2X, has been optimized for fixed services (FS). However, with the increased demand for communication and data services on the move, there is the need to optimize DVB-S2X for application in mobile satellite communication environments.


This project is aimed at mobility enhancement of the DVB-S2X standard, delivering switching thresholds in realistic mobile satellite environments. In pursuance of this aim, a realistic mobile satellite channel has been simulated using terrain data integrated into a mobile scenario created within the Systems Toolkit (STK) platform, allowing the determination of a Markovian state transition probability matrix for the transmission channel. 


Three channel states are represented in the model, namely line-of-sight (LOS) when the path between mobile terminal and satellite is unobstructed, shadowing and blockage. The bit-error-rate (BER) performance of various modulation and coding pairings (MODCOD) of DVB-S2X have been evaluated for a mobile terminal moving within a selected urban route in Cardiff. 


New channel performance improvement techniques will be developed to enhance data throughput and allow the specification of new switching thresholds for DVB-S2X application in a mobile satellite environment.

Artificial intelligence (AI) is expected to make a major global impact and to soon significantly shape the way we live and work. 


The application of AI in telecommunications is now a particularly hot topic and covers a wide range of issues, including Internet of Things (IOT), autonomous vehicles, network security, wireless connectivity, etc.


This project builds on our previous work in Ka-band satellite link measurements and deploys AI and Deep Learning to develop and train a system to predict the channel state of a fixed earth-space transmission path that is impacted by rain and other signal impairments. One application of the research results will be to implement proactive fade mitigation interventions and system resource allocations that prevent link outages and maximise data throughput and link resilience. 


The project will deliver the ability to generate reliable knowledge of imminent transmission channel conditions and hence to adaptively optimise signal packaging and modulation strategies to protect against link outage and data loss.

Funders


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Ministry of Science and Technology in Thailand