Advanced Stochastic Optimisation Techniques for Ultra-Reliable Low-Latency Communications (URLLC)
As real-time responses (in the order of sub-millisecond) with high reliabilities (packet error rate of less than 1e-5 ) are critical for many industrial applications, realising the potential benefits of the industrial IoT depends on enabling URLLC.
The overall goal of this research project is to propose and analyse radio resource allocation schemes enabled by advanced stochastic optimisation techniques including risk-sensitive decision making to support URLLC in uplink and downlink in coexistence with enhanced Mobile Broadband (eMBB) traffic.
This research project on URLLC is a step forward in materialising many mission-critical applications of IoT (such as autonomous and connected cars, and smart cities), which can significantly impact the public’s quality of life. The proposed techniques to enable URLLC have a strong potential to result in research patents, provide input to the standardisation bodies, and attract industrial partners and investments.
The proposed RRA schemes that incorporate advanced stochastic optimisation techniques will contribute to the state-of-the art in URLLC in four new ways:
- QoS provisioning through risk management
- Simultaneous delivery of URLLC and eMBB
- Grant-free URLLC uplink transmissions
- Machine learning-based real-time implementation of the proposed schemes.
A list of relevant publications can be downloaded from the Loughborough University repository.
Dr Mahsa Derakhshani, Lecturer in Digital Communications and Director of Telecommunications MSc Programmes
“This is an exciting and adventurous project because there is an urgent need for implementing URLLC, yet it remains a challenging endeavour, in particular in massive networks, as it comprises two conflicting quality of service (QoS) requirements of high reliability and low latency (both under resource availability constraints).”