The PRESTO, Predictive Quality-of-Service Management for Transport Services, project is to provide predictions of spatio-temporal network capacity, coverage and quality-of-service along streets. To realize such predictions, PRESTO will investigate, use and extend machine learning techniques to predict throughput and quality of service.
Recent advances in wireless communications, real-time control, sensing, positioning, collaborative spectrum management and artificial intelligence are enabling the transport sector to become more cost-efficient, secure, and sustainable. Due to new requirements arising in road, railway, air and maritime transport, reliable wireless communications between vehicles, road infrastructure and road users are no longer a "nice to have” but are integral parts of cooperative intelligent transportation systems and smart cities.
To deploy trustworthy mobile networks, which are capable of delivering both mobile broadband and mission critical services to the transport sector, network operators must deal with the problem that the performance of wireless networks vary in time and space. Conceptually, these performance variations can be addressed by improving service reliability and coverage and/or by enabling applications to foresee the dynamics of network performance and changes in the service quality. While techniques that enable the first approach (improving service reliability and coverage) are well-known, predicting the temporal and spatial variations of service performance metrics poses largely hard-to-answer and unsolved questions. Service performance metrics that are of high interest to mission critical applications include data throughput between users and the network as well as between users, and latency and service fulfillment probability. Indeed, if applications are provided with reliable predictions on these performance metrics, they can react proactively to changes in space and time.
The idea of the PRESTO project is therefore to provide predictions of spatio-temporal network capacity, coverage and quality-of-service along roads/streets. To realize such predictions, PRESTO will build on recent advances in the sensing capabilities of vehicles and the capacity increase of wireless networks, that enable the collection of large amounts of real-time measurement data. Specifically, the basic idea of PRESTO is to investigate, use and extend machine learning (ML) techniques to predict throughput and quality of service. The specific ML challenges that will be addressed are the following: ML for unbalanced or partial wireless measurements; ML with distributed data sets connected over communication networks; and real-time ML prediction.
To address these challenges, PRESTO will answer the following research questions and build on the answers to devise solutions that are applicable in wireless networks that are compliant with recent and currently evolving industry standards:
- What is the spatial granularity needed for transport applications such as assisted and autonomous driving?
- What are the pros and cons of network versus UE-centric predictive ML approaches?
- What is the ML prediction performance that can be achieved in real-time network operations?
- How should assisted vehicles and humans react to service quality predictions to take advantage of accurate predictions?