5G (and beyond) mobile communications will have to meet the strict requirements of several new services. In particular, four key elements of the 5G scientific and technological development are: i) ultra-reliable and low-latency communications (URLLC) for delay-sensitive applications; ii) cloud functionalities brought the closest possible to users and devices, in a paradigm called edge cloud and computing; iii) artificial intelligence and machine learning to optimize the activities of devices and machines; iv) MIMO communications with high-frequency radio signals, including millimeter wave (mmWave) communications. In such a context, this PhD investigation will focus on solutions for joint multi-connectivity (spatial diversity) and mmWave multi-GHz communications as enablers of URLLC. The major drawback of mmWave communications is their vulnerability to blocking events due to obstacles or beam collisions, which can be overcome through multi-connectivity: via diversity, messages can be delivered even when not all wireless connections allow effective communication due to temporary blockages. Communicating devices exploit multi-beamforming techniques to send information to several network access points at the same time. The PhD student will investigate the problems of beamforming, link selection, power optimization, and blockage counteracting in such a context, working on novel solutions that include spatial error-correcting coding. The PhD work will include a theoretical analysis of performance bounds and trade-offs, the numerical simulation of new error-correcting codes for multi-link communications, and the design of novel algorithms for the allocation of (radio, computational, and memory) resources in edge-cloud-assisted mobile networks. Artificial intelligence and machine learning will be exploited as tools to efficiently address the resource allocation problem in complex and dynamic scenarios that include user and obstacle mobility.
If interested, please send your CV to Nicola di Pietro (CEA LETI): email@example.com
(c) GdR 720 ISIS - CNRS - 2011-2020.