Motivated by the new era of automation and the explosion of the IoT, the future wireless connectivity landscape of 6G and beyond will feature low-complexity high-throughput, ultra-reliable and low-latency wireless communication systems. Such stringent requirements make the radio access design very challenging and some fundamental performance / complexity tradeoffs must be made. Example of such requirements are packet error rate in motion of 10-9 or lower with latency in the order of few microseconds-milliseconds. In building up such robust wireless systems, the radio access architecture is typically distributed with large number of nodes cooperating together to meet the target quality of service. For instance, cell-free massive MIMO systems, through coherent transmission, allow channel hardening by coherently beaming towards the mobile terminals.
However, there are several problems that need to be addressed before such distributed systems become practical and reliable. Cell-free massive MIMO systems present huge challenges in resource and user scheduling, synchronization, beamforming, power control, to name a few. Furthermore, in indoor dynamic environments e.g. moving, joining, leaving mobile terminals, there is a need for accurate tracking and proactive network reconfigurations to avoid outage scenarios, yet in relatively fast closed loop.
The purpose of this PhD is to provide solutions for the joint design of robust low-complexity 1) (adaptive) signal processing in the physical layer and 2) advanced scheduling algorithms in the medium access control layer. The PhD student will also explore the potential of machine learning-based processing. He/she will investigate how the signal processing and scheduling algorithms perceived as experts knowledge, can be can linked to and/or further complemented by machine learning.
The successful PhD candidate will be part of a large IMEC team working on the research, implementation and prototyping of future communications systems: experts in digital, analog and mm-wave ASIC design, wireless communications systems, PHY processing, MAC and higher layers, machine learning, optimization.
Required background: Electrical engineering: Signal processing for communication (equalization,synchronization ), Optimization theory
Type of work: 80% modeling/simulation, 10% experimental, 10% literature
Supervisor: Ingrid Moerman
Co-supervisor: Marc Moeneclaey (Ghent University)
Daily advisor: Mamoun Guenach
The reference code for this position is 2020-098. Mention this reference code in your application.