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/Job opportunities/Advanced Signal Processing in Cell-free Massive MIMO for Ultra-Reliable 6G Wireless Communication Systems

Advanced Signal Processing in Cell-free Massive MIMO for Ultra-Reliable 6G Wireless Communication Systems

PhD - Leuven | More than two weeks ago

You will be architecting the ultra-reliable and low-latency 6G wireless communications networks of tomorrow!

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 ms-ms. In building up such robust wireless systems, the radio access architecture is typically distributed with large number of nodes cooperating 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 fronthauling, synchronization, beamforming, load balancing and interference management, resource and user scheduling and their interplay, to name a few. Furthermore, in indoor dynamic environments with 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 with more focus on the synchronization of the distributed access points through a wired fronthaul  and 2) advanced joint power control and scheduling algorithms.  For some signal processing problems, the PhD student will also explore, when needed, the potential of machine learning to potentially beat the proposed sub-optimal algorithms and ensure better performance-complexity tradeoffs for real-time deployments  of cell-free massive MIMO.              

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/radar communications systems, PHY processing, MAC and higher layers, machine learning and optimization. This is a unique opportunity to actively contribute and develop breakthrough technology and build-up future 6G wireless communication systems.

 

Required background: Electrical engineering: Signal processing for communication, Optimization

 

Type of work: 80% modeling/simulation, 10% experimental, 10% literature

Supervisor: Ingrid Moerman

Co-supervisor: Marc Moeneclaey

Daily advisor: Mamoun Guenach

The reference code for this position is 2021-115. Mention this reference code on your application form.

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