PhD - Leuven | More than two weeks ago
Motivated by the new emerging applications such as augmented reality, virtual reality, point to point for backhaul and datacenter or point to multipoint broadband access, the future wireless connectivity landscape of 6G and beyond will feature a wide a range of applications with very-high-throughput requirements. Such stringent requirements make the radio access design very challenging and some fundamental performance / complexity tradeoffs must be made. The move to sub-THz bands (i.e. 100-300) GHz to achieve wired-like speeds in the order of 100 Gbps requires revisiting the radio access design beyond the state of the art wireless technologies such as 5G networks and WLAN standards.
Building up robust sub-THz wireless systems relying mostly on line-of-sight becomes challenging in particular in indoor environments which requires amongst others to distribute the radio access. A general distributed MIMO architecture in mmWave systems consists of a large number of cooperative distributed access nodes (sometimes referred to as cell-free) and potentially some intelligent reflecting surfaces, and is being considered as one of the main pillars towards reliable and robust radio access in the lower spectrum. However in the sub-THz bands, the targeted extremely large signal bandwidth will be limited by (i) the fronthaul (FH) bandwidth hence limited signal coordination across the access points, (ii) the complexity of the digital baseband signal processing, (iii) the harsh radio propagation channel including the Doppler shift/spread even with low mobility to name a few. These challenges are by nature different from those in the sub-7-GHz bands, hence the need for novel distributed radio access designs.
The PhD candidate will investigate distributed scalable PHY and low-MAC signal processing for distributed MIMO architectures that can potentially combine access nodes and intelligent reflecting surfaces. The underlying complexity must comply with boundary conditions imposed by HW technology and the FH capabilities resulting in NP-hard multidimensional designs. Different state-of-the-art air interface techniques will be investigated with the end-goal to simplify the overall end-to-end processing across the massive distributed access points. The proposed designs are expected to achieve (i) higher robustness to Doppler, RF and fronthaul hardware impairments, (ii) low-complexity synchronization, channel sounding and beamforming algorithms, and (iii) higher energy efficiency. Part of the research will leverage the expert knowledge to hire ideas from artificial neural networks to replace the complex design problems with a well-trained deep neural network, especially, for devising low-complexity radio resource allocation solutions.
The successful PhD candidate will build both on the state of the art and on IMEC's experience of high-throughput mm-wave communication systems. He will identify the key air interfaces and signal processing challenges to investigate. His proposed air interface, signal processing blocks and system architectures will be simulated primarily in Matlab in order to evaluate the system performance and optimize the different components. This research may be combined with experiments and measurement on communication testbeds developed at IMEC.
As a PhD student, you will be part of a large IMEC team working on the research, implementation and prototyping of sub-THz communications systems composed of experts in digital, analog and mm-wave design, wireless communication systems, signal processing and machine learning, channel measurements and modelling. This is a unique opportunity to develop innovative, multi-disciplinary technology and shape future wireless networks. You will publish your research in top-level journals and conferences.
Required background: Signal processing for wireless communications. Knowledge of channel modelling and optimization techniques is a plus. Proficiency with Matlab or Python is a must.
Type of work: 10% literature and theory, 80% design, modelling and simulation, 10% design/experimental
Supervisor: Ingrid Moerman
Daily advisor: Mamoun Guenach
The reference code for this position is 2024-067. Mention this reference code on your application form.