PhD Thesis defense: Efficient Network Control for Large and Highly Dense Millimeter Wave Deployments

5 Mar

Nina Grosheva, PhD Student, IMDEA Networks Institute and University Carlos III of Madrid

PhD Defense

Wireless networks have become an integral part of modern society, providing ubiquitous connectivity to a growing number of connected devices. Concepts like Augmented Reality (AR)/Virtual Reality (VR), remote surgery and Industry 4.0 will further increase the number of users and the volume of data being transferred. Satisfying the demands of these applications necessitates novel technologies and innovative designs. Operation in the high frequency Millimeter-Wave (mmWave) band is foreseen as a crucial part of the design of future wireless networks. The extremely large signal bandwidth offered at mmWave frequencies enables multi-Gbps, low-latency wireless connectivity for a peak performance that far exceeds what can be achieved in the currently used sub-6 GHz bands. Realising the potential of mmWave technology requires adaptation to the challenging propagation environment at all levels of the protocol stack. Significant work has already been done to enable single-link communication through narrow directional beampatterns. Network aspects and interactions in large and dense networks, however, remain largely unexplored. The goal of this thesis is to study the performance of mmWave protocols in dense deployments with many Access Points (APs) and Stations (STAs) near each other. Such deployments are required for sufficient coverage in real-world implementations, however, they come with unique challenges due to the complex nature of interference in mmWave networks. The thesis studies different proposed architectures for mmWave to gain insight into sources of inefficiency and performance degradation. We then propose solutions to address these challenges and enhance the operation of mmWave networks.

To enable research into dense mmWave networks we implemented the latest mmWave WiFi standard, IEEE 802.11ay, in the network simulator ns-3. We implemented advanced features like Multiple-Input and Multiple-Output (MIMO), channel bonding and novel Beamforming Training (BFT) protocols introduced in IEEE 802.11ay. Using our model, we were able to get in-depth insights regarding the performance of various protocol features of state-of-the-art mmWave WiFi. We first focus on BFT scalability in dense deployments, looking at how the accuracy of the training can degrade in high-interference environments, as well as how the growing overhead can limit communication throughput. We propose the use of the novel Group Beamforming protocol introduced in IEEE 802.11ay as it enables simultaneous training of all STAs within a Basic Service Set (BSS). We additionally propose performance enhancements for the Group Beamforming protocol that can increase accuracy. Our analysis demonstrates that the modified Group Beamforming protocol has higher accuracy than legacy BFT and enables higher network throughput due to the reductions in overhead. We then designed a physical (PHY) layer signalling solution that enhances packet reception in mmWave WiFi devices. We focused on two sources of inefficiency – the use of omnidirectional receiver beampatterns, and the overhearing of unwanted packets. Both of these problems limit the performance in the network and affect spatial re-use. SIGNaling in the PHY Preamble (SIGNiPHY) embeds the user identifier (ID) in the PHY packet preamble, allowing for early user identification. SIGNiPHY increases the resilience to interference, enabling packet decoding under challenging conditions and increasing spatial sharing. We evaluated SIGNiPHY in ns-3, as well as an FPGA testbed, revealing significant gains in throughput, latency and fairness.

The next work in the thesis presented our mmWave MIMO implementation with standard compliant MIMO BFT protocols and channel access. We demonstrate how our analog BFT protocol was able to train multiple transmit and receive antennas to find independent streams. Challenges with mobility, the sparsity of the mmWave channel and complex BFT protocols require further research into mmWave MIMO. However, we found promising results regarding the viability of mmWave MIMO even with a fully analog architecture. We further investigate an alternative architecture for devices with multiple Radio Frequency (RF) chains. In multi-connectivity networks, users maintain several simultaneous links with spatially distributed APs. Unlike MIMO networks, multi-connectivity designs aim to not only increase throughput but also enhance resilience and robustness. This makes them extremely suitable for mmWave networks which suffer from frequent outages and service interruptions. Therefore, we propose a distributed multi-connectivity design that relies solely on local analog beamforming for interference management. Our architecture was able to enhance resilience and maintain connectivity at all times even under high interference, as well as exploit the spatial diversity of the multiple links to achieve gains in throughput.

Finally, we study the novel IEEE 802.11bf protocol which aims to standardize sensing operation in WiFi. As a topic of significant interest from both academia and industry, environmental sensing using communication signals opens new possibilities for mmWave networks. We present a first initial system-level study that looks at joint communication and sensing in a mmWave WiFi network. We analyse how the sensing parameters affect the performance and identify network configurations where both sensing and communication can coexist, enabling successful integration of sensing and communication in a single system.

To conclude, in this thesis we present a comprehensive analysis of dense mmWave networks, proposing performance enhancements to enhance scalability and efficiency. We then look at future possibilities for mmWave, by analysing the possibilities of advanced devices with multiple RF chains, as well as novel paradigms that integrate environmental sensing into mmWave WiFi operation.

About Nina Grosheva

Nina Grosheva is a PhD student in the Wireless Networking Group at IMDEA Networks and in the Telematics program at the University Carlos III de Madrid. Previously, she completed an MSc in Communications Engineering at RWTH Aachen University and a BSc in Electrical Engineering at Saint Cyril and Methodius University in Skopje. Her research interest is in mmWave networks, with a particular focus on MAC and network layer design and analysis.


PhD Thesis Advisor: Dr. Joerg Widmer, IMDEA Networks Institute, Spain

University: University Carlos III of Madrid, Spain

Doctoral Program: Telematic Engineering

PhD Committee members:

  • President: Matthias Hollick, Head of the Secure Mobile Networking Lab, Technical University of Darmstadt
  • Secretary: Antonio de la Oliva, Professor Titular, University Carlos III of Madrid
  • Panel member: Dimitrios Koutsonikolas, Associate Professor, Department of Electrical and Computer Engineering, Northeastern University

More info

  • Location: Aula de Grados, Padre Soler Building, University Carlos III of Madrid, Avda. Universidad 30, 28911 Leganes – Madrid
  • Time: 15:00
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