Efficient network control for large and highly dense millimeter wave deployments

Abstract

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 for higher throughput, lower latency and higher reliability 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 the use of phased antenna arrays to generate 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 Station (STA) 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 used as a basis for our implementation the existing IEEE 802.11ad model, expanding it to cover 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 were able to get in-depth insights regarding the performance of various protocol features of the state-of-the-art mmWave WiFi, including, channel access, BFT, interference management and spatial sharing. 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 STA with a Basic Service Set (BSS), rather than relying on a per AP-STA training like legacy BFT from IEEE 802.11ad. We additionally propose performance enhancements for the Group Beamforming protocol that can increase the accuracy to ensure correct beampattern selection even under significant interference. 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 caused by the contention-based random channel access - 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. This enables the receiver to use the correct directional beampattern to receive the packet payload, as well as to filter any packets for which it is not the recipient. Thus, 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 spatially separated, 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 by introducing multi-connectivity. 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. Furthermore, mmWave multi-connectivity networks can have reduced implementation complexity by exploiting the spatial separation of the directional links. 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 look at resource allocation and study how the sensing and data traffic interact with each other. We further 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.