The next generation of mobile networks will connect vast numbers of devices and
support services with diverse requirements. Enabling technologies such as millimetre-wave
(mm-wave) backhauling and network slicing allow for increased wireless capacities
and logical partitioning of physical deployments, yet introduce a number of
challenges. These include among others the precise and rapid allocation of network
resources among applications, elucidating the interactions between new mobile networking
technology and widely used protocols, and the agile control of mobile infrastructure,
to provide users with reliable wireless connectivity in extreme scenarios.
This thesis presents several original contributions that address these challenges.
In particular, I will first describe the design and evaluation of an airtime allocation
and scheduling mechanism devised specifically for mm-wave backhauls, explicitly addressing
inter-flow fairness and capturing the unique characteristics of mm-wave communications.
Simulation results will demonstrate 5x throughput gains and a 5-fold
improvement in fairness over recent mm-wave scheduling solutions. Second, I will
introduce a utility optimisation framework targeting virtually sliced mm-wave backhauls
that are shared by a number of applications with distinct requirements. Based
on this framework, I will present a deep learning solution that can be trained within
minutes, following which it computes rate allocations that match those obtained with
state-of-the-art global optimisation algorithms. The proposed solution outperforms a
baseline greedy approach by up to 62%, in terms of network utility, while running
orders of magnitude faster. Third, the thesis investigates the behaviour of the Transport
Control Protocol (TCP) in Long-Term Evolution (LTE) networks and discusses
the implications of employing Radio Link Control (RLC) acknowledgements under
different link qualities, on the performance of transport protocols. Fourth, I will introduce
a reinforcement learning approach to optimising the performance of airborne cellular
networks serving users in emergency settings, demonstrating rapid convergence
(approx. 2.5 hours on a desktop machine) and a 5dB improvement of the median
Signal-to-Noise-plus-Interference-Ratio (SINR) perceived by users, over a heuristic
based benchmark solution. Finally, the thesis discusses promising future research directions
that follow from the results obtained throughout this PhD project
