  Bandit Convex Optimisation (BCO) is a powerful framework for sequential
decision-making in non-stationary and partially observable environments. In a
BCO problem, a decision-maker sequentially picks actions to minimize the
cumulative cost associated with these decisions, all while receiving partial
feedback about the state of the environment. This formulation is a very natural
fit for wireless-network optimisation problems and has great application
potential since: i) instead of assuming full observability of the network
state, it only requires the metric to optimise as input, and ii) it provides
strong performance guarantees while making only minimal assumptions about the
network dynamics. Despite these advantages, BCO has not yet been explored in
the context of wireless-network optimisation. In this paper, we make the first
steps to demonstrate the potential of BCO techniques by formulating an
unlicensed LTE/WiFi fair coexistence use case in the framework, and providing
experimental results in a simulated environment. On the algorithmic front, we
propose a simple and natural sequential multi-point BCO algorithm amenable to
wireless networking optimisation, and provide its theoretical analysis. We
expect the contributions of this paper to pave the way to further research on
the application of online convex methods in the bandit setting.
