  Optimal resource allocation in modern communication networks calls for the
optimization of objective functions that are only accessible via costly
separate evaluations for each candidate solution. The conventional approach
carries out the optimization of resource-allocation parameters for each system
configuration, characterized, e.g., by topology and traffic statistics, using
global search methods such as Bayesian optimization (BO). These methods tend to
require a large number of iterations, and hence a large number of key
performance indicator (KPI) evaluations. In this paper, we propose the use of
meta-learning to transfer knowledge from data collected from related, but
distinct, configurations in order to speed up optimization on new network
configurations. Specifically, we combine meta-learning with BO, as well as with
multi-armed bandit (MAB) optimization, with the latter having the potential
advantage of operating directly on a discrete search space. Furthermore, we
introduce novel contextual meta-BO and meta-MAB algorithms, in which transfer
of knowledge across configurations occurs at the level of a mapping from
graph-based contextual information to resource-allocation parameters.
Experiments for the problem of open loop power control (OLPC) parameter
optimization for the uplink of multi-cell multi-antenna systems provide
insights into the potential benefits of meta-learning and contextual
optimization.
