Gradient-free Online Resource Allocation Algorithms for Dynamic Wireless Networks
Résumé
Future communication networks will be faced with supporting highly mobile, heterogeneous (including aerial) devices, which calls for new and ecient resource allocation policies that are able to adapt on-the-fly to the network dynamics while
relying on little and possibly outdated information. The aim of this paper is twofold: to explicitly take into account the device
mobility, their network connectivity patterns and behavior (which may be completely arbitrary and unpredictable); and to greatly reduce the information required at the transmitter. For this, we exploit the framework of online optimization and exponential
learning to derive a provably ecient and gradient-free online power allocation algorithm relying only on a scalar-worth of
feedback.
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