Gradient-free Online Resource Allocation Algorithms for Dynamic Wireless Networks

Future communication networks will be faced with supporting highly mobile, heterogeneous (including aerial) devices, which calls for new and efficient 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allocation policies that are able to adapt on-the-fly to the network dynamics while relying on little and possibly outdated information.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 efficient and gradient-free online power allocation algorithm relying only on a scalar-worth of feedback.