Learning channel allocation strategies in real time

Abstract

Preliminary investigations into using connectionist machine learning for dynamic channel allocation in real time are described. The algorithms were implemented on a simple radio testbed. It consists of a channel allocator and two channel requesters. The channel allocator is a computer that communicates via a transceiver. It learns to model the time-dependent behavior of the two channel requesters, and thereby learns to allocate channels dynamically. Channels are requested by two different transceivers run by small processors. The learning criterion is to minimize a cost function of channel use. The results show that models of channel activity can be learned and that controllers can learn to use these models to allocate channels. A comparison indicates that such controllers perform better than a fixed controller that does not learn.<>