Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, …