Smooth Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios

Spectrum sharing in a cognitive radio system involves a secondary user updating transmit power for sharing spectrum with a primary user. The deep
$Q$
-network in the framework of deep reinforcement learning achieves transmit power control by a deep neural network for learning a nonlinear mapping from states to
$Q$
-values. Since a deep neural network is confronted with noise susceptibility, the deep
$Q$
-network produces deteriorative network parameters and volatile
$Q$
-values in the presence of contaminated states. In view of the positive effect of kernel least mean square (KLMS) for signal smoothing, we combine KLMS with the deep
$Q$
-network for smoothing network-generated outputs. Since an inappropriate step size of KLMS causes under-smoothing or over-smoothing issues, a weighting procedure using past
$Q$
-values is proposed for cooperating with KLMS. We assess the incremental ratio of the success rate of the smooth deep
$Q$
-network to that of the deep
$Q$
-network (RSR) in cognitive radios. Simulations show that RSR has averaged almost over 30% at the early stage of power control. In particular, the maximum RSR reaches almost over 80% or 180% at different scenarios of power control for the primary user. In addition, the smooth deep
$Q$
-network achieves an improved success rate in comparison with other algorithms.