We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions ...
— We consider the issue of fair share of the spectrum opportunity for the case of spectrum-overlay cognitive radio networks. Owing to the decentralized nature of the network, we ...
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...
—We present two Maximum Likelihood (ML) based estimators for Non-Data-Aided (NDA) symbol timing recovery in MIMO systems. These estimators are based on the classical Unconditiona...
Abstract— We describe a decentralized learning-based activation algorithm for a ZigBee-enabled unattended ground sensor network. Sensor nodes learn to monitor their environment i...