Network Estimation in Cognition-Empowered Wireless Networks

An approach to parametric identification of the transmission processes of the terminals in a wireless network is proposed, presenting a tradeoff between accuracy of capturing the temporal dependencies in observations of transmission processes and the time complexity of the estimation procedure. The maximum likelihood estimator is built for an approximation of the true likelihood function for the observed network activity. A complex network where terminals store packets in a finite buffer and implement a backoff-based random channel access protocol is considered. Minimal information is available for observation to the cognitive terminals, in the form of energy readings mapped to the number of transmitting nodes in each time instant. The entanglement of the transmission processes induced by interference and the filtering effect of packet buffering make this task particularly difficult. It is shown how, based on the estimated parameters, the cognitive terminals, operating in the same channel resource, can predict the transmission trajectories of the other nodes and devise smart transmission strategies controlling the interference generated to the network.