Architecture Model for Wireless Network Conscious Agent

Cognitive radios (CRs) use artificial intelligence algorithms to obtain an improved quality of service (QoS). CRs also benefit from meta-cognition algorithms that enable them to determine the most suitable intelligent algorithm for achieving their operational goals. Examples of intelligent algorithms that are used by CRs are support vector machines, artificial neural networks and hidden markov models. Each of these intelligent algorithms can be realized in a different manner and used for different tasks such as predicting the idle state and duration of a channel. The CR benefits from jointly using these intelligent algorithms and selecting the most suitable algorithm for prediction at an epoch of interest. The incorporation of meta-cognition also furnishes the CR with consciousness. This is because it makes the CR aware of its learning mechanisms. CR consciousness consumes the CR resources i.e. battery and memory. The resource consumption should be reduced to enhance CR's resources available for data transmission. The discussion in this paper proposes a meta-cognitive solution that reduces CR resources associated with maintaining consciousness. The proposed solution incorporates the time domain and uses information on the duration associated with executing learning and data transmission tasks. In addition, the proposed solution is integrated in a multimode CR. Evaluation shows that the performance improvement for the CR transceiver power, computational resources and channel capacity lies in the range 18.3% - 42.5% , 21.6% - 44.8% and 9.5% - 56.3% on average, respectively.