Abstract
The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form . Some simulation results are presented.
Highlights
The electromagnetic radio spectrum is a limited but precious natural resource
In this paper we propose an algorithm with artificial intelligence for spectrum sensing determine the occupancy status of a channel, enabling opportunistic spectrum access
The scanning system scans the channels licensed for the primary user PU and the signal to noise ratio (SNR), bandwidth efficiency, channel capacity over the channel, and distance “d” is given as input to the Artificial Neural Network (ANN) model to predict the status of the channel
Summary
The electromagnetic radio spectrum is a limited but precious natural resource. With the technological development of various wireless communication systems and demand for frequencies, there is a common notion of scarcity of spectrum at which these wireless devices operate [1]. The cognitive radio is being considered as an intelligent device that enables an efficient use of spectrum. In this paper we propose an algorithm with artificial intelligence for spectrum sensing determine the occupancy status of a channel, enabling opportunistic spectrum access. We propose a new ANN model with a view to examine and subsequently predict the status of the channel in the TV band. This prediction algorithm enables one to decide whether a given band is occupied by a primary user (PU) or not— leading to an effective identification of a white space or spectrum hole for possible allocation to a secondary user (SU). Tized index 0,1 after suitably formulating the algorithm with appropriate training of the ANN engine
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