Abstract
Cognitive radio (CR) provides a way to utilize the radio resources in a smart manner. In order to exploit the benefits of CR technology either in CR systems or in CR enabled wireless sensor networks, CR based smart grids, CR based wireless body area networks (WBANs) and CR based Internet of Things (IOTs), it is necessary to characterize the radio traffic in a realistic way. In perspective of CR operating models, the radio traffic can be modeled either using conditional or unconditional modeling approaches. In unconditional modeling approach, observed data traffic is solely considered as interference while in conditional modeling approach the observed data traffic is first classified as signal and noise. Prior knowledge about the statistics of interference in unconditional modeling approach while signal (primary user) and noise (secondary user) characterization in conditional modeling framework will help in efficient utilization of radio resources within CR-enabled radio environment. Furthermore, performance analysis is carried out for both the unconditional and conditional models based on devised information theoretic criterion. Multivariate Gaussian mixture (MGM) as a special case of hidden markov random field (HMRF) and Gaussian mixture (GM) are suitable candidate models for the observed data traffic in ISM (Industrial, Scientific and Medical) band based on devised criterion in unconditional and conditional models, respectively.
Highlights
In recent times many innovative wireless technologies and high speed data services have been introduced to attract radio customers
Uni-modal and multi-modal distribution models are chosen to characterize the observed data traffic in 2.4 GHz ISM band. Such characterization of observed data traffic is a key as a prior knowledge which enable the utilization of available radio resources efficiently within Cognitive radio (CR)-enabled radio environments in perspective of both primary and secondary users
Performance analysis is carried out for both unconditional and conditional models based on devised information theoretic criterion
Summary
In recent times many innovative wireless technologies and high speed data services have been introduced to attract radio customers. The CR devices are capable to adjust the operating parameters dynamically by learning about their environment which clearly helps them to utilize the spectral and temporal resources efficiently, in a process known as dynamic spectrum access (DSA) [3], [4]. Due to this learning capability, the CR devices can switch between different operating. An accurate statistical model of the observed data traffic is the key for efficient DSA with non-interfering SU signals in CR-enable radio environments. The devised modeling approaches really pave the way for efficient DSA within CR-enabled radio environments by providing a prior information about observed data traffic statistics.
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