Entropy‐based opportunistic spectrum access for cognitive radio networks

Abstract

This paper proposes an opportunistic spectrum decision scheme based on the weighted residual entropy concept for cognitive radio networks. The proposed scheme maximum entropy channel access selects appropriate spectrum opportunities based on the usefulness of the idle-channel remaining lifetime estimated through the weighted residual entropy function for the unoccupied channels. The performance of the proposed spectrum decision scheme is evaluated using a wealth of efficiency metrics, namely channel utilisation, channel collision, spectrum handoff rate and collision probability in perfect and imperfect spectrum sensing environments. The probabilities of primary signal detection and false detection are taken into the proposed spectrum hole selection technique. Despite the increasing probability of false detection, simulation results show that this central spectrum decision scheme greatly improves spectrum utilisation when the stated performance metrics were compared with those of known spectrum decision schemes such as random channel access, first-opportunity channel access and maximum remaining lifetime.

Bayesian inference and fuzzy inference for spectrum sensing order in cognitive radio networks

“…Collisions with primary users, channel evacuations during data transmission, and throughput were the main performance metrics where the proposed technique outperformed random scheduler. Similarly, Pirmoradian and Adigun borrowed an important concept from information theory, which is entropy. Their proposed technique evaluates channels based on weighted residual entropy function.…”

Dimensionality, Granularity, and Differential Residual Weighted Entropy

“…From the outset, Shannon identified two kinds of entropy: the "absolute" entropy of an outcome selected from amongst a set of discrete possibilities [1] (p. 12) and the "differential" entropy of a continuous random variable [1] (p. 36). The differential version of weighted entropy has found several applications: Pirmoradian et al [15] used it as a quality metric for unoccupied channels in a cognitive radio network and Tsui [16] showed how it can characterize scattering in ultrasound detection. However, under Shannon's definition the differential entropy of a physical variable requires the logarithm of a dimensioned quantity, an operation which necessitates careful interpretation [17].…”