Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Spatial Wireless Channel Prediction under Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel

Energy detection based spectrum sensing over enriched multipath fading channels

Energy detection has been for long constituting the most popular sensing method in RADAR and cognitive radio systems. The present paper investigates the sensing behaviour of an energy detector over Hoyt fading channels, which have been extensively shown to provide rather accurate characterization of enriched multipath fading conditions. To this end, a simple series representation and an exact closed-form expression are firstly derived for the corresponding average probability of detection for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving novel analytic results for the case of both collaborative detection and square-law selection diversity reception. The derived expressions have a relatively tractable algebraic representation which renders them convenient to handle both analytically and numerically. As a result, they can be utilized in quantifying the effect of fading in energy detection based spectrum sensing and in the determination of the trade-offs between sensing performance and energy efficiency in cognitive radio communications. Based on this, it is shown that the performance of the energy detector depends highly on the severity of fading as even slight variations of the fading conditions affect the value of the average probability of detection. It is also clearly shown that the detection performance improves substantially as the number of branches or collaborating users increase. This improvement is substantial in both moderate and severe fading conditions and can practically provide full compensation for the latter cases