Abstract:
The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary
user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been
examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South
Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was
employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR
performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive
channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU
disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density
conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement
campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for
prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.
