Spectrum occupancy information is neces-sary in a cognitive radio network(CRN)as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access(DSA).However,in a CRN,it is difficul...Spectrum occupancy information is neces-sary in a cognitive radio network(CRN)as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access(DSA).However,in a CRN,it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior.In this context,the spectrum occupancy predic-tion proves to be very useful in enhancing the quality of experience of the secondary user.This paper investigates the practical prowess of various time-series modeling approaches and the machine learning(ML)techniques for predicting spectrum occupancy,based on a spectrum measurement campaign conducted in Jaipur,Rajasthan,India.Moreover,the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data.Nevertheless,prediction through ML-based recurrent neural network proves to perform reasonably well,thereby providing an accurate future spectrum occupancy information for DSA.展开更多
文摘Spectrum occupancy information is neces-sary in a cognitive radio network(CRN)as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access(DSA).However,in a CRN,it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior.In this context,the spectrum occupancy predic-tion proves to be very useful in enhancing the quality of experience of the secondary user.This paper investigates the practical prowess of various time-series modeling approaches and the machine learning(ML)techniques for predicting spectrum occupancy,based on a spectrum measurement campaign conducted in Jaipur,Rajasthan,India.Moreover,the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data.Nevertheless,prediction through ML-based recurrent neural network proves to perform reasonably well,thereby providing an accurate future spectrum occupancy information for DSA.
