Cooperative Soft Fusion for HMM-Based Spectrum Occupancy Prediction

Spectrum occupancy prediction allows cognitive radio secondary users to exploit temporal spectrum opportunities one step-ahead. Temporal correlations in spectrum sensing measurements can be utilized to predict primary user activity patterns. Where applicable, cooperative spectrum prediction has the potential to improve prediction accuracy compared to single user (local) spectrum prediction. This letter presents the concept and methods for soft fusion-based cooperative spectrum occupancy prediction. The proposed methods were simulated and the results show significant improvement in prediction error over local, and hard fusion-based spectrum prediction.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  Robin J. Evans,et al.  HMM based cooperative spectrum occupancy prediction using hard fusion , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[3]  Andrea Giorgetti,et al.  Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation, and Localization , 2012 .

[4]  Taieb Znati,et al.  Cooperative Spectrum Prediction in Multi-PU Multi-SU Cognitive Radio Networks , 2014, Mob. Networks Appl..

[5]  Robin J. Evans,et al.  Statistical spectrum occupancy prediction for dynamic spectrum access: a classification , 2018, EURASIP Journal on Wireless Communications and Networking.

[6]  Geoffrey Ye Li,et al.  Cognitive radio networking and communications: an overview , 2011, IEEE Transactions on Vehicular Technology.

[7]  Robin J. Evans,et al.  Spectrum occupancy prediction using a Hidden Markov Model , 2015, 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS).

[8]  Walid Saad,et al.  A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.

[9]  Andrea J. Goldsmith,et al.  Capacity of Finite State Channels Based on Lyapunov Exponents of Random Matrices , 2006, IEEE Transactions on Information Theory.

[10]  Hai Jiang,et al.  Energy Detection for Spectrum Sensing in Cognitive Radio , 2014, SpringerBriefs in Computer Science.

[11]  Wei Cheng,et al.  Spectrum prediction in cognitive radio networks , 2013, IEEE Wireless Communications.

[12]  Yasir Saleem,et al.  Primary radio user activity models for cognitive radio networks: A survey , 2014, J. Netw. Comput. Appl..