Spectrum prediction using hidden Markov models for industrial cognitive radio

• Published in: 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) pp. 1 - 7
• Main Authors: Saad, Ahmad, Staehle, Barbara, Knorr, Rudi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2016
• Subjects: Cognitive Radio; Hardware; Hidden Markov models; Industrial Communication; Prediction algorithms; Quality of service; Sensors; Spectrum Prediction; Wireless communication; Wireless sensor networks
• DOI: 10.1109/WiMOB.2016.7763231

Cognitive radio (CR) is a key enabler of wireless in industrial applications especially for those with strict quality-of-service (QoS) requirements. The cornerstone of CR is spectrum occupancy prediction that enables agile and proactive spectrum access and efficient utilization of spectral resources. Hidden Markov Models (HMM) provide powerful and flexible tools for statistical spectrum prediction. In this paper we introduce a HMM-based spectrum prediction algorithm for industrial applications that accurately predicts multiple slots in the future. Traditional HMM prediction approaches use two hidden states enabling the prediction of only one step ahead in the future. This one step is most often not enough due to internal hardware delays that render it outdated. We show in this work that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification approach enables a prediction span of multiple slots in the future even with fine spectrum sensing resolution. We verify the suitability of our approach to industrial wireless through extensive simulations that utilize a realistic measurement-based traffic model specifically tailored for industrial automotive settings.