Empirical validation and performance of duty cycle–based DTMC model in channel estimation

This paper explores the learning capability of hidden Markov model (HMM) in capturing the temporal correlation and predicting primary user (PU) activity pattern of real spectrum data of GSM-900 band through an USRP-LabVIEW platform for cognitive radio (CR) systems. The inability of the widely used s...

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Bibliographic Details
Published in:Annales des télécommunications 2020-06, Vol.75 (5-6), p.229-240
Main Authors: Bepari, Dipen, Koley, Santasri, Mitra, Debjani
Format: Article
Language:English
Subjects:
Circuits
Cognitive radio
Communications Engineering
Computer Communication Networks
Empirical analysis
Engineering
Information and Communication
Information Systems and Communication Service
Learning
Markov analysis
Markov chains
Networks
Occupancy
R & D/Technology Policy
Signal,Image and Speech Processing
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Summary:This paper explores the learning capability of hidden Markov model (HMM) in capturing the temporal correlation and predicting primary user (PU) activity pattern of real spectrum data of GSM-900 band through an USRP-LabVIEW platform for cognitive radio (CR) systems. The inability of the widely used stationary Markov model in estimating the occupancy pattern of primary channels for a long duration of time has been verified. We proposed an alternative duty cycle (DC)–based two-state discrete-time Markov chain (DTMC-DC) model. Analysis of empirical data indicates that DC required for a non-stationary DTMC-DC model can be well approximated by a trapezoidal shape and the PU spectrum usage pattern estimated using DTMC-DC is capable of learning the statistical behavior (length of idle and busy interval periods) of a real channel accurately with a reduced complexity.
ISSN:0003-4347
1958-9395
DOI:10.1007/s12243-019-00747-1