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Optimal sensing energy and sensing interval in cognitive radio networks with energy harvesting
Author(s) -
Abdelfattah Islam S.,
Rabia Sherif I.,
Abdelrazek Amr M.
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4742
Subject(s) - cognitive radio , computer science , energy (signal processing) , interval (graph theory) , channel (broadcasting) , interference (communication) , partially observable markov decision process , throughput , computer network , energy harvesting , energy consumption , markov decision process , idle , transmission (telecommunications) , markov chain , real time computing , markov process , telecommunications , markov model , wireless , machine learning , electrical engineering , statistics , mathematics , combinatorics , operating system , engineering
Summary Cognitive radio networks with energy harvesting aim to improve spectrum utilization and allow users to move freely. Cognitive radio technology enhances the utilization of the spectrum by permitting the unlicensed user (secondary user) to use the channel during the absence of the licensed user (primary user). A major issue in this approach is that the secondary user needs to sense the channel frequently, which leads to energy wasting. This problem can be dealt with using the notion of sensing interval where the channel is sensed every multiple of time slots. In this paper, we consider the problem of determining the optimal sensing energy and the optimal sensing interval that maximize the secondary user throughput and minimize both the consumed energy and the interference to the primary user. By formulating this optimization problem as a mixed observable Markov decision process, a dynamic policy for the secondary user is generated taking into account the total accumulated secondary user reward. Numerical results show that the proposed policy outperforms existing results, especially when the secondary user prefers the transmission mode to the energy‐saving mode. Additionally, the numerical results show that applying sensing interval concept for both idle and busy sensing results is better than applying it for the idle case only.