Zilong Jin1,2, Chengbo Zhang1
, Kan Yao3
, Dun Cao4
, Seokhoon Kim5, Yuanfeng Jin6,*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5991-6005, 2022, DOI:10.32604/cmc.2022.021498
- 11 October 2021
Abstract
Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed spectrum. However, these cognitive radio devices (secondary users) are greatly affected by the original users (primary users) of licensed channels. Cognitive users have to adjust operation parameters frequently to adapt to the dynamic network environment, which causes extra energy consumption. Energy consumption can be reduced by predicting the future activity of primary users. However, the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of
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