Performance analysis of artificial neural network-based learning schemes for cognitive radio systems in LTE-UL

Ahsan Adeel, Hadi Larijani, Ali Ahmadinia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cognitive radio is widely accepted as a promising technology to intelligently manage the scarce radio resources and correspondingly select the optimal radio configurations. The process of cognition is challenging because of the trade-offs among response time, accuracy, available training samples, and NN structure complexity, which is a limiting factor for cognitive radio (CR) to achieve optimal configuration settings in real time. In this paper, a complex model of LTE uplink is analysed and a cognitive engine(CE) is introduced with ANN as an artificial intelligence technique. The CE is characterizing the achievable communication performance of all available secondary and primary users configurations. Furthermore, Suggesting the optimal radio configurations, taking into account the user requirements as well as the electromagnetic environment. Performance evaluation of the proposed ANN has revealed 60% improvement in accuracy and efficiency as compared to existing ANN models for the same parameters configurations. © 2014 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA 14)
Place of PublicationWashington, DC, USA
PublisherIEEE
Pages731-736
Number of pages6
ISBN (Print)9781479926534
DOIs
Publication statusPublished - May 2014

Keywords

  • cognitive radio (CR)
  • LTE uplink
  • cognitive engine

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