Performance analysis of random neural networks in LTE-UL of a cognitive radio system

Ahsan Adeel, Hadi Larijani, Ali Ahmadinia

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

6 Citations (Scopus)


In cognitive radio networks (CRNs), the cognitive Engine (CE) is responsible for decision making. This is quite a challenging task as it requires finding the balance between prediction accuracy and efficient learning for optimal configuration settings for the CRN. Artificial neural networks (ANNs) have been widely used as predictive tools in cognitive radio. In this paper, random neural networks (RNNs) have been proposed to achieve better generalization and to speed up the cognition process in LTE (Long Term Evolution) cognitive-eNodeB. The developed CE is characterizing the achievable communication performance (throughput) of available configuration settings and suggesting the optimal radio parameters for specific service demand. Furthermore, the RNN-CE is coordinating the inter-cell-interference by suggesting the acceptable transmit power of adjacent channel users. Performance evaluation has revealed 42.85% better prediction accuracy (based on MSE) and 68% better learning efficiency (based on epochs required for convergent result) of RNN as compared to ANN.

Original languageEnglish
Title of host publication2014 1st International Workshop on Cognitive Cellular Systems (CCS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479941391
Publication statusPublished - 23 Oct 2014
Event1st International Workshop on Cognitive Cellular Systems - Rhine River, Germany
Duration: 2 Sept 20144 Sept 2014


Conference1st International Workshop on Cognitive Cellular Systems
Abbreviated titleIEEE CCS 2014
CityRhine River


  • Artificial neural networks
  • Interference
  • Cognitive radio
  • Neurons
  • Training
  • Accuracy

ASJC Scopus subject areas

  • Media Technology
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Performance analysis of random neural networks in LTE-UL of a cognitive radio system'. Together they form a unique fingerprint.

Cite this