Deep Learning-Based Spectrum Management to Enhance the Performance of Cognitive Radio Network Using MobileNet

References

[1] Sairam, M. V. S ; Raju Egala ; Killada Sai Nohith. "Deep Learning Framework for Enhancing the Performance of Cognitive Radio Network" Journal of Emerging Technologies and Innovative Research (JETIR) Volume 11 Issue 11 2024 Page a425–a429.

[2] Mitola, J ; G. Q. Maguire ; "Cognitive Radio: Making Software Radios More Personal " IEEE Personal Communications, Volume 6, Issue 4, 1999 Page 13–18

[3] Arjoune, Y ; N. Kaabouch ; "A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions" Sensors (Basel) 2019, https://doi.org/10.3390/s19010126.

[4] Sairam, Metta Venkata Satya; Mupparaju Sivaparvathi. "Reduction of Reporting Time for Throughput Enhancement in Cooperative Spectrum Sensing-Based Cognitive Radio" Network, Volume 164, 2017, https://www.inass.org/2018/2018022817.pdf

[5] Hanumanthu Rajasekhar; M.V.S. Sairam ; Raju Egala. "SLM-BASED PAPR REDUCTION IN OFDM SYSTEM USING FOUR DISTINCT MATRICES" IJRAR - International Journal of Research and Analytical Reviews (IJRAR) Volume 11 Issue 4 2024 Page 276-282 http://www.ijrar.org/IJRAR24D2168.pdf

[6] Howard, A., et al. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications " ArXiv, abs/1704.04861 2017 Page 1–9.

[7] Egala, Raju ; M. V. S. Sairam. "A Review on Medical Image Analysis Using Deep Learning" Engineering Proceedings, Volume 66, Issue 1 2024, Page 7, https://doi.org/10.3390/engproc2024066007.

[8] Daldal, N ; Ö. Y1ld1r1m ; K. Polat. "Deep Long Short-Term Memory Networks-Based Automatic Recognition of Six Different Digital Modulation Types under Varying Noise Conditions Neural Computing and Applications, Volume 2 2019 Page 1967 1981, https://doi.org/10.1007/s00521-019-04261-2.

[9] Huynh-The T., et al. "Learning Constellation Map with Deep Q” Neural Computing and Applications Volume 33, 2021, Page 7723–7745, https://doi.org/10.1007/s00521-020-05514-1.

[10] Doan, V. S. "CNN for Accurate Modulation Recognition " Proceedings of the IEEE Global Communications Conference GLOBECOM 2020 https://doi.org/10.1109/GLOBECOM42002.2020.9348129.

[11] Peng, S., et al. "Modulation Classification Based on Signal Constellation Diagrams and Deep Learning " IEEE Transactions on Neural Networks and Learning Systems Volume 30 2019 Page 718–727 https://doi.org/10.1109/TNNLS.2018.2850703.

[12] Chen, S., et al. "Spectrum Sensing in Cognitive Radio Networks: Requirements, Challenges, and Design Trade-Offs " IEEE Communications Magazine Volume 49, Issue 3 2011 Page 79–85

[13] Chen, Y., et al. "Fuzzy Logic-Based Dynamic Spectrum Access in Cognitive Radio Networks” Wireless Personal Communications Volume 63 Issue 2 2012 Page 331–346

[14] Kim, Y., and K. K. Loo. "Fuzzy-Based Spectrum Sensing in Cognitive Radio Networks" 2011 IEEE Symposium on Computers and Communications (ISCC) IEEE 2011 Page 173–178.

[15] Wang, T., et al. "Deep Learning-Based Modulation Recognition with Multi-Cue Fusion" IEEE Wireless Communications Letters Volume 10 2021 Page 1757–1760 https://doi.org/10.1109/LWC.2021.3078878.

[16] Suchetha N V ; Arjun K ; Panchami B S . "CNN-Driven Plant Species Recognition System" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 358-366

[17] Shi, F. Y., et al. "Combining Neural Networks for Modulation Recognition" Digital Signal Processing: A Review Journal Volume 120 2022 Page 103264 https://doi.org/10.1016/j.dsp.2021.103264.

[18] Rahman, A. A ; Biswal, B ; Hasan, S ; Sairam, M. V. S. “Robust segmentation of vascular network using deeply cascaded AReN-UNet” Biomedical Signal Processing and Control Volume 69 2021 Page 102953 https://doi.org/10.1016/j.bspc.2021.102953

[19] Pavani P, G ; Biswal, B ; Sairam, M. V. S ;Biswal, P. K. “An exclusive disjunction based detection of neovascularisation using multi scale CNN IET Image Processing Volume 15 issue 7 2021 Page 1518-1529. https://doi.org/10.1049/ipr2.12122

[20] Gulli, A ; S. Pal. Deep Learning with Keras Packt Publishing, 2017.

[21] Haykin, S ; M. Moher. “An Introduction to Analog and Digital Communications” 2nd ed., John Wiley & Sons, Inc., 2007.

[22] Khan, M. A ; F. Algarni "A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS" IEEE Access Volume 8 2020 Page 122259–122269 https://doi.org/10.1109/ACCESS.2020.3006424.

[23] https://www.kaggle.com/code/sainohithkillada/modulations-4 uploaded on 27 November 2024

[24] https://github.com/Nohith222/modulations-4-/blob/main/modulations(4). *+mno|}‰ŠŒ¡¢¤¶·¸¹ºÅ ôåÙåÍ·«ž«”‡|”o|cUI=Ih i¹hwë6�OJQJh i¹h²$·6�OJQJh i¹h²$·6�H*OJQJhú%”h^~ H*OJQJhú%”h i¹OJPJQJh i¹H*OJPJQJh°Eh i¹OJPJQJh i¹OJPJQJh²$·h i¹OJPJQJh²$·h i¹H*OJQJh²$·h i¹OJQJh^~ hÌdœOJQJh^~ CJ(OJPJQJhÌdœCJ(OJPJQJh²$·hÌdœCJ(OJPJQJhyCJ(OJPJQJno¸ . ¨ i j k l ¤¥*+òòÝÈÈÈÃÈÈȵ««««”«$ &F„7„Éý¤^„7`„Éýa$gd i¹m$ $¤a$gd i¹ [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[25] „ñÿdð¤]„ñÿgd i¹gd°E$„ñÿ„ dð¤]„ñÿ^„ a$gd9\¿$„`„öÿdð¤^„``„öÿa$gd9\¿$dð¤a$gd9\¿ - . / 0 E § ¨ ª h i j k l óçÛÍçÁçÛ±¥—‰}q}aUI:h^~ h˜gCJOJQJaJh²$·CJOJQJaJhú%”CJOJQJaJh°Ehú%”6�CJOJQJaJh i¹hÛ 6�OJQJh i¹hü½6�OJQJh i¹h°E6�H*OJQJh i¹h°E6�CJPJaJh i¹h°E6�CJaJh i¹h°E6�CJH*PJaJh i¹hwë6�OJQJh i¹h²$·6�H*OJQJh i¹h^~ 6�OJQJh i¹h²$·6�OJQJh i¹h×"36�OJQJl t v � ‘ œ ± Á Ê Û î ÷ HQyƒ†¦ ÍÕô [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1706640,
      author = {M. V. S. Sairam, Raju Egala, Hanumanthu Rajasekhar, Killada Sai Nohith},
      title = {Deep Learning-Based Spectrum Management to Enhance the Performance of Cognitive Radio Network Using MobileNet},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {274-279},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706640.pdf},
      abstract = {Cognitive radio (CR) is a leading-edge technology in fifth-generation (5G) network. CR network (CRN) performance can be augmented by effective implementation of spectrum management, which is a decisive function. Signal classification plays a critical role in enhancing spectrum management. Deep learning-based spectrum management (DLSM) is a transformative technology to enhance the performance of CRN. The present work proposes a DLSM using a predefined convolutional neural network (CNN) architecture, MobileNet. The proposed DLSM was developed using a dataset with 1000 constellation diagrams of several digital modulation schemes at a signal to noise ratio (SNR) = 10 dB. The dataset was divided in to 60% for training, 20% for validation, and 20% for testing. In the proposed DL model, the dataset is pre-processed, and feature extraction is conducted using convolution layers; then, classification of images is accomplished using fully connected layers. The results outperformed with 89.4% accuracy, 90% precision, 89% recall, and 89% F1 score. The proposed DLSM exhibits substantial waveform classification performance; hence, it can be recommended for spectrum management in CRN. The dataset was generated and the work implemented using the open-source Python programming language and the licensed Colab Pro platform.},
      keywords = {Cognitive radio network, deep learning- based spectrum sensing, CNN, accuracy, confusion matrix, AUC.},
      month = {December},
  }