Advertisement

Identification of Non-Conforming Cordless Phone Signals in Licensed Bands

  • Selen GeçgelEmail author
  • Mehmet Akif Durmaz
  • Hakan Alakoca
  • Güneş Karabulut Kurt
  • Cem Ayyıldız
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 504)

Abstract

Identification of interference signals is critical in telecommunication systems increasing the need for automated signal identification. As deep convolutional neural networks demonstrate significant achievements in pattern recognition problems, it can be inferred that deep learning methods will give successful results in the field of wireless communications, especially about identification of signal. This paper investigates a new approach of signal identification based on deep convolutional neural network with the convolution architecture for feature extraction (CAFFE) framework. Authors provide the identification for the types of interference signals based on non-conforming digital enhanced cordless telecommunications (DECT) devices. For training NVIDIA-DIGITS, the NVIDIA Deep Learning GPU Training System, is used. The classification accuracy of the system under additive white Gaussian noise and Rayleigh fading channel conditions is observed to be high despite low signal to noise ratio values.

Keywords

Deep learning Signal identification Classification Rayleigh AWGN 

References

  1. 1.
    European conference of postal and telecommunications administrations (1999). Adjacent band compatibility between UMTS and other services in the 2 GHz bandGoogle Scholar
  2. 2.
    Li J, Qi L, Lin Y (2016) Research on modulation identification of digital signals based on deep learning (2016). In: 2016 IEEE international conference on electronic information and communication technology (ICEICT), pp 402–405Google Scholar
  3. 3.
    Khan FN, Zhong K, Al-Arashi WH, Yu C, Lu C, Lau APT (2016) Modulation format identification in coherent receivers using deep machine learning. IEEE Photonics Tech Lett 28(17):1886–1889CrossRefGoogle Scholar
  4. 4.
    Zhang M, Diao M, Guo L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5:11074–11082CrossRefGoogle Scholar
  5. 5.
    Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  6. 6.
    Gardner WA (1994) Cyclostationarity in communications and signal processing. Technical report, Statistical Signal Processing Inc Yountville CaGoogle Scholar
  7. 7.
    Fehske A, Gaeddert J, Reed JH (2005) A new approach to signal classification using spectral correlation and neural networks. In: International symposium on new frontiers in dynamic spectrum access networks (DySPAN), pp 144–150Google Scholar
  8. 8.
    Davy Manuel, Gretton Arthur, Doucet Arnaud, Rayner Peter JW (2002) Optimized support vector machines for nonstationary signal classification. IEEE Signal Process Lett 9(12):442–445CrossRefGoogle Scholar
  9. 9.
    Zhou R, Li X, Yang TC, Liu Z, Wu Z (2012) Real-time cyclostationary analysis for cognitive radio via software defined radio. In: Global communications conference (GLOBECOM), pp 1495–1500Google Scholar
  10. 10.
    Bahadir Tuğrel H, Alakoca H, Tekbiyik K, Karabulut Kurt G, Ayyildiz C (2016) OFDMA system identification using cyclic autocorrelation function: a software defined radio testbed. In: Signal processing and communication systems (ICSPCS), pp 1–7Google Scholar
  11. 11.
    Lopez-Risueno G, Grajal J, Sanz-Osorio A (2005) Digital channelized receiver based on time-frequency analysis for signal interception. IEEE Trans. Aerosp Electron Syst 41(3):879–898CrossRefGoogle Scholar
  12. 12.
    Cohen L (1989) Time-frequency distributions-a review. Proc IEEE 77(7):941–981CrossRefGoogle Scholar
  13. 13.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Conference on computer vision and pattern recognition (CVPR), pp 1–9Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Selen Geçgel
    • 1
    Email author
  • Mehmet Akif Durmaz
    • 1
  • Hakan Alakoca
    • 1
  • Güneş Karabulut Kurt
    • 1
  • Cem Ayyıldız
    • 2
  1. 1.Istanbul Technical University, Wireless Communication Research Laboratory (WCRL)IstanbulTurkey
  2. 2.Turkcell Technology Research and Development LaboratoryIstanbulTurkey

Personalised recommendations