TR2019-134

Deep Learning Assisted User Identification in Massive Machine-Type Communications


    •  Liu, B., Wei, Z., Yuan, J., Pajovic, M., "Deep Learning Assisted User Identification in Massive Machine-Type Communications", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9014177, December 2019, pp. 1-6.
      BibTeX TR2019-134 PDF
      • @inproceedings{Liu2019dec,
      • author = {Liu, Bryan and Wei, Zhiqiang and Yuan, Jinhong and Pajovic, Milutin},
      • title = {Deep Learning Assisted User Identification in Massive Machine-Type Communications},
      • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
      • year = 2019,
      • pages = {1--6},
      • month = dec,
      • doi = {10.1109/GLOBECOM38437.2019.9014177},
      • url = {https://www.merl.com/publications/TR2019-134}
      • }
  • Research Areas:

    Communications, Machine Learning, Signal Processing

In this paper, we propose a deep learning aided list approximate message passing (AMP) algorithm to further improve the user identification performance in massive machine type communications. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round of the AMP algorithm. The neural network returns the false alarm likelihood and it is expected to learn the unknown features of the false alarm event and the implicit correlation structure in the quantized pilot matrix. Then, via employing the idea of list decoding in the field of error control coding, we propose to enforce the suspicious device to be inactive in every iteration of the AMP algorithm in the second round. The proposed scheme can effectively combat the interference caused by the suspicious device and thus improve the user identification performance. Simulations demonstrate that the proposed algorithm improves the mean squared error performance of recovering the sparse unknown signals in comparison to the conventional AMP algorithm with the minimum mean squared error denoiser.

 

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      Date: December 9, 2019 - December 13, 2019
      Where: Waikoloa, Hawaii, USA
      MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Rui Ma; Philip Orlik; Pu (Perry) Wang
      Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information Security
      Brief
      • MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.

        GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
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