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  • 1.
    Saxena, Vidit
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden.;Ericsson Res, Stockholm, Sweden..
    Jaldén, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..
    Tullberg, Hugo
    Ericsson Res, Stockholm, Sweden..
    DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 6658-6662Conference paper (Refereed)
    Abstract [en]

    In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs/NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.

  • 2. Saxena, Vidit V.
    et al.
    Feldt, Tommy
    KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
    Goel, M.
    Augmented telepresence as a tool for immersive simulated dancing in experience and learning2014In: ACM International Conference Proceeding Series, ACM Digital Library, 2014, p. 86-89Conference paper (Refereed)
    Abstract [en]

    The paper explores the use of interaction technologies in the domain of dance and attempts to visualize a future tool to complement current applications. It begins with a review of various tools and technologies that have been used within the domain in the past and make a projection for how interaction technologies could develop in the coming decade. It then presents a conceptual tool for simulated dancing - 'disDans', which utilizes the modalities of touch, vision and hearing in order to provide an immersive experience. It allows multiple users to touch and feel each other while dancing together, without having to be physically present in the same space. In the end the paper discusses some challenges and limitations to the proposal.

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