6,605 research outputs found

    Learning How to Demodulate from Few Pilots via Meta-Learning

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    Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data

    Impact of polarization diversity in massive MIMO for industry 4.0

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    The massive polarimetric radio channel is evaluated in an indoor industrial scenario at 3.5 GHz using a 10×10 uniform rectangular array (URA). The analysis is based on (1) propagation characteristics like the average received gain and the power to interference ratio from the Gram matrix and (2) system-oriented metrics such as sum-rate capacity with maximum-ratio transmitter (MRT). The results clearly show the impact of polarization diversity in an industrial scenario and how it can considerably improve different aspects of the system design. Results for sum-rate capacity are promising and show that the extra degree of freedom, provided by polarization diversity, can optimize the performance of a very simple precoder, the MRT
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