Outdated channel state information (CSI) has a severe impact on a wide variety of wireless techniques. Making use of artificial intelligence, a multi-antenna channel predictor is proposed in this paper. It can accurately forecast the future CSIs up to tens of symbols ahead in a fast fading channel. By tuning the number of neurons in the input and output layers of neural network (NN) in terms of the number of antennas, it congenitally suits a multi-antenna system. Relying on a NN with real-valued weights, this predictor is simpler but substantially outperforms existing complex-valued NN predictors.


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    Title :

    Multi-Antenna Fading Channel Prediction Empowered by Artificial Intelligence


    Contributors:


    Publication date :

    2018-08-01


    Size :

    639361 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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