LIDAR and Position-Aided mmWave Beam Selection With Non-Local CNNs and Curriculum Training

Author:

Zecchin Matteo1ORCID,Mashhadi Mahdi Boloursaz2ORCID,Jankowski Mikolaj3ORCID,Gunduz Deniz3ORCID,Kountouris Marios1ORCID,Gesbert David1

Affiliation:

1. Communication Systems Department, EURECOM, Sophia-Antipolis, France

2. 5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, Guildford, U.K.

3. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.

Funder

Marie Sklodowska Curie action WINDMILL

European Research Council

Huawei France-funded Chair towards Future Wireless Networks

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Reference32 articles.

1. Learning both weights and connections for efficient neural network;han;Proc Adv Neural Inf Process Syst,0

2. Optimal brain damage;lecun;Proc Adv Neural Inf Process Syst,0

3. Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

4. Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels

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