  Combining millimetre-wave (mmWave) communications with an extremely
large-scale antenna array (ELAA) presents a promising avenue for meeting the
spectral efficiency demands of the future sixth generation (6G) mobile
communications. However, beam training for mmWave ELAA systems is challenged by
excessive pilot overheads as well as insufficient accuracy, as the huge
near-field codebook has to be accounted for. In this paper, inspired by the
similarity between far-field sub-6 GHz channels and near-field mmWave channels,
we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the
optimal near-field mmWave codeword, which aims to reduce pilot overhead and
bypass the channel estimation. Moreover, we adopt deep learning to perform this
dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and
a novel neural network structure called NMBEnet is designed to enhance the
precision of beam training. Specifically, when considering the orthogonal
frequency division multiplexing (OFDM) communication scenarios with high user
density, correlations arise both between signals from different users and
between signals from different subcarriers. Accordingly, the convolutional
neural network (CNN) module and graph neural network (GNN) module included in
the proposed NMBEnet can leverage these two correlations to further enhance the
precision of beam training.
