Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave
Communication Systems
- URL: http://arxiv.org/abs/2101.05206v2
- Date: Fri, 22 Jan 2021 00:53:33 GMT
- Title: Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave
Communication Systems
- Authors: Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang, Sheng Chen
- Abstract summary: Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications.
We propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage.
To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly.
- Score: 15.297530726877786
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Huge overhead of beam training imposes a significant challenge in
millimeter-wave (mmWave) wireless communications. To address this issue, in
this paper, we propose a wide beam based training approach to calibrate the
narrow beam direction according to the channel power leakage. To handle the
complex nonlinear properties of the channel power leakage, deep learning is
utilized to predict the optimal narrow beam directly. Specifically, three deep
learning assisted calibrated beam training schemes are proposed. The first
scheme adopts convolution neural network to implement the prediction based on
the instantaneous received signals of wide beam training. We also perform the
additional narrow beam training based on the predicted probabilities for
further beam direction calibrations. The second scheme adopts long-short term
memory (LSTM) network for tracking the movement of users and calibrating the
beam direction according to the received signals of prior beam training, in
order to enhance the robustness to noise. To further reduce the overhead of
wide beam training, our third scheme, an adaptive beam training strategy,
selects partial wide beams to be trained based on the prior received signals.
Two criteria, namely, optimal neighboring criterion and maximum probability
criterion, are designed for the selection. Furthermore, to handle mobile
scenarios, auxiliary LSTM is introduced to calibrate the directions of the
selected wide beams more precisely. Simulation results demonstrate that our
proposed schemes achieve significantly higher beamforming gain with smaller
beam training overhead compared with the conventional and existing
deep-learning based counterparts.
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