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Deep learning assisted calibrated beam training for millimeter-wave communication systems

Deep learning assisted calibrated beam training for millimeter-wave communication systems
Deep learning assisted calibrated beam training for millimeter-wave communication systems
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. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, 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.
0090-6778
Ma, Ke
4a5144d2-9587-49fd-81e0-35b09b4e2f52
He, Dongxuan
7dfac860-7ce4-4f6e-b203-827d151d79d3
Sun, Hancun
52ad9a70-4bec-4bad-b4f4-49a3bfc4a2f5
Wang, Zhaocheng
bbee8d68-6240-4a32-b811-39c2abd788f5
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ma, Ke
4a5144d2-9587-49fd-81e0-35b09b4e2f52
He, Dongxuan
7dfac860-7ce4-4f6e-b203-827d151d79d3
Sun, Hancun
52ad9a70-4bec-4bad-b4f4-49a3bfc4a2f5
Wang, Zhaocheng
bbee8d68-6240-4a32-b811-39c2abd788f5
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Ma, Ke, He, Dongxuan, Sun, Hancun, Wang, Zhaocheng and Chen, Sheng (2021) Deep learning assisted calibrated beam training for millimeter-wave communication systems. IEEE Transactions on Communications. (In Press)

Record type: Article

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. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, 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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Accepted/In Press date: 13 July 2021

Identifiers

Local EPrints ID: 451261
URI: http://eprints.soton.ac.uk/id/eprint/451261
ISSN: 0090-6778
PURE UUID: d03c8ff0-16c0-4f28-adb9-6da69b1309bf

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Date deposited: 15 Sep 2021 16:31
Last modified: 15 Sep 2021 16:31

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