Robust and efficient beam training scheme for millimetre wave indoor communications

Kutty, Shajahan

doi:10.1049/iet-com.2017.0378

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…For example, references [5,6] enhance the coverage of mmW signals in NLOS propagation scenarios by installing a metallic reflector. Reference [7] shows that antenna beamforming technology can effectively compensate for propagation loss. Besides, dynamic beam search and tracking algorithms have also been introduced in [8,9] to reduce NLOS errors.…”

Section: Introductionmentioning

confidence: 99%

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

Fan

et al. 2022

IET Communications

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Line‐of‐sight (LOS)/non‐line‐of‐sight (NLOS) identification is crucial for millimetre‐wave (mmW) since it is vulnerable to blocking in the NLOS environment. In this paper, an LOS/NLOS environment identification method is proposed based on angle information learning. Specifically, a neural network named ALNN (angle information learning neural network) is employed, which can learn the difference between the various angle paraments to identify the LOS and NLOS environments for mmW wireless communication systems. First, the 3D mmW channel model is applied to extract the desired angle information. Moreover, an mmW wireless transmission system is constructed to introduce actual measurement data to test the proposed method. Furthermore, various hyperparameters of the ALNN are adjusted to improve identification accuracy. Simulation results show that the proposed method has outstanding identification performance, and the identification accuracy is up to 99.41%.

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Section: Introductionmentioning

confidence: 99%

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

Fan

et al. 2022

IET Communications

View full text Add to dashboard Cite

show abstract

Robust and efficient beam training scheme for millimetre wave indoor communications

Cited by 1 publication

References 18 publications

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

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