180 research outputs found
Device-Agnostic Millimeter Wave Beam Selection using Machine Learning
Most research in the area of machine learning-based user beam selection
considers a structure where the model proposes appropriate user beams. However,
this design requires a specific model for each user-device beam codebook, where
a model learned for a device with a particular codebook can not be reused for
another device with a different codebook. Moreover, this design requires
training and test samples for each antenna placement configuration/codebook.
This paper proposes a device-agnostic beam selection framework that leverages
context information to propose appropriate user beams using a generic model and
a post processing unit. The generic neural network predicts the potential
angles of arrival, and the post processing unit maps these directions to beams
based on the specific device's codebook. The proposed beam selection framework
works well for user devices with antenna configuration/codebook unseen in the
training dataset. Also, the proposed generic network has the option to be
trained with a dataset mixed of samples with different antenna
configurations/codebooks, which significantly eases the burden of effective
model training.Comment: 30 pages, 19 figures. This article was submitted to IEEE Trans.
Wirel. Commun. on Nov 14 202
Radio-frequency chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections
The next-generation wireless communications require reduced energy consumption, increased data rates and better signal coverage. The millimetre-wave frequency spectrum above 30 GHz can help fulfil the performance requirements of the next-generation mobile broadband systems. Multiple-input multiple-output technology can provide performance gains to help mitigate the increased path loss experienced at millimetre-wave frequencies compared with microwave bands. Emerging hybrid beamforming architectures can reduce the energy consumption and hardware complexity with the use of fewer radio-frequency (RF) chains. Energy efficiency is identified as a key fifth-generation metric and will have a major impact on the hybrid beamforming system design. In terms of transceiver power consumption, deactivating parts of the beamformer structure to reduce power typically leads to significant loss of spectral efficiency. Our aim is to achieve the highest energy efficiency for the millimetre-wave communications system while mitigating the resulting loss in spectral efficiency. To achieve this, we propose an optimal selection framework which activates specific RF chains that amplify the digitally beamformed signals with the analogue beamforming network. Practical precoding is considered by including the effects of user interference, noise and hardware impairments in the system modelling
- …