solidot新版网站常见问题,请点击这里查看。
消息
本文已被查看157次
Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network. (arXiv:1704.06500v1 [cs.IT])
来源于:arXiv
In this paper, we propose a learning-based low-overhead channel estimation
method for coordinated beamforming in ultra-dense networks. We first show
through simulation that the channel state information (CSI) of geographically
separated base stations (BSs) exhibits strong non-linear correlations in terms
of mutual information. This finding enables us to adopt a novel learning-based
approach to remotely infer the quality of different beamforming patterns at a
dense-layer BS based on the CSI of an umbrella control-layer BS. The proposed
scheme can reduce channel acquisition overhead by replacing pilot-aided channel
estimation with the online inference from an artificial neural network, which
is fitted offline. Moreover, we propose to exploit joint learning of multiple
CBSs and involve more candidate beam patterns to obtain better performance.
Simulation results based on stochastic ray-tracing channel models show that the
proposed scheme can reach an accuracy of 99.74% in settings with 20 查看全文>>