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pro vyhledávání: '"Luan, Dianxin"'
Autor:
Luan, Dianxin, Thompson, John
In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is empl
Externí odkaz:
http://arxiv.org/abs/2302.04368
Autor:
Luan, Dianxin, Thompson, John
In this paper, we propose a method to design the training data that can support robust generalization of trained neural networks to unseen channels. The proposed design that improves the generalization is described and analysed. It avoids the require
Externí odkaz:
http://arxiv.org/abs/2302.02302
Autor:
Luan, Dianxin, Thompson, John
In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for t
Externí odkaz:
http://arxiv.org/abs/2204.13465
Autor:
Luan, Dianxin, Thompson, John
Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed c
Externí odkaz:
http://arxiv.org/abs/2201.09934