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Communication Dans Un Congrès Année : 2020

Enhancing Least Square Channel Estimation Using Deep Learning

Résumé

Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.
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Dates et versions

hal-02504757 , version 1 (11-03-2020)

Identifiants

  • HAL Id : hal-02504757 , version 1

Citer

Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis. Enhancing Least Square Channel Estimation Using Deep Learning. 2020 IEEE 91st Vehicular Technology Conference: VTC2020-Spring, May 2020, Antwerp, Belgium. ⟨hal-02504757⟩
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