Details

Autor(en) / Beteiligte

• Gao, Xuanxuan
• Jin, Shi
• Wen, Chao-Kai
• Li, Geoffrey Ye

Titel

ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers

Ist Teil von

• IEEE communications letters, 2018-12, Vol.22 (12), p.2627-2630

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2018

Quelle

IEL

Beschreibungen/Notizen

• In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.