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

• Published in: IEEE communications letters Vol. 22; no. 12; pp. 2627 - 2630
• Main Authors: Gao, Xuanxuan, Jin, Shi, Wen, Chao-Kai, Li, Geoffrey Ye
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel estimation; Computer memory; Computer simulation; Data recovery; Deep learning; Frequency division multiplexing; Learning data; Machine learning; OFDM; Orthogonal Frequency Division Multiplexing; Receivers; Signal detection; Signal processing; Signal to noise ratio; Wireless communication; Wireless communications; Wireless networks
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2018.2877965

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.