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A Neuro-Fuzzy based detection approach for HARQ-CC in FBMC-OQAM systems

Hybrid automatic repeat request (HARQ) is a link adaptation scheme that provides high transmission reliability. However, it may suffer from distortions and nonlinearities of the transmission environment. In this paper, we propose a new deep learning approach for joint blind detection and combining of HARQ-CC packets in FBMC-OQAM systems. Our method uses inference rules that deploy a trained neural network to recover the original transmitted packet. Simulations show the effectiveness of the proposed system in terms of Block Error Rate (BLER) and root Mean Square Error (RMSE).