Channel Estimation for OFDM Systems Over Doubly Selective Channels Based on CEHNet
Authors : Ruochen Wang (Sans affiliation), Biyun Ma (Sans affiliation), Jiaojiao Liu (Sans affiliation), Yuehua Ding (LINEACT), Zhiheng Zhou (Sans affiliation)
Article : Articles dans des revues internationales ou nationales avec comité de lecture - 01/09/2025 - IEEE COMMUNICATIONS LETTERS
In dynamic scenarios, time-frequency doubly selective channels challenge accurate estimation. Deep learning based method emerges as a promising way by leveraging temporal correlation and local time-frequency features characterized by wireless channels. To enhance adaptability in dynamic channels with fewer pilots, this letter proposes a novel channel estimation algorithm based on a channel-enhanced deep Horblock network
(CEHNet), where the Horblock structure is integrated into
the super-resolution convolutional neural network (SRCNN) to
capture long-range dependencies effectively. Additionally, the
autocorrelation of the channel state information (CSI) matrix,
derived from pilot signals, is fed into CEHNet in parallel, thereby emphasizing multipath delay and Doppler frequency shift
information therein. Furthermore, the incorporation of Lasso
regression accelerates network convergence. Experimental results demonstrate that the proposed algorithm outperforms baseline methods in various scenarios, achieving superior performance with fewer epochs, particularly when pilots are sparse or missing.