Low Complexity Channel estimation with Neural Network Solutions
This addresses the problem of high-speed communication demands in wireless systems, but it is incremental as it builds on existing neural network approaches with specific optimizations.
The paper tackles channel estimation for OFDM signals in wireless communications by deploying a residual convolutional neural network with a simple interpolation layer to reduce computation costs. Results on 3GPP channel models show improved mean squared error performance compared to other deep learning methods.
Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach.