Autonomous driving paper index
Deep Neural Network-based Residual Self- interference Cancellation Method for In-band Full- duplex Underwater Acoustic Communications
One-line summary
We propose a deep neural network-based residual self-interference cancellation (DNN-RSIC) method to handle residual self-interference after analog or spatial domain SIC.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In-band full-duplex underwater acoustic communication (IBFD-UWAC) provides a reliable solution to improve the utilization of the underwater spectrum. An effective self-interference cancellation (SIC) method leads to a better bit error rate (BER) performance. We propose a deep neural network-based residual self-interference cancellation (DNN-RSIC) method to handle residual self-interference after analog or spatial domain SIC. The estimated self-interference channel in the current environment is obtained by training the neural network so that the estimated self-interference signal can be deleted from the received signal to achieve SIC. We show the enhanced performance of the DNN-RSIC through simulations and experiments.
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