Autonomous driving paper index
Design and Implementation of a Neural Network Aided Self-Interference Cancellation Scheme for Full-Duplex Radios
One-line summary
In this work, we present a hardware architecture for a neural network based non-linear self-interference canceller and we compare it with our own hardware implementation of a conventional polynomial based canceller.
Engineering notes
We show that, for the same cancellation performance, the neural network canceller has a significantly higher throughput and requires fewer hardware resources.
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 systems are able to transmit and receive information simultaneously on the same frequency band. Due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we present a hardware architecture for a neural network based non-linear self-interference canceller and we compare it with our own hardware implementation of a conventional polynomial based canceller. We show that, for the same cancellation performance, the neural network canceller has a significantly higher throughput and requires fewer hardware resources.
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