In-band full-duplex systems can transmit and receive information simultaneously and on the same frequency band. However, 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 describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. Our results show that, for the same SI cancellation performance, the neural network canceller has an $8.1\times $ smaller area and requires $7.7\times $ less power than the polynomial canceller. Moreover, the neural network canceller can achieve 7 dB more SI cancellation while still being $1.2\times $ smaller than the polynomial canceller and only requiring $1.3\times $ more power. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also lead to order-of-magnitude implementation complexity reductions.

中文翻译：

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神经自干扰消除的硬件实现

带内全双工系统可以在同一频带上同时发送和接收信息。但是，由于发射机对自身接收机造成的强自干扰，使用非线性数字自干扰消除是必不可少的。在这项工作中，我们描述了基于神经网络的非线性自干扰 (SI) 消除器的硬件架构，并将其与我们自己的基于多项式的传统 SI 消除器的硬件实现进行了比较。我们的结果表明，对于相同的 SI 消除性能，神经网络消除器具有 $8.1\times $ 面积较小，需要 $7.7\times $ 比多项式消除器的功率小。此外，神经网络消除器可以实现多 7 dB 的 SI 消除，同时仍然 $1.2\times $ 小于多项式对消器并且只需要 $1.3\times $ 更多的权力。这些结果表明，应用于通信的基于 NN 的方法不仅从性能角度来看是有用的，而且还可以导致实现复杂性的数量级降低。