  In-band full duplex wireless is of utmost interest to future wireless
communication and networking due to great potentials of spectrum efficiency.
IBFD wireless, however, is throttled by its key challenge, namely
self-interference. Therefore, effective self-interference cancellation is the
key to enable IBFD wireless. This paper proposes a real-time non-linear
self-interference cancellation solution based on deep learning. In this
solution, a self-interference channel is modeled by a deep neural network
(DNN). Synchronized self-interference channel data is first collected to train
the DNN of the self-interference channel. Afterwards, the trained DNN is used
to cancel the self-interference at a wireless node. This solution has been
implemented on a USRP SDR testbed and evaluated in real world in multiple
scenarios with various modulations in transmitting information including
numbers, texts as well as images. It results in the performance of 17dB in
digital cancellation, which is very close to the self-interference power and
nearly cancels the self-interference at a SDR node in the testbed. The solution
yields an average of 8.5% bit error rate (BER) over many scenarios and
different modulation schemes.
