  With the crowding of the electromagnetic spectrum and the shrinking cell size
in wireless networks, crosstalk between base stations and users is a major
problem. Although hand-crafted functional blocks and coding schemes are proven
effective to guarantee reliable data transfer, currently deep learning-based
approaches have drawn increasing attention in the communication system
modeling. In this paper, we propose a Neural Network (NN) based signal
processing technique that works with traditional DSP algorithms to overcome the
interference problem in realtime. This technique doesn't require any feedback
protocol between the receiver and transmitter which makes it very suitable for
low-latency and high data-rate applications such as autonomy and augmented
reality. While there has been recent work on the use of Reinforcement Learning
(RL) in the control layer to manage and control the interference, our approach
is novel in the sense that it introduces a neural network for signal processing
at baseband data rate and in the physical layer. We demonstrate this "Deep
Interference Cancellation" technique using a convolutional LSTM autoencoder.
When applied to QAM-OFDM modulated data, the network produces significant
improvement in the symbol error rate (SER). We further discuss the hardware
implementation including latency, power consumption, memory requirements, and
chip area.
