Full-Duplex Self-Interference Cancellation Using Dual-Neurons Neural Networks

In-band full-duplex (FD) technology has protruded as one of the most promising solutions for spectrum scarcity, by allowing users to transmit and receive simultaneously at the same center frequency. However, the FD systems suffer from a severe self-interference (SI) caused by the coupling between the transmit and receive antennas. Thereby, the potential of FD systems can not be attained without employing robust SI cancellation techniques. Traditionally, the SI is modeled using the polynomial-based cancelers, which are computationally expensive. Consequently, neural networks (NNs) have been recently introduced to model the SI with lower computational complexity. In this letter, a novel NN structure referred to as the dual neurons- $\ell $ hidden layers NN (DN- $\ell $ HLNN) is proposed. The DN- $\ell $ HLNN exploits two neurons in the first hidden layer to recognize the memory effect of the input and output samples with reduced complexity. The numerical simulations show that the DN- $\ell $ HLNN-based canceler significantly reduces the computational complexity and the memory requirements compared to the polynomial-and the existing NN-based cancelers while maintaining a similar non-linear cancellation performance.