A Neural-Network-Based Non-linear Interference Cancellation Scheme for Wireless IoT Backhaul with Dual-Connectivity

In this paper, we consider an Internet of Things (IoT) wireless network using Long Term Evolution (LTE) cellular system as backhaul. To provide high throughput, by using dual-connectivity technique, the IoT gateway simultaneously connects to two evolved Node Bs (eNBs) on two carriers, one for downlink and the other for uplink. As a result, the receive link will be severely interfered by the harmonic interference (HI) and inter-modulation (IM) components caused by the imperfections of power amplifier (PA) and in-phase/quadrature (I/Q) modulator. To solve this problem, in this paper, an neural-network (NN)based non-linear interference cancellation scheme is proposed for dual-connectivity IoT gateway. In the proposed scheme, the nonlinear interference is first reconstructed by using the transmit signal and the trained NN in baseband, and then subtracted from the received signal in digital domain at receiver. The NN precisely models the link behavior from the baseband transmitter to the baseband receiver, including all the linear and non-linear effect. Additionally, the NN can be used to reconstruct and cancel not only the HI, but also the IM components of the mirror-frequency interference (MFI) caused by I/Q imbalance, and direct current (DC) bias caused by local oscillator (LO) leakage. To evaluate the performance of the proposed scheme, a hardware prototype is designed and implemented. Experimental results show that the proposed scheme has a superior performance in dual-connectivity system compared with the traditional non-linear interference cancellation scheme using polynomial (PM) model.