Wireless Interference Identification With Convolutional Neural Networks Based on the FPGA Implementation of the LTE Cell-Specific Reference Signal (CRS)

Abstract

Wireless Interference Identification (WII) is an important function in the context of non-cooperative spectrum coexistence management problems because interference may cause loss or degradation of service. Deep Learning (DL) has been recently introduced in spectrum coexistence problems and more specifically in WII where it has demonstrated a superior performance to shallow machine learning algorithms. This paper proposes an advancement in literature by exploiting the existing function in modern cellular networks systems for channel estimation to implement WII. In particular, a channel estimator function based on the LTE Cell Specific Reference Signal (CRS) was implemented in Field Programmable Gate Array (FPGA) by the authors and it was used to generate channel estimates, which are given as an input to a DL algorithm. This study applies Convolutional Neural Networks (CNN) on three different data sets for WII, where the victim is a LTE-plus communication system with 40 MHz bandwidth and the interferences are 1) LTE with 20 MHz bandwidth and FDD modulation, 2) LTE with 20 MHz bandwidth and TDD modulation and 3) WiFi (802.11g). This paper describes the FPGA channel estimator implementation and it performs an extensive analysis of the impact of the parameters of the proposed approach and the CNN architecture. The results show that the proposed approach outperforms other approaches based on DL and constellation diagrams or shallow machine learning algorithms.