Lazy Learning-Based Self-Interference Cancellation for Wireless Communication Systems With In-Band Full-Duplex Operations

Abstract

To further improve spectral efficiency for current wireless communication systems, we propose a new lazy learning-based cancellation approach to suppress self-interference (SI) sent from a base station and enable in-band full-duplex (IBFD) transmissions in cellular networks. Differ from the existing IBFD systems based on traditional approaches, our proposal consists of two phases: an offline phase for database generation and an online phase for data transmission. In the offline phase, the output before a 0/1 decision is previously measured without the desired signal input and recorded to a database with self-defined feature vectors (FVs). In the online phase, for the same system architecture with the desired signal input, a suitable result is sought from the generated database with the help of a learning method and the use of FV; then, the result is assigned as a value of SI cancellation. Computer simulation results indicate that the proposed cancellation approaches can achieve about 134 dB SI suppression and thus enables the IBFD transmissions in the considered systems.