A Lazy Learning-Based Self-Interference Cancellation Approach for In-Band Full-Duplex Wireless Communication Systems

We propose a new lazy learning-based cancellation approach to improve spectral efficiency for current wireless communication systems, suppress self-interference (SI) sent from base stations, and enable in-band full-duplex (IBFD) transmissions in cellular networks. Our proposed approach consists of two phases based on traditional IBFD systems: 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 premeasured without the desired signal input and recorded in a database with self-defined feature vectors (FVs). In the online phase, a suitable result is sought from the generated database with the help of a learning method and FV for the same system architecture with the desired signal input. The result is then assigned an SI cancellation value. Regular and eager learning-based cancellation approaches are employed to evaluate the proposed method and simulate the transmission output. Computer simulation results indicated that the proposed cancellation methods could achieve about 134 dB SI suppression and achieve nearly the same transmission levels as methods with no SI effect, enabling the IBFD operations in wireless communication systems better than the regular and eager learning-based techniques.