Photonic-Assisted Adaptive Multipath Self-Interference Cancellation Using Deep Reinforcement Learning
Xiao Yu, Jia Ye, Lianshan Yan, Tao Zhou, Yue Zhu, Peng Li, Xihua Zou, Wei Pan
Abstract
Multipath self-interference (MSI) between the transmitter and receiver is an inevitable problem in realistic in-band full-duplex (IBFD) transmissions. Due to the dynamic and intricate MSI channels, it is crucial for the MSI cancellation system to quickly obtain the optimal configuration of multi-dimensional parameters. To overcome this problem, a photonics-assisted adaptive MSI cancellation scheme using deep reinforcement learning (DRL) is proposed and experimentally demonstrated. Multiple local reference signals are obtained using optical wavelength division multiplexing to achieve MSI cancellation. An adaptive algorithm based on DRL is applied for adaptive optimization of multi-dimensional optical parameters to achieve real-time MSI cancellation. To manipulate the amplitude and time delay of the multiple-path local reference signals, a multipath optical tunable delay line (MOTDL) module is introduced. A proof-of-concept experiment was carried out to verify the feasibility of the proposed scheme. By superimposing three-path local reference signals, a MSI cancellation depth of 24 dB with a bandwidth of 1 GHz was achieved. After undergoing random exploration, the adaptive algorithm learns the state relationship between the MSI and multiple-path local reference signal. This facilitates achieving optimal cancellation within just 5 steps from most random starting point. In the experiment, a 16-QAM OFDM signal with a bandwidth of 600 MHz was successfully recovered from the MSI signal with a 1 GHz bandwidth in an IBFD transmission. Furthermore, the adaptive capability of the proposed algorithm is also validated in response to varying MSI conditions.