Reinforcement learning-based adaptive beam alignment in a photodiode-integrated array antenna module.

We successfully demonstrated an intelligent adaptive beam alignment scheme using a reinforcement learning (RL) algorithm integrated with an 8 × 8 photonic array antenna operating in the 40 GHz millimeter wave (MMW) band. In our proposed scheme, the three key elements of RL: state, action, and reward, are represented as the phase values in the photonic array antenna, phase changes with specified steps, and an obtained error vector magnitude (EVM) value, respectively. Furthermore, thanks to the Q-table, the RL agent can effectively choose the most suitable action based on its prior experiences.

Reinforcement learning for W-band radio-over-fiber system using a polarization modulator.

Reinforcement learning (RL) is applied to improve the performance of the polarization modulator (PolM)-based W-band radio-over-fiber (RoF) system in this Letter. By controlling the polarization angle of the dual-wavelength laser source in the PolM-based scheme, the RF response can be easily modified and therefore it hugely increases the available bandwidth in the RoF system. In the proposed RL scheme, the state is described as the value of the angle from the polarization controller (PC).

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network.

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing.