Number of Users Detection in Multi-Point FSOC Using Unsupervised Machine Learning

Multi-point free-space optical communication (FSOC) has recently received extensive interest as a valuable solution for providing increased capacity in the upcoming communication networks. Diverse optical multiple access control (O-MAC) techniques have proved to successfully support multi-user FSOC. However, to adaptively and automatically select the most fitting O-MAC technique or combination thereof for signal demodulation, the number of transmitting users should be known a priori at the receiver side. In this letter, a technique based on histogram peak detection and unsupervised machine learning (e.g., k-mean, k-medoid, hierarchical, and fuzzy clustering), is proposed and experimentally demonstrated for number of users detection. The proposed methodology relies on amplitude information of received signals. Results show that when multiple users' signals are received with equal amplitude, under-estimation of user number was observed; hence, a modified approach-a weighted clustering-was employed and experimentally validated. The work reported herein demonstrated the ability of the proposed methodology to simultaneously and accurately detect multiple transmitting users in real time and under severe atmospheric turbulence.