Free Space Optical (FSO) communication is promising to provide a wireless connection of tens to hundreds of gigabit capacity. Despite of many advantages, FSO link is sensitive to weather conditions and atmospheric turbulence. To offset these limitations, using Radio Frequency (RF) link as a backup link is a wise choice and thus hybrid FSO/RF system gains wider attention. Research on link switching is of great importance as a key technology in hybrid FSO/RF system. It is well known that link switching is closely related to the Received Signal Strength Indication (RSSI). In this letter, by combining power control and link switching using Recurrent Neural Networks (RNN) to predict RSSI, we propose an intelligent power control and link switching scheme to enhance transmission effectiveness in hybrid FSO/RF system. We set up a testbed of hybrid FSO/RF system and experimentally validate the proposed scheme. Experimental results demonstrate that the proposed scheme can forecast the trend of RSSI accurately and the power control strategy can effectively improve transmission quality of FSO link by reducing the frequency of switching.

Hybrid free space optical (FSO)/radio frequency (RF) system has emerged as a high-data-rate solution for the last mile access network. In this paper, we establish a hybrid FSO/RF testbed including an intensity modulation/direct detection (IM/DD) FSO subsystem with space diversity and a high-order quadrature amplitude modulation (QAM) RF subsystem with polarization diversity, respectively. Based on the testbed, we propose and experimentally validate an intelligent transmission system where the link selection and switching technology is implemented. In particular, we propose a gated recurrent unit (GRU) neural network enhanced by time attention mechanism for the hybrid system. The experimental results demonstrate that the proposed system can achieve the prediction of FSO channel fading with a high precision where absolute percentage error (APE) values lower than 3% account for up to 90% of the prediction results. The implemented intelligent hybrid system can significantly reduce the link switching frequency and link interruption duration. Then the BER is further verified on the testbed, and results show that the intelligent hybrid system can improve the BER from the order of 10-2 to the order of 10-3 during the time periods when link switching occurs.

In this paper, we propose a method to encrypt the permutation information (PI) and insert the synchronization head based on digital chaos and constellation shaping for orthogonal frequency division multiplexing-free space optical (OFDM-FSO) communication. Probabilistic shaping technology can improve laser emission efficiency, but it will generate the PI, which, transmitted separately, will occupy channel bandwidth. Therefore, we use a digital chaotic algorithm to encrypt the PI and insert it into the synchronization head. This processing reduces the occupation of the PI on communication bandwidth and improves the security of the transmission system. The PI can be converted into a binary bit stream and encrypted by the pseudo-random binary sequence (PRBS) generated by a 1-dimensional chaotic map amplifier (1-DCMA). In the case of regional probabilistic shaping, it can improve by 3.8-10% transmission distance. The results show that the proposed method has about 0.8-1 dB improvement in the received optical power higher than the original model. The synchronization head assisted the chaotic PI can not only enhance security but also improve the performance of transmission bit error rate (BER).

Free space optical communication (FSO) has been becoming one of the main directions of future communication network due to its advantages of high transmission rate, small divergence angle, and strong confidentiality. The acquisition, tracking, pointing (ATP) sub-system is an important guarantee for stable communication of FSO system. In the AI-enabled ATP system, data augmentation algorithms are effective to reduce the size of the dataset for spot prediction, and thus to lower the system structural complexity. In this paper, we propose a novel data augmentation algorithm that introduces wavelet transform into the fusing-and-filling generative adversarial network (F2GAN). The wavelet transform is utilized to convert the image information from time domain to frequency domain. The diversity of the generated images obtained from the GAN model is thus enhanced with the richer details. The experimental results show that, compared to the F2GAN algorithm, the proposed algorithm can improve peak signal to noise ratio by 14.5%, structure similarity index measure by 11.8%, and learned perceptual image patch similarity by 21%, respectively.

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, the positioning system can only achieve two-dimensional (2D) positioning and requires the assistance of inertial measurement units (IMU). When the LED is not captured or decoding fails, the system’s positioning error increases further. Thus, we propose a novel three-dimensional (3D) visible light positioning system based on image sensors for various environments. Specifically, (1) we use IMU to obtain the receiver’s state and calculate the receiver’s 2D position. Then, we fit the height–size curve to calculate the receiver’s height, avoiding the coordinate iteration error in traditional 3D positioning methods. (2) When no LED or decoding fails, we propose a firefly-assisted unscented particle filter (FA-UPF) algorithm to predict the receiver’s position, achieving high-precision dynamic positioning. The experimental results show that the system positioning error under a single LED is within 10 cm, and the average positioning error through FA-UPF under no light source is 6.45 cm.

Recently, with the development of communication and positioning technologies, progressively higher demands have been placed on the timeliness of indoor communication and the effectiveness of indoor positioning. This paper proposes a low-cost and full-time domain coverage indoor communication and localization fusion system that uses an infrared camera to identify mobile LEDs based on region of interest optical camera communication (RoI-OCC) and calculate three-dimensional (3D) world coordinates based on perspective-n-point (PnP) algorithm. An intermediate delimiter modulation-demodulation method is designed to achieve asynchronous OCC. In processing infrared images, a region-growth-based absolute-directional-mean-difference (RG-ADMD) algorithm is developed to detect infrared targets and recover the scale. Additionally, a local-contrast-method-based Lucas-Kanade (LCM-LK) optical flow estimation algorithm is designed to track infrared targets and determine the centroid coordinates of pixels. In a 5×5 m experimental area, the established system can achieve a target detection recall rate of over 85%, the asynchronous OCC with error-free transmission and an average 3D positioning accuracy of 2.01 cm under a LCM-LK tracking algorithm.