# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 1 Introduction  
Free-space optical (FSO) communication, known as optical wireless communication (OWC), has the advantages of a license-free spectrum, large bandwidth, flexible network and high data rate. However, FSO links are inevitably affected by atmospheric turbulence and adverse meteorological situations, including snow, fog, dust, etc. Radio frequency (RF) communication can provide a reliable link but is sensitive to rainy circumstances, especially for microwave and millimeter wave systems. To address these vulnerabilities, the past decade has witnessed growing interest in the integration of FSO link and RF link, forming hybrid FSO/RF heterogeneous networks. Such amalgamated systems aim to capitalize on the complementary strengths of both communication technologies, thereby enabling more efficient and reliable data transmission [1; 2; 3; 4]. There are two modes for using FSO/RF links: hard-switching and soft-switching. In soft switching mode, both links are active simultaneously [5; 6]. Only one link is active in hard switching mode [7]. For mixed channels, a new throughput maximization algorithm was proposed in [5] to optimize the bit rate, and the system performance of LDPC codes with regular or irregular structures was analyzed. In [6], data were simultaneously transmitted over two links at the same rate, and space diversity technology was adopted to maximize spectrum utilization and reduce the influence of the turbulence channel. Through the analysis of outage probability, ref. [7] studied the performance of a hybrid FSO/intelligent reflecting surface (IRS)-aided RF communication system based on hard switching. Experimental results showed that the outage probability increases with the increase in the switching threshold, and the increase in the signal-to-noise power ratio(SNR) suppressed outage performance. Additionally, deploying more elements in the IRS could result in SNR gain. When hard switching worked, ref. [8] used a machine learning (ML) algorithm to predict the indicator of received signal strength (RSSI), which proved the reliability of hard switching. RSSI considered the values of the current state and the previous state, and the proper selection of threshold limits for the RSSI parameter was crucial: when the RSSI exceeded the threshold, the FSO link was chosen, whereas when it fell below the threshold, the RF link was selected. Several researchers have explored channel prediction based on ML algorithms, including hybrid links, especially those combined with adaptive modulation (AM). In [9], a proposal was made for a method of estimating SNR using artificial neural networks in AM and coding schemes. Power spectral density was used to classify SNR and played a role in adaptive coding and modulation. Once trained, it could determine the optimal adaptive coding and scheme at lower complexity, demonstrating its effectiveness in throughput performance. The possibility of using AM to select and switch modulation modes in hybrid link systems was proposed and verified by using intelligent power control and link switching in [10]. It demonstrated that not only could the variation trend in RSSI be predicted, but also that power control could effectively reduce switching frequency, thereby enhancing the transmission quality of FSO link. In [11], the ML algorithm was used to predict channel state information, in which the SNR of the next transmission channel was taken as the prediction target, and the past SNR with other relevant information was treated as the prediction basis. In [12], the ML model was used to train the amplitude-frequency vector of data symbols with the goal of matching the SNR and achieving SNR estimation. The experiment showed that even with very low SNR, ML could estimate the SNR with very high accuracy, even reducing the mean square error to less than 0.01. Ref.