## 1 Introduction  
The networking industry has been dominated by the use of proprietary hardware appliances built with application-specific integrated circuits (ASIC). Network hardware appliances deliver one or more specific network functionality, including routing, intrusion detection, and traffic shaping. Network Function Virtualization (NFV) aims to replace this traditional approach to networking infrastructure by decoupling network functions from the hardware appliances, with software functions running as modular software on commercial off-the-shelf (COTS) servers with virtualization. The modular approach allows implementing virtualized network functions (VNF) as connectable blocks that can be chained to define complex services. NFV brings several benefits such as elasticity, availability, reliability, shorter deployment times, and reduced capital and operational expenditures compared to the traditional methods [1].  
While less than a decade old, the NFV concept has gained rapid acceptance to support diverse applications but mostly for the terrestrial domain. Recent works have started the discussion of the possible benefits of NFV and Software-Defined Networking (SDN) for satellite communication networks, for example, to achieve high rates in satellite-5G applications with wide-scale coverage and high availability. It is expected that the use of Extremely High Frequencies (EHF) or free-space optical (FSO) in the feeder links will push the data rates to the order of Terabit/s in the future High Throughput Satellite (HTS) systems. However, one limitation is that the resulting performance may not be consistent because, with such high frequencies, atmospheric attenuation due to clouds, fog, and rainfall can cause severe time-varying channel impairments [2]. Even with the use of AdaptiveCoding Modulation (ACM) or improved error correction, codes can disable or significantly reduce the performance of the satellite link in the event of bad atmospheric conditions [3].  
This work investigates an intelligent mitigation approach that is defined as a VNF to deal with the channel impairments of an HTS system exploiting spatial diversity for the feeder link. This approach allows forwarding the feeder link communications through selected gateways from a set of geographically distributed alternatives that are interconnected through high-speed terrestrial links, with the gateways separated by at least 20 km. While this possibility has been discussed in the literature [2; 3], the prevailing approach to the problem is static, which involves manual interventions to change the forwarding policy with the knowledge of the system and transmission medium conditions. This work introduces a reinforcement learning (RL) approach that autonomously decides how to set the data forwarding policy without requiring the knowledge of weather conditions. The proposed method not only allows quick routing adaptation to the changes to precipitation fades in the Q/V bands but also to the traffic carried by the different feeder links. We implemented the smart routing method as an SDN controller that runs as a virtual network function (VNF). The deployment of the proposed VNF for onboard controllers helps to improve the reliability and availability of the feeder links.