RNN-Based Twin Channel Predictors for CSI Compression and Recovery in UAV-Assisted Next-Generation Wireless Networks

Unmanned aerial vehicles (UAVs) evolution has gained an unabated interest for the use in several applications, such as agriculture, aerial surveillance, goods delivery, disaster recovery, intelligent transportation. The main features of this technology are high coverage, strong line-of-sight (LoS) links, promising throughput, cost-effective and flexible deployment. Currently, the third Generation Partnership Project (3GPP) is working on the specification of release-17 (R-17) new radio (NR) for non-terrestrial networks (NTN). Therefore, owing to the drastic increase of UAV technology, in this paper, we propose channel state information (CSI) compression and its recovery with the aid of machine learning (ML)-based twin channel predictors. Due to the characteristic of gaining higher LoS communication paths in UAV network, the proposed strategy can bring potential benefits such as over-the-air (OTA)-overhead reduction and minimizing mean-squared-error (MSE) of a channel. Simulation-based results corroborate the validity of proposed strategy, which can reap benefits in multiple factors, for example, textit{hover time} maximization of a UAV and achieving precoding gain, etc.