An intelligent energy efficient cooperative MIMO-AF multi-hop and relay based communications for Unmanned Aerial Vehicular networks

Abstract

Unmanned Aerial Vehicles (UAVs) are recently used for both civilian and military applications in worldwide. Energy Efficiency (EE) is an exceptional design approach for modern communication based systems. New advance technology is needed in order to support UAV applications with reduced energy usage. In this paper, an Energy Efficiency with Hybrid Fuzzy Firefly Algorithm (EE-HFFA) method is introduced for Multiple-Input–Multiple-Output (MIMO) Amplify-And-Forward (AF) systems in which Partial Channel State Information (PCSI) estimation is existing at the relays because of the high speed mobility. A new EE-HFFA algorithm is presented in this research, by means of merging the benefits of the Firefly Algorithm (FA) as well as Differential Evolution (DE). For increasing information sharing both the techniques are implemented in parallel and as a result improve searching efficiency. The outcomes of these two techniques are based upon the fuzzy membership function. For approximation of PCSI for the source node as well as relay nodes, Branch Convolutional Neural Network (B-CNN) classifier is presented to raise the capability of cooperative MIMO-AF systems. EE-optimal source and relay precoding matrices are cooperatively enhanced by means of EE-HFFA. Simulation out comes illustrate that the presented EE-HFFA as well as B-CNN classifier could enhance the EE of MIMO-AF systems with PSCI while matched up with direct/relay link merely precoding optimization.