UAVs joint optimization problems and machine learning to improve the 5G and Beyond communication

Recently, unmanned aerial vehicles (UAVs) have gained notable interest in various applications such as wireless coverage, aerial surveillance, precision agriculture, construction, power lines monitoring and blood delivery, etc. The UAVs implicit attributes e.g., rapid deployment, quick mobility, increase in flight duration, improvements in payload capacities, etc., place it as an effective candidate for many applications in 5G and Beyond communications. The UAVs-assisted next-generation communications are determined to be highly influenced by various techniques and technologies like artificial intelligence (AI), machine learning (ML), deep reinforcement learning (DRL), mobile edge computing (MEC), and software-defined networks (SDN). In this article, we develop a review to investigate the UAVs joint optimization problems to enhance system efficiency. We classify the joint optimization problems based on the number of parameters used in proposed optimization problems. Moreover, we explore the impact of AI, ML, DRL, MEC, and SDN over UAVs joint optimization problems and present future research challenges and directions.