# Research Trends in Collaborative Drones
## 7 Networked Drones Using Cellular Technologies
### Future Research  
Drone communications are different from traditional cellular communications due to the tight closed-loop requirements of uplink and downlink communications and the mixed traffic types communicated, such as command and control signals, sensor measurements,and bandwidth-hungry high-definition video and liDAR. Future research should ideally be conducted in the 5G network environment, which aims to overcome the drawbacks of 4G networks concerning the support of drone communications. The 5G networks make drones capable of operating across long non-line-of-sight distances. The wide 5G bandwidth enables more considerable data traffic. The BVLOS concept paves the way for new applications. For example, drones can communicate with each other and ground stations to combine their efforts to achieve a search and rescue mission. The 5G BVLOS communications with drones have their challenges, including propagation channel modeling, 3D trajectory planning, energy-efficient design, and network and radio resource allocation.  
An exciting idea is the use of drones to physically carry core network elements [60; 61; 62; 63; 64; 65]. They can play the role of relay or Base Station (BS). They can be untethered or tethered. The battery's capacity limits the time of operation of an untethered drone, such as one hour. A tethered drone is powered and linked by a cable. Fly time is not limited by battery capacity. It can last for days. The wired link can provide wider bandwidth than a wireless link. However, being attached to the ground, the range of operation of a drone is limited to a particular zone, typically a truncated hemisphere. Benefits of flying relays or BSs include easy relocation and installation concerning ground relays and BSs. Their lifetime is, of course, limited, especially for untethered BS, which are dependent on the energy reserve of their battery. Research challenges include air-to-ground and air-to-air channel modeling and optimal placement of flying relays or BS. Past research has investigated the quality of ground user connections served by a flying BS and achieving the best possible data rates using machine learning approaches to determine the best location for flying relays.  
A challenge in the BVLOS connected drone use cases is ensuring drone connectivity and Quality of Service (QoS) support throughout a drone mission. Ensuring predictable coverage and low communication delays is a non-trivial task due to the phenomenon of scattered cell associations where different altitudes result in varying patterns of received signal power [66]. Classical drone navigation strategies tend to focus on reducing the travel time and energy consumption of drones. However, the optimal navigation path does not likely provide the uplink and downlink QoS requirements of a drone or may unnecessarily consume a large amount of network resources. Minimizing travel time and energy usage while maximizing connectivity and reducing cellular network resources are conflicting requirements that need novel algorithms and approaches to enable widespread connected drone use-case adoption. Another challenge within this domain is drone re-connectivity upon connection disruption. Even with connectivity-aware path planning, a drone may often lose connectivity due to propagation errors or changes in network deployment and increased load. To mitigate this, novel algorithms that can re-route the drone to alternative routes with the required QoS connectivity are needed.