Drones and Unmanned Aerial Vehicles (UAVs) have become invaluable assets for end-users, enterprises, and governments in the last decade. They continue to prove their value as propulsion, sensor, and avionics technologies evolve, and their increasing affordability and availability enable newer and higher capabilities for countless applications in different industries. We are witnessing an incredible increase in autonomous aerial operations in urban and industrial areas. Thus, the global markets for commercial drones, urban air mobility, and drone delivery maintain their strong growth.
Various companies have already started testing autonomous aerial transportation of goods and people. Meanwhile, others create value for markets by collecting precious aerial data and intelligently processing it with the help of AI. These new applications have the potential to change many industries, from agriculture to mining and construction.
Two fundamental leaps in the technology centered on autonomy of the vehicles and the flight range. The result… the vehicles were not limited to a few hundred feet in any direction and an operator’s manual inputs, thus gradually gained the ability to fly beyond the visual line of sight (BVLOS). These advancements make a whole range of new applications possible such as drone delivery, aerial inspection and monitoring, and urban air mobility.
As futuristic as all of this may sound, there exists a substantial obstacle in the way of autonomous aerial vehicles becoming commonplace. That is the problem of connectivity.
Innovators are designing and developing vehicle-wise technologies and advancing the capabilities of aerial vehicles. However, if we cannot create a unified infrastructure to respond to the connectivity needs, it is not possible to ensure safe flights and manage air traffic. So many of the proposed applications of autonomous flights and aerial vehicles won’t take off without a seamless, robust, and range-independent connectivity technology, which is the ultimate building block for enabling safe, intelligent, and autonomous aerial operations.
Drone and UAV Connectivity
Drone and UAV connectivity has traditionally been thought of as direct, point-to-point communication between a ground station/operator and the vehicle itself. Although communication is a broad topic, we can classify aerial vehicle connectivity under two types: control and non-payload communication (CNPC), and payload communication (PC).
CNPC is the dedicated two-way link that enables the transfer of critical mission data between the vehicle and a ground station/operator. It provides useful and safe operation by ensuring that the aircraft is remotely monitored, controlled, and intervened if necessary.
Payload communications, on the other hand, is often implemented as a one-way link that transmits application-specific payload data from the drone to the ground. Since a PC facilitates the transmission of high-quality and large payload data such as real-time HD, thermal, infrared, or multispectral video, it requires much higher throughput compared to CNPC.
In contrast, CNPC is more time-sensitive and requires much less latency. Having sizes that range from a few centimeters to several tens of meters and a large variety of configurations such as rotary-wing, fixed-wing, multirotor, and many others, these vehicles are utilized for a diverse spectrum of applications. In addition to the difference between the requirements of PC and CNPC, numerous individual combinations of connectivity parameter settings must be present to fulfill these different use cases.
Since it is of utmost importance to safely manage the air traffic, technologies, and protocols to support critical communications of piloted aircraft have existed for decades. Non-military use of pilotless aircraft is relatively more recent and goes back several years rather than decades. Nevertheless, there’s an astonishing level of focus, and much work has been done by stakeholders that are industry leaders, to include new aircraft, air taxis, and delivery drones to enter and share the skies of our future safely.
The US Federal Aviation Administration (FAA), who has been the main regulatory body that has been indisputably leading the aviation industry throughout the world, has decided to separate the piloted and pilotless traffic management to better fulfill the different requirements for each, namely NextGen and Unmanned Traffic Management (UTM). This separation is an excellent start for scaling aerial operations. Especially with the ever-increasing amount of onboard data from sensors, avionics, and payloads on both types of aerial vehicles, current communication architectures that optimally enable collision avoidance, voice data, and telemetry will not suffice the next generation of piloted vehicles. Besides, when you consider that the estimations predict that there will be a demand for millions of drones in the coming years, this will be a massive problem.
Some might point out that the apparent solution to the drone connectivity problem is to utilize cellular networks, since they stand ready nationwide, providing wide-area, high-quality and secure connectivity for humans. 5G has already started enabling more reliable, low-latency, and high-throughput cellular communications for many of its users. So, it might just be the solution to the emerging problem of aerial vehicle connectivity. However, autonomous aerial operations impose entirely different challenges to meet the requirements of capacity, coverage, cost, user data rates, and latency.
Current cellular networks cannot adequately support any aerial vehicle, vertical take-off and landing (VTOL) aircraft, or passenger drones.
Limitations of Cellular Network Infrastructure
Current cellular network infrastructure and protocols are designed to support terrestrial users. The antenna and channel models are designed for 2-dimensional ground coverage and are not able to adapt to a 3D space with always moving users. The uplink/downlink bandwidth allocation is optimized for human use, meaning that the physical bandwidth allocated and algorithms are designed to support much higher download speeds. Due to altitude, a high probability of line-of-sight channels with a vast number of base stations causes interference and association problems.
A UAV that flies at a 100-feet altitude can easily get downlink signals from more than 100 ground base stations in dense urban settings. It receives a multitude of weak signals from many base stations. That is precisely the opposite way of how cellular networks and current protocols are designed.
The UAVs also cause interference to these ground base stations, which means that an individual channel is jammed in almost 100 cells and not able to serve even the terrestrial users. Due to these reasons, current cellular networks cannot adequately support any aerial vehicle, vertical take-off and landing (VTOL) aircraft, or passenger drones. These networks need to evolve to be able to support autonomous aerial operations. Companies like Uber and Ehang have already started testing their aerial vehicles with the existing infrastructure. However, the infrastructure needs, at the very least, some significant upgrades if not built from scratch.
A protocol stack for autonomous aerial operations built on existing 5G specifications might enable millions of continuously moving and connected devices that are constantly exchanging information. The new wireless network should enable ultra-reliable, low-latency, high-throughput communications for vehicles traveling at high velocity.
Vehicles in the autonomous world will operate for countless types of applications in many different locations and conditions. Each vehicle will be unique, and so will their connectivity requirements be. This calls for extremely flexible network settings that will adapt accordingly to meet those requirements continually. Built-in capabilities to delegate the computation of crucial and time-sensitive operations to the device edge and cloud edge will help optimize the load and immensely improve the overall network performance and economics.
New Wireless Network
This new cellular communications network will provide seamless connectivity and unlimited range for drones and aerial vehicles, and ensure all data is transmitted and received to and from related parties from any location. Facilitating a greater data exchange will offer extensive cloud computing capabilities, enabling drones to scale on a cloud through data processing and management services. Some of the examples of these services can be UTM, AI-powered data analysis, and over-the-air software updates, to name a few.
Traditionally cellular communication networks are thought of as one big architecture that serves all. However, advancements in autonomous vehicles, UAVs, and robotics, in general, have been forcing the nature of telecom infrastructure a change . While a great deal of attention has been given to IoT connectivity, connectivity of data-intense vehicles is rather a recent phenomenon. When we think that there will be an addition of millions of autonomous vehicles in the near future, unified approaches to telecom infrastructure need iteration. So deploying partitioned networks tailored for specific needs might be the solution. Indeed, these developments may entail that a next-generation telecommunications company might be a one that serves solely to the autonomous world.
Editor’s Note: This article was republished with permission from Soar Robotics.
Deniz Kalaslioglu is currently working as the technical co-founder of Soar Robotics. He is an electrical and electronics engineer specialized in embedded hardware and software of aerial robots, with 7+ years of experience in AI-powered autonomous drones research and development. He is currently developing the next generation of telecommunications technologies for autonomous mobility at Soar Robotics.
Previously, Kalaslioglu co-founded a drone-in-a-box company called Robostate, where he led engineering teams and actively participated in development of one of the first fully-functional autonomous industrial drone systems in the world. Since 2015, he has been actively involved in the development and implementation of deep neural networks, especially in the context of industrial transformation. He assisted many big and medium-sized companies in Healthcare, Energy, Retail, Pharma, Government, Automotive, Construction and other verticals with successful end-to-end AI transformation and automation implementations.