A Toolkit for UAM Communications Management

Air/Ground (AG) communications (AG Comm) are well established for traditional National Airspace System participants, but for the Advanced Air Mobility (AAM) concept, the infrastructure, frequency bands, and related infrastructure build-outs are in their infancy. Initially, AAM pilots will be in the cockpit and AG Comm will use existing voice channels. However, for UAM Maturity Level 4 (UML-4) and onwards, vehicles are controlled by remote pilot. AG Comm will require higher bandwidths than AG Comm does today for transmitting video and reproducing the cockpit on the remote pilots workstation (a digital twin of the aircraft). TUCM targets this remote pilot concept, UML-4 and onwards. TUCM uses a combination of statistical and Machine Learning (ML) tools to estimate the signal strength as a vehicle traverses the airspace. The signal strength is a complicated function of direct line of sight, multipath interference due to reflections off the ground and nearby buildings, electromagnetic interference, and atmospheric effects. This problem is compounded by the movement of the vehicle. The signal strength computations are then packaged into a marketable toolkit that can be inserted as a module into existing AAM management tools, AAM simulations, or used as a stand-alone tool for engineering and health checking of communication systems. TUCMs purpose is to increase the resiliency and reliability of AAM AG Comm. Air-ground (AG) communications has a century of history, yet as with other modern wireless communication applications, requirements for new AG wireless system throughput (data rate) and performance have grown. For example, for unmanned aircraft systems (UAS), both remotely piloted and completely autonomous UAS will require communication links with larger throughput than existing air traffic control (ATC) links. Our proposed TUCM is focused on airspace operations within the AAM domain. AAM operations occur at low-altitude and they carry people, packages, food, or some combination of these items. TUCM is targeted for remote pilot operations. Remote piloting puts stress on the UAM communications system. Not only must the complete state of the vehicle be transmitted to the remote pilot continuously, but video of an out-the-cockpit is transmitted. The bandwidth and communication rates increase over the current voice communications requirements. Using TUCM will enable scaling of demand and complexity of UAM operations, as well as increasing the resiliency of the communications network. Objectives Technical Objective 1: Increase the Fidelity of Machine Learning Models Technical Objective 2: Increase the Fidelity of Statistical Models Technical Objective 3: Compare Results with Live Flight Tests Build the TUCM Tool and Commercialize It Proposed Deliverables The TUCM Tool with Integrated Machine Learning Prediction and Statistical Estimation of Path Loss (including executable software) A Machine Learning Model that Accurately Predicts Path Loss Statistical Models that Approximate Path Loss

NASA Glenn Research Center actively investigates AG Comm issues for all aviation business models, including AAM. In addition, Langley and Ames Research Centers are testing concepts and platforms to support its High Density Vertiplex program associated with a UAM ecosystem. Both the ATM-X and the AAM Projects can productively use the TUCM tool. NASA also works with five state and local governments in MA, MN, TX, OH, and the City of Orlando to develop civic transportation plans to support emerging passenger-carrying air taxi services. Target markets for TUCM include state and local governments planning AAM services. Provider of Support for UAM (PSUs) may also find the tool useful. Communication engineers can use the tool to site AAM AG Comm towers and to determine when a tower may need repair. Avionics suppliers and AAM vehicle manufacturers will also find the tool useful.

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