Source-linked AI summary
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Ursula Challita, Aidin Ferdowsi, Mingzhe Chen, Walid Saad
TL;DR
Cellular-connected UAV applications require reliable wireless connectivity and secure operation despite challenges including interference, mobility, handover, attacks, and authentication. This paper exposes these challenges across delivery, multimedia streaming, and intelligent transportation systems and introduces ANN-based solutions. Preliminary simulations show benefits for each application use case.
Problem
Cellular-connected UAVs need reliable connectivity and secure operation across delivery, multimedia streaming, and intelligent transportation applications, but existing work does not address both wireless and security challenges with AI-based solutions.
Method
The paper introduces ANN-based schemes that predict future network changes so UAVs can adaptively optimize resources and secure operation across three application use cases.
Results
Preliminary simulation results show benefits from the introduced AI solutions for each cellular-connected UAV application use case.
Takeaways & Limitations
Machine-learning solutions are presented as a way for cellular-connected UAVs to adapt online to network dynamics while addressing wireless and security challenges.
Abstract
from arXiv · showhide
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.
I. INTRODUCTION
Cellular-connected UAVs support diverse applications but introduce wireless and security challenges across delivery, multimedia streaming, and intelligent transportation. The paper focuses on machine-learning approaches, particularly ANN-based schemes, to address these challenges adaptively and online.
- Cellular connectivity extends UAV capabilities through beyond-line-of-sight control, low latency, real-time communication, robust security, and ubiquitous coverage.
- Delivery, multimedia streaming, and intelligent transportation applications face distinct challenges, including handover, path planning, cooperative transmission, and secured swarm consensus.
- Machine learning is emphasized because it can predict future network states, adapt to online network dynamics, generalize to unseen states, and scale to large networks.
- The paper addresses a stated gap by proposing and evaluating AI-based solutions for both wireless and security challenges in cellular-connected UAV applications.
- The proposed ANN approaches target adaptive resource optimization and secure operation across three cellular-connected UAV use cases, with preliminary simulations assessing their benefits.
A. Motivation
UAV delivery systems can accelerate delivery and reach remote areas, but realizing these benefits requires cellular connectivity and solutions to associated wireless and security challenges.
- UAV delivery systems support package, food, medical, vaccination, and passenger delivery while potentially reducing delivery time and cost.
- Cellular connectivity enables operators to track delivery UAV locations and help secure transported goods.
- Cellular-connected delivery systems must address challenges ranging from efficient handover and path planning to cyber-physical attacks.
1) Ultra-Reliable and Low-Latency Communications (URLLC):
UAV delivery requires highly reliable, low-latency control communications while maintaining connectivity along efficient paths. The proposed AI schemes address latency, handover, and connectivity-constrained path planning.
- 1) Ultra-Reliable and Low-Latency Communications (URLLC):: Delivery control information requires latency of 1 ms or less and a target block error rate as low as 10^-5 in mission-critical scenarios.Wireless latency includes signaling overhead and data transmission.
- 1) Ultra-Reliable and Low-Latency Communications (URLLC):: Bidirectional LSTM processing uses previous and future channel-quality context to update cell associations while avoiding frequent handovers.
- 1) Ultra-Reliable and Low-Latency Communications (URLLC):: Delivery paths must minimize mission time while maintaining an instantaneous SINR threshold for reliable control communication.The optimization depends on UAV location, cell association, transmit power, and serving ground-BS location.
- 1) Ultra-Reliable and Low-Latency Communications (URLLC):: The deep ESN-based reinforcement-learning framework optimizes multiple UAV trajectories online while minimizing latency and interference under connectivity constraints.
- 1) Ultra-Reliable and Low-Latency Communications (URLLC):: Deep ESN-based path planning provides more reliable wireless connectivity and lower latency than a wireless-unaware shortest-path approach.
C. Security Challenges and AI Solutions
Cellular-connected delivery UAVs face cyber-physical attacks because altitude limitations and line-of-sight links expose them to adversaries seeking control of the aircraft and its cargo.
- Cyber-physical attackers may compromise a delivery UAV, take control, and destroy, delay, or steal transported goods.
- The paper proposes creating a cyber-physical threat map that categorizes adversary locations using environmental objects where UAVs can be physically attacked.
III. UAV-BASED REAL-TIME MULTIMEDIA STREAMING APPLICATIONS
Cellular-connected UAVs support low-latency multimedia streaming but face uplink interference from simultaneous line-of-sight links to multiple base stations and ground users. Addressing these applications requires both network-design improvements and AI-based processing approaches.
- Real-time UAV multimedia applications include online video streaming, broadcasting, virtual reality, tracking, localization, and surveillance.Cellular connectivity enables online data transmission and low-latency wireless communication for these applications.
- Uplink transmissions from UAVs can create substantial mutual interference because each UAV may have line-of-sight connectivity with multiple ground base stations.The interference can affect both cellular-connected UAVs and ground users.
- Future cellular networks require advanced receivers, cell coordination, three-dimensional frequency reuse, and three-dimensional beamforming to address this interference.
2) UAV-enabled Edge Caching:
UAV edge caching reduces the data that must be collected for multimedia generation by storing popular or commonly requested content. An ESN-based predictor uses user context and request distributions to guide caching, with simulations showing accurate prediction and lower transmit power than no caching.
- Cache-enabled UAVs store common data files for popular content or videos users may request later, reducing the data they need to collect.
- The ESN-based algorithm takes users’ age, gender, and job as input and outputs their content request distribution for cache selection.The UAVs can then transmit cached content without backhaul connections.
- The ESN-based algorithm accurately predicts a given user’s content request distribution using real Youku data.
- The proposed ESN algorithm yields a considerable reduction in average UAV transmit power compared with a baseline without caching.Figure 4(b) evaluates transmit power as a function of the number of users.
3) Identification of Aerial and Ground Users:
Identifying aerial users and authenticating large UAV populations are security and resource-management challenges in multimedia streaming. An LSTM-based deep reinforcement-learning authentication framework performs better than two baselines when the proportion of vulnerable UAVs increases.
- Airborne users require different radio-resource allocation from ground users because cellular-connected UAVs experience a distinct radio propagation environment.Self-reporting alone cannot reliably distinguish airborne from ground users.
- Forged UAV identities can enable insider attacks that disrupt multimedia transmissions, while authenticating every UAV simultaneously can exceed base-station computational resources.This can delay processing of received multimedia files.
- The authentication evaluation compares an LSTM-based deep RL algorithm with equal-probability allocation and allocation proportional to UAV signal values.
- As the proportion of vulnerable UAVs increases, the LSTM-based deep RL method outperforms both baselines and reduces the proportion of compromised UAVs.At low vulnerability proportions, it performs the same as the two baseline scenarios.
IV. UAV-ENABLED INTELLIGENT TRANSPORTATION SYSTEMS
UAV-enabled intelligent transportation systems can support traffic control, incident monitoring, road safety, and vehicular platoons. Their sensing platforms generate diverse multimedia and large datasets, creating a need for coordinated transmission among UAVs, vehicles, and infrastructure.
- UAVs in intelligent transportation systems can control road traffic, monitor incidents, enforce road safety, and serve as flying roadside units, speed cameras, or dynamic traffic signals.
- Cellular-connected UAVs can reduce vehicular-platoon congestion by sending control and network information to one vehicle for redistribution.
- UAVs equipped with LiDAR and GPS must transmit multiple types of multimedia files and large datasets, including three-dimensional environment maps.
- Different UAVs in the same geographical area should coordinate transmissions by sending different parts of a data file to all vehicles.This approach is intended to produce faster data transmission than having each UAV send the whole file to its corresponding vehicle.
2) Multimodal Sensor Fusion:
In UAV-enabled intelligent transportation systems, integrating heterogeneous sensor readings into one vector reduces transmission demands, helping limit congestion and support more simultaneous UAV service.
- Dense UAV deployments can congest cellular networks when each UAV transmits every sensor reading to other network nodes.
- Integrating heterogeneous sensor readings into one vector reduces the data transmitted from each UAV.
- Reduced wireless congestion enables a larger number of UAVs to be served simultaneously.
C. Security Challenges and AI Solutions
The paper frames UAV-ITS security around protecting coordinated swarm data sharing while organizing wireless and security challenges alongside ANN-based solution schemes. Preliminary simulations report benefits across the cellular-connected UAV use cases.
- Coordinated UAV swarms share data to reach consensus over tasks and respond autonomously to changing conditions.
- Data sharing among UAV swarms is generally prone to adversarial machine learning attacks.
- Table I summarizes wireless and security challenges across UAV-DS, UAV-RMS, and UAV-ITS alongside ANN-based solution schemes.
- Preliminary simulation results show benefits for the introduced solutions in each cellular-connected UAV application use case.