| Abstract |
Highlights: What are the main findings? The study provides a comprehensive analysis of UAV-to-UAV communication, focusing on energy and spectral efficiency across multiple frequency bands (2.4 GHz, 5.8 GHz, 28 GHz, and 60 GHz) in dynamic smart city environments. Results indicate that sub-6 GHz frequencies offer superior energy efficiency (up to 0.15 bits/Joule). At the same time, millimetre-wave bands (28 GHz and 60 GHz) suffer from higher path loss and reduced efficiency. What are the implications of the main findings? Smart city UAV networks should adopt multi-band communication strategies, leveraging sub-6 GHz for long-range and energy-efficient connectivity while utilising mmWave bands for high-data-rate applications in close-proximity scenarios. Adaptive trajectory planning, dynamic frequency selection, and machine-learning-driven power control are essential to optimising UAV network efficiency, ensuring sustainable and high-performance communication in urban environments. Unmanned Aerial Vehicles (UAVs) are integral to the development of smart city infrastructures, enabling essential services such as real-time surveillance, urban traffic regulation, and cooperative environmental monitoring. UAV-to-UAV communication networks, despite their adaptability, have significant limits stemming from onboard battery constraints, inclement weather, and variable flight trajectories. This work presents a thorough examination of energy and spectral efficiency in UAV-to-UAV communication over four frequency bands: 2.4 GHz, 5.8 GHz, 28 GHz, and 60 GHz. Our MATLAB R2023a simulations include classical free-space path loss, Rayleigh/Rician fading, and real-time mobility profiles, accommodating varied heights (up to 500 m), flight velocities (reaching 15 m/s), and fluctuations in the path loss exponent. Low-frequency bands (e.g., 2.4 GHz) exhibit up to 50% reduced path loss compared to higher mmWave bands for distances exceeding several hundred meters. Energy efficiency ((Formula presented.)) is evaluated by contrasting throughput with total power consumption, indicating that 2.4 GHz initiates at around 0.15 bits/Joule (decreasing to 0.02 bits/Joule after 10 s), whereas 28 GHz and 60 GHz demonstrate markedly worse (Formula presented.) (as low as (Formula presented.) – (Formula presented.)), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain (Formula presented.) in near-line-of-sight scenarios, whereas 60 GHz lines encounter significant attenuation at distances above 200–300 m without sophisticated beamforming techniques. Polynomial-fitting methods indicate that the projected (Formula presented.) diverges from actual performance by less than 5% after 10 s of flight, highlighting the feasibility of machine-learning-based techniques for real-time power regulation, beam steering, or multi-band switching. While mmWave UAV communication can provide significant capacity enhancements (100–500 MHz bandwidth), energy efficiency deteriorates markedly without meticulous flight planning or adaptive protocols. We thus advocate using multi-band radios, adaptive modulation, and trajectory optimisation to equilibrate power consumption, ensure connection stability, and meet high data-rate requirements in densely populated, dynamic urban settings. © 2025 by the authors. |
