Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities
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 significa...
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MDPI AG
2025-03-01
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| Series: | Smart Cities |
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| author | Mfonobong Uko Sunday Ekpo Ubong Ukommi Unwana Iwok Stephen Alabi |
| author_facet | Mfonobong Uko Sunday Ekpo Ubong Ukommi Unwana Iwok Stephen Alabi |
| author_sort | Mfonobong Uko |
| collection | DOAJ |
| description | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula> (as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>10</mn><mrow><mo>−</mo><mn>4</mn></mrow></msup><mspace width="0.166667em"></mspace><mrow><mi>bits</mi><mo>/</mo><mi>Joule</mi></mrow></mrow></semantics></math></inline-formula>), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>12</mn></mrow></msup><mspace width="0.166667em"></mspace><mrow><mi>bps</mi><mo>/</mo><mi>Hz</mi></mrow></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula> 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. |
| format | Article |
| id | doaj-art-211df60d76d84b838e57e55752424dfe |
| institution | OA Journals |
| issn | 2624-6511 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Smart Cities |
| spelling | doaj-art-211df60d76d84b838e57e55752424dfe2025-08-20T02:18:05ZengMDPI AGSmart Cities2624-65112025-03-01825410.3390/smartcities8020054Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart CitiesMfonobong Uko0Sunday Ekpo1Ubong Ukommi2Unwana Iwok3Stephen Alabi4Communication and Space Systems Engineering Research Team, Manchester Metropolitan University, Manchester M15 6BH, UKCommunication and Space Systems Engineering Research Team, Manchester Metropolitan University, Manchester M15 6BH, UKDepartment of Electrical/Electronics Engineering, Faculty of Engineering, Akwa Ibom State University, Obio Akpa P.M.B. 1167, NigeriaDepartment of Electrical/Electronics Engineering, Faculty of Engineering, Univeristy of Uyo, Uyo P.M.B 1017, NigeriaSmOp Cleantech, Unit 22, Wilsons Park, Monsall Rd, Manchester M40 8WN, UKUnmanned 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula> (as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>10</mn><mrow><mo>−</mo><mn>4</mn></mrow></msup><mspace width="0.166667em"></mspace><mrow><mi>bits</mi><mo>/</mo><mi>Joule</mi></mrow></mrow></semantics></math></inline-formula>), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>12</mn></mrow></msup><mspace width="0.166667em"></mspace><mrow><mi>bps</mi><mo>/</mo><mi>Hz</mi></mrow></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>η</mi><mi>e</mi></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/2624-6511/8/2/54UAV-to-UAV communicationenergy efficiencydynamic networksspectral efficiencysmart cities |
| spellingShingle | Mfonobong Uko Sunday Ekpo Ubong Ukommi Unwana Iwok Stephen Alabi Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities Smart Cities UAV-to-UAV communication energy efficiency dynamic networks spectral efficiency smart cities |
| title | Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities |
| title_full | Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities |
| title_fullStr | Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities |
| title_full_unstemmed | Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities |
| title_short | Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities |
| title_sort | energy and spectral efficiency analysis for uav to uav communication in dynamic networks for smart cities |
| topic | UAV-to-UAV communication energy efficiency dynamic networks spectral efficiency smart cities |
| url | https://www.mdpi.com/2624-6511/8/2/54 |
| work_keys_str_mv | AT mfonobonguko energyandspectralefficiencyanalysisforuavtouavcommunicationindynamicnetworksforsmartcities AT sundayekpo energyandspectralefficiencyanalysisforuavtouavcommunicationindynamicnetworksforsmartcities AT ubongukommi energyandspectralefficiencyanalysisforuavtouavcommunicationindynamicnetworksforsmartcities AT unwanaiwok energyandspectralefficiencyanalysisforuavtouavcommunicationindynamicnetworksforsmartcities AT stephenalabi energyandspectralefficiencyanalysisforuavtouavcommunicationindynamicnetworksforsmartcities |