UIUnwana Ubong Iwok
Unwana Ubong Iwok
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Unwana Ubong Iwok has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Mfonobong Uko
Manchester Metropolitan University
2 shared publications - Sunday Cookey Ekpo
Communication and Space Systems Engineering Research Team, Manchester Metropolitan University, Manchester, UK
2 shared publications - Ubong Ukommi
Akwa Ibom State University
2 shared publications Akaninyene Bernard Obot
Department of Electrical and Electronics Engineering, University of Uyo, Uyo, Nigeria
1 shared publicationAkpabio Itoro Kingsley
Department of Electrical and Electronics Engineering, University of Uyo, Uyo, Nigeria
1 shared publication- Aniebiet Inyang
Manchester Metropolitan University
1 shared publication
Latest Publications
- 17 October 2025
- Published by Springer Nature
- p. 179-187
Artificial Neural Network Modelling and Characterization of a 3.2–3.8-GHz Low-Noise Amplifier for Sub-6-GHz Applications
- 17 October 2025
- Published by Springer Nature
- p. 179-187
Artificial Neural Network Modelling and Characterization of a 3.2–3.8-GHz Low-Noise Amplifier for Sub-6-GHz Applications
- Open Access
- 22 March 2025
- Published by MDPI AG in Smart Cities
- Vol. 8 (2) , 54
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 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 () 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 (as low as –), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain 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 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.- 22 March 2025
- Published by MDPI AG in Smart Cities
- Vol. 8 (2) , 54
Open AccessEnergy 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 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 () 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 (as low as –), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain 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 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. - 26 August 2023
- Published by Sciencedomain International in Journal of Engineering Research and Reports
- Vol. 25 (8) , 197-216
Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification
- 26 August 2023
- Published by Sciencedomain International in Journal of Engineering Research and Reports
- Vol. 25 (8) , 197-216
Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification