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:


Latest Publications

  • Artificial Neural Network Modelling and Characterization of a 3.2–3.8-GHz Low-Noise Amplifier for Sub-6-GHz Applications

    by Mfonobong Uko, Sunday Ekpo, Fanuel Elias, Sunday Enahoro, Ubong Ukommi, Rahul Unnikrishnan, Unwana Ubong Iwok, Aniebiet Inyang


  • Open Access

    Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities

    by Mfonobong Uko, Sunday Ekpo, Ubong Ukommi, Unwana Iwok, Stephen Alabi

    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 (ηe) 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 ηe (as low as 10−3–10−4bits/Joule), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain 4×10−12bps/Hz 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 ηe 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.


  • Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

    by Unwana Ubong Iwok, Kingsley Monday Udofia, Akaninyene Bernard Obot, Kufre Michael Udofia, Unyime Anietie Michael, Akpabio Itoro Kingsley