AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access

Philip Omohimire Omolaye; Samuel Adedeji Adeleye; Eiyike Smith Jeffrey; Gabriel Agu Igwue

doi:doi:10.11648/j.ajnc.20261501.11

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AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access

Philip Omohimire Omolaye^*

, Samuel Adedeji Adeleye

, Eiyike Smith Jeffrey, Gabriel Agu Igwue

Published in American Journal of Networks and Communications (Volume 15, Issue 1)

Received: 7 January 2026 Accepted: 17 January 2026 Published: 30 January 2026

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Abstract

The rapid growth in wireless communications and the increasing scarcity of spectrum necessitate intelligent and adaptive technologies for efficient utilization of available resources. Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs) offers a promising solution to these challenges. However, achieving real-time spectrum agility and interference mitigation remains a technical hurdle. This paper presents a novel artificial intelligence (AI)-assisted adaptive beamforming scheme based on reinforcement learning (RL) to dynamically steer antenna beams toward legitimate users while suppressing interference. An 8-element Uniform Linear Array (ULA) operating at 2.4 GHz is modeled in MATLAB, and a Q-learning algorithm is employed to learn optimal beamforming weights through spectrum feedback. Simulation results demonstrate that the RL-based approach achieves a 4.9 dB improvement in Signal-to-Interference-plus-Noise Ratio (SINR) and 38% faster convergence compared to classical Least Mean Squares (LMS) algorithms. Unlike conventional adaptive beamforming methods, the proposed scheme does not require prior knowledge of the interference environment or channel statistics, enabling autonomous adaptation in highly dynamic spectrum conditions. Moreover, the system exhibits robustness to user mobility and Signal-to-Noise-Ratio (SNR) variations, making it suitable for cognitive base stations, Unmanned Aerial Vehicles (UAV) communications, and Spectrum-sharing Internet of Things (IoT) environments. These results indicate that reinforcement learning–driven beam control can serve as a practical enabler for real-time spectrum intelligence in next-generation wireless systems. This work underscores the potential of intelligent beamforming for next-generation wireless systems and sets the stage for future enhancements using deep RL and hybrid beamforming architectures.

Published in	American Journal of Networks and Communications (Volume 15, Issue 1)
DOI	10.11648/j.ajnc.20261501.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Cognitive Radio Networks, Adaptive Beamforming, Reinforcement Learning, Dynamic Spectrum Access, Q-learning Algorithm

References

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[2]	Wang, Q., & Yu, W. (2022). Adaptive resource allocation in dynamic spectrum sharing: A reinforcement learning approach. IEEE Transactions on Wireless Communications, 21(9), 7423–7436. https://doi.org/10.1109/TWC.2022.3171572
[3]	Ali, F., Khan, A., Iqbal, M., & Alkhateeb, A. (2021). Machine learning for beamforming in 5G and beyond: Recent advances and future directions. IEEE Communications Surveys & Tutorials, 23(4), 2366–2393. https://doi.org/10.1109/COMST.2021.3101600
[4]	Chen, Y., Zhang, Y., Li, H., & Jiang, T. (2024). Reinforcement learning-based beamforming for dynamic spectrum access in cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 10(1), 58–72. https://doi.org/10.1109/TCCN.2023.3339812
[5]	Zhang, H., Xu, X., Song, J., & Han, Z. (2025). Intelligent beam management for cognitive UAV networks using deep reinforcement learning. IEEE Internet of Things Journal, 12(6), 4883–4896. https://doi.org/10.1109/JIOT.2024.3320151
[6]	Van Veen, B. D., & Buckley, K. M. (2020). Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 5(2), 4–24. https://doi.org/10.1109/53.665
[7]	Khalaf, B., Khan, W. Z., & Lloret, J. (2021). A survey on beamforming techniques for wireless comm and networks. Sensors, 21(3), 891. https://doi.org/10.3390/s21030891
[8]	Alkhateeb, A., & Heath, R. W. (2020). Deep learning for beam selection in millimeter wave communications. IEEE Transactions on Communications, 68(9), 5639–5653. https://doi.org/10.1109/TCOMM.2020.2998573
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[12]	Zhang, L., Li, W., & Zhang, Y. (2021). Transfer reinforcement learning for fast beam adaptation in CRNs. IEEE Access, 9, 123500–123514. https://doi.org/10.1109/ACCESS.2021.3109382
[13]	Wang, H., Zhang, X., & Han, Z. (2023). Meta-learning-enhanced beamforming for spectrum-aware networks. IEEE Internet of Things Journal, 10(4), 2811–2825. https://doi.org/10.1109/JIOT.2022.3181011
[14]	Lee, K., Kim, Y., & Bennis, M. (2020). Federated beamforming: Distributed learning with privacy in massive MIMO systems. IEEE Transactions on Communications, 68(11), 6657–6671. https://doi.org/10.1109/TCOMM.2020.3003830
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APA Style

Omolaye, P. O., Adeleye, S. A., Jeffrey, E. S., Igwue, G. A. (2026). AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. American Journal of Networks and Communications, 15(1), 1-9. https://doi.org/10.11648/j.ajnc.20261501.11

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ACS Style

Omolaye, P. O.; Adeleye, S. A.; Jeffrey, E. S.; Igwue, G. A. AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. Am. J. Netw. Commun. 2026, 15(1), 1-9. doi: 10.11648/j.ajnc.20261501.11

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AMA Style

Omolaye PO, Adeleye SA, Jeffrey ES, Igwue GA. AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. Am J Netw Commun. 2026;15(1):1-9. doi: 10.11648/j.ajnc.20261501.11

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@article{10.11648/j.ajnc.20261501.11,
  author = {Philip Omohimire Omolaye and Samuel Adedeji Adeleye and Eiyike Smith Jeffrey and Gabriel Agu Igwue},
  title = {AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access},
  journal = {American Journal of Networks and Communications},
  volume = {15},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajnc.20261501.11},
  url = {https://doi.org/10.11648/j.ajnc.20261501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20261501.11},
  abstract = {The rapid growth in wireless communications and the increasing scarcity of spectrum necessitate intelligent and adaptive technologies for efficient utilization of available resources. Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs) offers a promising solution to these challenges. However, achieving real-time spectrum agility and interference mitigation remains a technical hurdle. This paper presents a novel artificial intelligence (AI)-assisted adaptive beamforming scheme based on reinforcement learning (RL) to dynamically steer antenna beams toward legitimate users while suppressing interference. An 8-element Uniform Linear Array (ULA) operating at 2.4 GHz is modeled in MATLAB, and a Q-learning algorithm is employed to learn optimal beamforming weights through spectrum feedback. Simulation results demonstrate that the RL-based approach achieves a 4.9 dB improvement in Signal-to-Interference-plus-Noise Ratio (SINR) and 38% faster convergence compared to classical Least Mean Squares (LMS) algorithms. Unlike conventional adaptive beamforming methods, the proposed scheme does not require prior knowledge of the interference environment or channel statistics, enabling autonomous adaptation in highly dynamic spectrum conditions. Moreover, the system exhibits robustness to user mobility and Signal-to-Noise-Ratio (SNR) variations, making it suitable for cognitive base stations, Unmanned Aerial Vehicles (UAV) communications, and Spectrum-sharing Internet of Things (IoT) environments. These results indicate that reinforcement learning–driven beam control can serve as a practical enabler for real-time spectrum intelligence in next-generation wireless systems. This work underscores the potential of intelligent beamforming for next-generation wireless systems and sets the stage for future enhancements using deep RL and hybrid beamforming architectures.},
 year = {2026}
}

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TY  - JOUR
T1  - AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access
AU  - Philip Omohimire Omolaye
AU  - Samuel Adedeji Adeleye
AU  - Eiyike Smith Jeffrey
AU  - Gabriel Agu Igwue
Y1  - 2026/01/30
PY  - 2026
N1  - https://doi.org/10.11648/j.ajnc.20261501.11
DO  - 10.11648/j.ajnc.20261501.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20261501.11
AB  - The rapid growth in wireless communications and the increasing scarcity of spectrum necessitate intelligent and adaptive technologies for efficient utilization of available resources. Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs) offers a promising solution to these challenges. However, achieving real-time spectrum agility and interference mitigation remains a technical hurdle. This paper presents a novel artificial intelligence (AI)-assisted adaptive beamforming scheme based on reinforcement learning (RL) to dynamically steer antenna beams toward legitimate users while suppressing interference. An 8-element Uniform Linear Array (ULA) operating at 2.4 GHz is modeled in MATLAB, and a Q-learning algorithm is employed to learn optimal beamforming weights through spectrum feedback. Simulation results demonstrate that the RL-based approach achieves a 4.9 dB improvement in Signal-to-Interference-plus-Noise Ratio (SINR) and 38% faster convergence compared to classical Least Mean Squares (LMS) algorithms. Unlike conventional adaptive beamforming methods, the proposed scheme does not require prior knowledge of the interference environment or channel statistics, enabling autonomous adaptation in highly dynamic spectrum conditions. Moreover, the system exhibits robustness to user mobility and Signal-to-Noise-Ratio (SNR) variations, making it suitable for cognitive base stations, Unmanned Aerial Vehicles (UAV) communications, and Spectrum-sharing Internet of Things (IoT) environments. These results indicate that reinforcement learning–driven beam control can serve as a practical enabler for real-time spectrum intelligence in next-generation wireless systems. This work underscores the potential of intelligent beamforming for next-generation wireless systems and sets the stage for future enhancements using deep RL and hybrid beamforming architectures.
VL  - 15
IS  - 1
ER  -

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Author Information

Philip Omohimire Omolaye

Department of Electrical & Electronics Engineering, Joseph Sarwuan Tarka University, Makurdi, Nigeria

Contact Email

http://orcid.org/0000-0002-0373-2245
Samuel Adedeji Adeleye

Department of Computer Science, Federal University of Technology, Akure, Nigeria

http://orcid.org/0009-0000-9168-2673
Eiyike Smith Jeffrey

Department of Electrical & Electronics Engineering, Joseph Sarwuan Tarka University, Makurdi, Nigeria
Gabriel Agu Igwue

Department of Electrical & Electronics Engineering, Joseph Sarwuan Tarka University, Makurdi, Nigeria

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APA Style

Omolaye, P. O., Adeleye, S. A., Jeffrey, E. S., Igwue, G. A. (2026). AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. American Journal of Networks and Communications, 15(1), 1-9. https://doi.org/10.11648/j.ajnc.20261501.11

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ACS Style

Omolaye, P. O.; Adeleye, S. A.; Jeffrey, E. S.; Igwue, G. A. AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. Am. J. Netw. Commun. 2026, 15(1), 1-9. doi: 10.11648/j.ajnc.20261501.11

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AMA Style

Omolaye PO, Adeleye SA, Jeffrey ES, Igwue GA. AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access. Am J Netw Commun. 2026;15(1):1-9. doi: 10.11648/j.ajnc.20261501.11

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@article{10.11648/j.ajnc.20261501.11,
  author = {Philip Omohimire Omolaye and Samuel Adedeji Adeleye and Eiyike Smith Jeffrey and Gabriel Agu Igwue},
  title = {AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access},
  journal = {American Journal of Networks and Communications},
  volume = {15},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajnc.20261501.11},
  url = {https://doi.org/10.11648/j.ajnc.20261501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20261501.11},
  abstract = {The rapid growth in wireless communications and the increasing scarcity of spectrum necessitate intelligent and adaptive technologies for efficient utilization of available resources. Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs) offers a promising solution to these challenges. However, achieving real-time spectrum agility and interference mitigation remains a technical hurdle. This paper presents a novel artificial intelligence (AI)-assisted adaptive beamforming scheme based on reinforcement learning (RL) to dynamically steer antenna beams toward legitimate users while suppressing interference. An 8-element Uniform Linear Array (ULA) operating at 2.4 GHz is modeled in MATLAB, and a Q-learning algorithm is employed to learn optimal beamforming weights through spectrum feedback. Simulation results demonstrate that the RL-based approach achieves a 4.9 dB improvement in Signal-to-Interference-plus-Noise Ratio (SINR) and 38% faster convergence compared to classical Least Mean Squares (LMS) algorithms. Unlike conventional adaptive beamforming methods, the proposed scheme does not require prior knowledge of the interference environment or channel statistics, enabling autonomous adaptation in highly dynamic spectrum conditions. Moreover, the system exhibits robustness to user mobility and Signal-to-Noise-Ratio (SNR) variations, making it suitable for cognitive base stations, Unmanned Aerial Vehicles (UAV) communications, and Spectrum-sharing Internet of Things (IoT) environments. These results indicate that reinforcement learning–driven beam control can serve as a practical enabler for real-time spectrum intelligence in next-generation wireless systems. This work underscores the potential of intelligent beamforming for next-generation wireless systems and sets the stage for future enhancements using deep RL and hybrid beamforming architectures.},
 year = {2026}
}

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TY  - JOUR
T1  - AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access
AU  - Philip Omohimire Omolaye
AU  - Samuel Adedeji Adeleye
AU  - Eiyike Smith Jeffrey
AU  - Gabriel Agu Igwue
Y1  - 2026/01/30
PY  - 2026
N1  - https://doi.org/10.11648/j.ajnc.20261501.11
DO  - 10.11648/j.ajnc.20261501.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20261501.11
AB  - The rapid growth in wireless communications and the increasing scarcity of spectrum necessitate intelligent and adaptive technologies for efficient utilization of available resources. Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs) offers a promising solution to these challenges. However, achieving real-time spectrum agility and interference mitigation remains a technical hurdle. This paper presents a novel artificial intelligence (AI)-assisted adaptive beamforming scheme based on reinforcement learning (RL) to dynamically steer antenna beams toward legitimate users while suppressing interference. An 8-element Uniform Linear Array (ULA) operating at 2.4 GHz is modeled in MATLAB, and a Q-learning algorithm is employed to learn optimal beamforming weights through spectrum feedback. Simulation results demonstrate that the RL-based approach achieves a 4.9 dB improvement in Signal-to-Interference-plus-Noise Ratio (SINR) and 38% faster convergence compared to classical Least Mean Squares (LMS) algorithms. Unlike conventional adaptive beamforming methods, the proposed scheme does not require prior knowledge of the interference environment or channel statistics, enabling autonomous adaptation in highly dynamic spectrum conditions. Moreover, the system exhibits robustness to user mobility and Signal-to-Noise-Ratio (SNR) variations, making it suitable for cognitive base stations, Unmanned Aerial Vehicles (UAV) communications, and Spectrum-sharing Internet of Things (IoT) environments. These results indicate that reinforcement learning–driven beam control can serve as a practical enabler for real-time spectrum intelligence in next-generation wireless systems. This work underscores the potential of intelligent beamforming for next-generation wireless systems and sets the stage for future enhancements using deep RL and hybrid beamforming architectures.
VL  - 15
IS  - 1
ER  -

Copy | Download