Research Article
AI-Assisted Adaptive Beamforming Antennas for Dynamic Spectrum Access
Philip Omohimire Omolaye*
,
Samuel Adedeji Adeleye,
Eiyike Smith Jeffrey,
Gabriel Agu Igwue
Issue:
Volume 15, Issue 1, June 2026
Pages:
1-9
Received:
7 January 2026
Accepted:
17 January 2026
Published:
30 January 2026
DOI:
10.11648/j.ajnc.20261501.11
Downloads:
Views:
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.
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 in...
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