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
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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Research Article
Predicting Customer Churn in the Telecommunications Industry using Machine Learning Techniques
Issue:
Volume 15, Issue 1, June 2026
Pages:
10-26
Received:
11 February 2026
Accepted:
8 April 2026
Published:
24 April 2026
DOI:
10.11648/j.ajnc.20261501.12
Downloads:
Views:
Abstract: Voluntary customer churn constitutes a persistent financial risk for telecommunications operators, particularly within enterprise customer segments where high-value accounts administer complex, multi-subscription portfolios. Industry data indicate that acquiring a new account costs between five and seven times more than retaining an existing one. Despite heightened industry awareness, the majority of operational retention platforms remain reactive, detecting departure only after the event has occurred. This investigation constructs and evaluates a machine learning pipeline engineered to identify enterprise customer churn risk proactively, drawing on authentic operational records extracted from a business-tobusiness telecommunications environment. The study follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) lifecycle. A dataset of 8,454 unique business accounts, characterised by 14 raw attributes and enriched to a final 22-variable feature set, underpins the empirical work. Pronounced class imbalance, churned accounts representing approximately 6.5minority ratio of 14.3:1, necessitated specialised resampling prior to classifier training. Five oversampling strategies were benchmarked; SVMSMOTE produced the largest gain in minority-class sensitivity and was adopted for all subsequent training cycles. Ten classifier families were trained and assessed, including EasyEnsembleClassifier, RUSBoostClassifier, XGBoost, LightGBM, CatBoost, Histogram Gradient Boosting, Balanced Bagging, a multilayer perceptron, a soft-voting ensemble, and a stacking ensemble. EasyEnsembleClassifier emerged as the leading model, attaining an F1-score of 0.129 and a recall of 38.242 of 110 churned accounts. Post-hoc explainability analysis through SHAP and LIME identified active subscriber rate, geographic billing zone, and engineered interaction terms as the dominant predictive signals. The framework was operationalised within a FastAPI-based application supporting realtime individual scoring, batch CSV prediction, and retention campaign monitoring. The projected annual revenue protection under conservative assumptions exceeds 74,000 currency units. The study illustrates that interpretable, explainability-augmented machine learning frameworks can bridge the gap between quantitative model output and managerial action, offering a replicable blueprint for data-driven churn governance in both emerging and mature telecommunications markets.
Abstract: Voluntary customer churn constitutes a persistent financial risk for telecommunications operators, particularly within enterprise customer segments where high-value accounts administer complex, multi-subscription portfolios. Industry data indicate that acquiring a new account costs between five and seven times more than retaining an existing one. D...
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