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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mamilus A. Ahaneku
University of Nigeria
Computer Science & IT

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Stochastic resonance-aided energy detection for RF-powered cognitive radio networks Henry Onyemauche Osuagwu; Mamilus A. Ahaneku; Vincent C. Chijindu; Obinna M. Ezeja
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27596

Abstract

Conventional stochastic resonance (SR) techniques often face challenges with higher-frequency signals and parameter optimization for real-time applications, as observed in practical orthogonal frequency-division multiplexing (OFDM) systems that are vulnerable to noise uncertainty (NU). In this study, we present a novel SR-aided energy detection (ED) method that incorporates multi-taper spectrum estimation technique to improve spectrum estimation precision and Gauss-Seidel-like iteration method to accurately adjust the SR parameters for real-time adaptation. This combined strategy enhances weak signal detection, prevents signal distortion, and increases robustness against fluctuating noise conditions. Results from 5,000 Monte Carlo simulations showed that, at 0 dB NU, SR-aided ED attained 90% detection probability at -11 dB, outperforming conventional ED with an SNR gain of 12.5 dB. At 3 dB NU, the conventional ED accuracy degraded by 5.5 dB, resulting in a false alarm probability of 77%, while SR-aided ED demonstrated robustness to NU. At 10 dB NU, ED failed to distinguish the differences between noise and signal power, giving rise to 99% false alarm probability. In contrast, despite a 6 dB degradation, the developed SR-aided ED approach still guarantees a 1% false alarm probability. In clipping-prone systems, conventional ED is vulnerable to signal clipping. Conversely, SR-aided ED remains unaffected.
Improved channel quality indicator estimation using extended Kalman filter in LTE networks under diverse mobility models Hilary U. Ezea; Mamilus A. Ahaneku; Vincent C. Chijindu; Obinna Ezeja; Udora N. Nwawelu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.27205

Abstract

Accurate channel quality indicator (CQI) estimation is crucial for optimizing resource allocation, improving link adaptation, and sustaining high performance in long term evolution (LTE) networks. In real-world scenarios, where channel conditions fluctuate rapidly due to user mobility, inaccurate CQI estimation can lead to suboptimal scheduling, degraded throughput, and reduced quality of service (QoS) for both users and network operators. Traditional Kalman filter (KF) approaches often struggle with the non-linear and time-varying nature of wireless channels, especially under unpredictable mobility patterns. This paper proposes an improved CQI estimation method based on the extended Kalman filter (EKF), which models non-linear system dynamics more effectively. The method is implemented in LTE-Sim, analyzed using MATLAB, and evaluated under random and Manhattan mobility models. Results show that across mobility regimes, KF outperforms EKF in the structured Manhattan model, while in the non-linear random-direction model, EKF yields markedly higher signal-to-interference-plus-noise ratio (SINR) stability and robustness to channel variation with SINR values above 10 dB between 300-450 s and a peak of approximately 60 dB. These results underscore the importance of mobility-aware estimation strategies in enhancing LTE network adaptability and throughput.
Co-Authors Henry Onyemauche Osuagwu Hilary U. Ezea Obinna Ezeja Obinna M. Ezeja Udora N. Nwawelu Vincent C. Chijindu