The paper introduces a wavelet-based tool for analyzing long-range dependence (LRD) and a semi-parametric estimator of the Hurst parameter. The estimator is shown to be unbiased under general conditions and efficient under Gaussian assumptions, with high computational efficiency and robustness against deterministic trends. It is applied to Ethernet traffic traces, revealing new features with implications for model selection for performance evaluation. The study also investigates mono versus multifractality and preliminary results on stationarity with respect to the Hurst parameter and deterministic trends. The wavelet-based estimator is compared with traditional estimators, including the Whittle estimator, and its performance is demonstrated through numerical simulations and real data analysis. The paper highlights the importance of choosing the appropriate wavelet with a sufficient number of vanishing moments to effectively detect and eliminate the effects of deterministic trends, making it a powerful tool for analyzing LRD in telecommunications networks.The paper introduces a wavelet-based tool for analyzing long-range dependence (LRD) and a semi-parametric estimator of the Hurst parameter. The estimator is shown to be unbiased under general conditions and efficient under Gaussian assumptions, with high computational efficiency and robustness against deterministic trends. It is applied to Ethernet traffic traces, revealing new features with implications for model selection for performance evaluation. The study also investigates mono versus multifractality and preliminary results on stationarity with respect to the Hurst parameter and deterministic trends. The wavelet-based estimator is compared with traditional estimators, including the Whittle estimator, and its performance is demonstrated through numerical simulations and real data analysis. The paper highlights the importance of choosing the appropriate wavelet with a sufficient number of vanishing moments to effectively detect and eliminate the effects of deterministic trends, making it a powerful tool for analyzing LRD in telecommunications networks.