Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz
In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionSimilar Papers 제목 키워드 기반
The Power of Factorization Mechanisms in Local and Central Differential Privacy
We give new characterizations of the sample complexity of answering linear queries (statistical queries) in the local and central models of differential privacy: *In the non-interactive local model, we give the first app…
TRIAGE: Characterizing and auditing training data for improved regression
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are…
regressionA novel optical needle probe for deep learning-based tissue elasticity characterization
The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (O…
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis…
Band GapCharacterizing the Sample Complexity of Private Learners
In 2008, Kasiviswanathan et al. defined private learning as a combination of PAC learning and differential privacy. Informally, a private learner is applied to a collection of labeled individual information and outputs a…
PAC learning