Assessing Wireless Communication Systems Performance Metrics Using Artificial Neural Networks: A Modelling and Simulation Approach
Bibliographic record
Abstract
In light of the increasing complexity associated with processing voluminous data, including signal, image, and video information, this study presents an approach to assess wireless communication system performance using artificial neural networks (ANNs).The focus is on key metrics such as mean square error (MSE), capacity, and error probability.ANNs, as intelligent tools, facilitate the analysis of these metrics via training and testing of data, employing backpropagation algorithms.Special attention is given to the evaluation of error probability related to signal transmission and reception, MSE for wireless channel coefficient estimation, and capacity for information transfer quantity relative to signal-tonoise ratio.These evaluations are executed in the matrix laboratory (MATLAB) environment.The findings suggest that the proposed ANN-based modelling and simulation method offers an excellent means for assessing wireless system metrics, along with existing research methodologies.The proposed approach is pertinent for the development of advanced wireless applications associated with 5G and 6G systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".