16-QAM modulation type is used and root-raised cosine pulse shaping filters are implemented
Bibliographic record
Abstract
In this paper a simulation of a wireless digital communication system is presented. The purpose of this work is to demonstrate important components of digital communication systems and how their presence impacts system behaviour. The simulations for this project were carried out using MATLAB. The modeled system is a frequency flat channel and the transmitted data is subject to AWGN, Rayleigh fading, and phase errors. A 16-QAM modulation type is used and root-raised cosine pulse shaping filters are implemented. This report also presents techniques to mitigate channel effects. These include phase estimation/correction algorithms and receiver diversity through the use of multiple receiver antennas. Simulation results demonstrate the effect of the SNR on several parameters including the received signal constellation as well as the BER for AWGN and Rayleigh channels. These simulation results are shown to be in agreement with the theoretical (expected) results. Simulations also show that small phase errors can led to a large degradation in system performance. The implemented phase estimator/corrector helps mitigate these effects for both AWGN and Rayleigh fading channels. BER improvements occur when receiver diversity is implemented Using a maximal ratio combiner. This improvement in performance is demonstrated by simulating a system with one, two, and four receive antennas. Investigations of the power spectral density of the root-raised cosine filter as a function of the roll-off factor are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".