Wavelet‐based cognitive SCMA system for mmWave 5G communication networks
Why this work is in the frame
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Bibliographic record
Abstract
Fifth generation (5G) communication networks can achieve high spectral efficiency using sparse code multiple access (SCMA) scheme when large number of users are trying to transmit their data simultaneously. The sparsity of SCMA codewords offers the possibility of applying a low‐complexity message passing algorithm as an alternative to maximum likelihood detector. However, the requirement of densely deployed 5G users is to opportunistically explore new frequencies via cognitive features to overcome spectrum scarcity challenges. In this study, spectrum sensing enables cognitive radio capabilities for the SCMA system applied in millimetre wave (mmWave) 5G communications. Proposed cognitive SCMA system can sense the spectrum holes and adapt the transmission in order to utilise the available subcarriers. Besides, wavelet packet transform based techniques are used instead of conventional Fourier‐based spectrum sensing (FSS) and orthogonal frequency‐division multiple access (OFDMA). Wavelet packet spectrum sensing offers more accurate estimation of frequency and power compared with FSS. On the other hand, wavelet packet multiple access is more flexible and robust against interference compared with OFDMA. The simulation results verify that the proposed method can significantly improve the performance of SCMA system in terms of probabilities of false alarm and detection, and symbol error rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it