Performance of uplink WFRFT‐based hybrid carrier systems with non‐orthogonal multiple access
Bibliographic record
Abstract
In this paper, the performance of hybrid carrier (HC) systems based on weighted fractional Fourier transform (WFRFT) is investigated in an uplink non‐orthogonal multiple access (NOMA) scenario. NOMA is a promising technology to improve the system capacity, in which two users (far‐user and near‐user relative to a base station) are allocated to use the same time‐frequency resources, and the successive interference cancellation (SIC) technique is implemented to decode signals at the receiver. Considering the actual error decoding in the SIC process (i.e. imperfect SIC), NOMA cannot avoid the inter‐user interference (IUI) and residual interference (or error propagation). Therefore, firstly IUI and residual interference are analysed, and signal to interference plus noise ratio (SINR) of the far‐user is expressed mathematically considering the residual interference. Then, based on the analysis of IUI, considering different WFRFT orders, a near‐user BER expression over additive white Gaussian noise (AWGN) channels is derived. Furthermore, the optimal WFRFT order selection to minimise the interference influence in the uplink is formulated and solved efficiently. Simulation results have verified the mathematical expression of SINR, the near‐user theoretical BER expression, and the proposed optimal WFRFT order selection to obtain the maximum sum spectral efficiency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".