A Low Complexity PAR Reduction Technique Using Cyclic Shifted Data Sequences in DS-CDMA Signals
Bibliographic record
Abstract
Downlink direct sequence-code division multiple access signals have a large dynamic range causing these signals to be distorted by the nonlinear characteristics of the high power amplifier. The signal dynamic range is often characterized by peak-to-average ratio (PAR). Several techniques have been proposed to minimize such forms of distortion by reducing PAR such as partial transmit sequences and selected mapping (SLM), where several representations of the same signal are generated and the one with the minimum PAR is selected for transmission. Such techniques remarkably improve the system performance, but at the expense of an increased system complexity and computational burden. In this paper, we present a low complexity technique to reduce PAR based on cyclic shifts of the users' data sequences (namely CSS). The search and optimization procedure in the proposed CSS technique is also simplified by adopting a greedy algorithm that selects the best representation sequentially as new users are added to the system. Comparison of the presented technique against SLM shows comparable improvement in terms of PAR reduction and bit error performance but with remarkable complexity reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".