DSP implementation of a bit loading algorithm for adaptive wireless multicarrier transceivers
Bibliographic record
Abstract
Abstract In this paper, we present a proof‐of‐concept, fixed‐point, DSP hardware implementation of an adaptive bit loading algorithm that is designed for wireless multicarrier transceivers. Adaptive bit loading is used to enhance the performance of multicarrier transceivers by tailoring the subcarrier signal constellations to the channel conditions, which can vary across the subcarriers. Since most bit loading algorithms possess a high computational cost and are unable to cope with rapid variations of wireless channels, they are seldom used in present wireless standards. To prove that adaptive bit loading is feasible for wireless transceivers, our work focuses on the implementation of a known bit loading algorithm that can quickly search for the final bit allocation in an iterative manner. The goal of this algorithm is to yield the largest‐possible throughput while satisfying a mean BER constraint. The performance of the hardware implementation operating in time‐varying channel conditions is studied in terms of the overall throughput. Furthermore, the robustness of the hardware implementation is evaluated, relative to sudden changes in the channel that interrupts the run of the algorithm. Real‐time operations and fixed‐point representation issues are included in the discussion. Additionally, we propose a modified algorithm implementation that is more robust to channel variations. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".