Rapid Prototyping Hardware Platforms for the Development and Testing of OFDM Based Communication Systems
Bibliographic record
Abstract
Implementation of modern digital transceivers requires an expertise in numerous fields. Conventional transceiver design methods are no longer sufficient to guarantee a fast conversion from initial concept to final product. Moreover, in the testing phase, system simulations alone cannot provide the full insight into the system parameters and performance, especially at the RF stages, where the modeling of power amplifier non-linearities is a highly complex task. To address these design gaps, this paper utilizes software radio solutions. Specifically, it elaborates on transceiver architectural methods at the baseband involving hardware/software partitioning, as well as automatic digital signal processing (DSP) coding strategies that allow for rapid prototyping, testing and verification of algorithms developed in the design simulation stages. In particular, DSP processor and field programmable gate array (FPGA)-based testbeds are described that offer different advantages in the transceiver rapid prototyping methodology. These testbeds were designed to eventually be used in experiments geared towards demonstrating the effectiveness of compensation algorithms for wireless systems like wireless local area network (WLAN) and digital audio broadcasting (DAB), where orthogonal frequency division multiplexed (OFDM) signaling is deployed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".