Reconfigurable platform for software/hardware co-design of SDR base band pre-processing module
Bibliographic record
Abstract
This paper proposes a reconfigurable platform for software/hardware co-design of base band pre-processing module of wideband wireless transceivers. The platform uses the Digital Signal Processors (DSP) and Field Programmable Gate Array (FPGA) to run advanced digital signal processing algorithms intended for the performance optimization of the transceiver radiofrequency front-end. Having the possibility to generate a variety of modulation techniques at high sampling rates and wide bandwidth up to 60MHz, the proposed platform is suitable for implementing innovative software defined radio transceivers' base band modules including digital pre-processing algorithms to boost the transceivers performances. An implementation and validation approach is also proposed. Taking advantage from the communication protocol between the DSP and the FPGA, the validation is done using the DSP it self for acquiring digital data at the output of each hardware function. A comparison of the emulation results to the high-level simulation ones is used to validate the hardware implementation and debug eventual bugs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".