Embedded Reconfigurable Solution for OFDM Detection Over Fast Fading Radio Channels
Bibliographic record
Abstract
OFDM demodulation under fast fading radio channels is very computationally demanding, making the implementation of Software Defined Radio (SDR) solutions problematic. A sub-optimal demodulation algorithm based on QR decomposition of blocks of the channel transfer matrix offers near optimal performance at lower computational cost, but hardware support is still needed. We first propose a COordinate Rotation DIgital Computer (CORDIC) rotator in reconfigurable hardware to expose and then exploit at software level the intra-block paralellism of the QR decomposition. In particular, we show that although the rotator is deeply pipelined, the scale factor inherent to CORDIC algorithm can still be distributedly compensated throughout the pipeline at no additional cycle time penalty. Then, for a Nios II processor augmented with a Reconfigurable Functional Unit (RFU) that incorporates the proposed CORDIC rotator, we also propose a computing scenario that keeps all the data to be processed inside the RFU, to minimize overhead of the data trafic between the Register File and the CORDIC rotator. Overall, we show that OFDM demodulation under fast-fading can be performed in fixed-point arithmetic and in real-time on a Nios II reconfigurable embedded system, proving that an SDR solution for OFDM demodulation under fast fading is possible.
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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 | 0.000 |
| 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) | 0.002 | 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".