CORDIC-based LMMSE equalizer for Software Defined Radio
Bibliographic record
Abstract
In Code Division Multiple Access (CDMA) systems, the orthogonality of the spreading codes used to achieve multiple access over a channel is severely degraded due to multi-path interference. Expensive equalization techniques are needed to recover the transmitted signal. The Linear Minimum Mean Square Error (LMMSE) equalizer is a sub-optimal equalizer that is a good compromise between computational complexity and communication system performance. It uses computationally-intensive matrix inversion operations to perform equalization. In this paper, we address the computational challenges of implementing the LMMSE equalizer on Software Defined Radio (SDR) platforms. SDR platforms are favored by the wireless industry due to their significant benefits of reduced development costs and accelerated time-to-market. We present COordinate Rotation DIgital Computer (CORDIC) Instruction Set Architecture (ISA) extensions that speed up the LMMSE equalization algorithm. The costs and benefits of the ISA extensions are evaluated on the Sandbridge Sandblaster 3000 (SB3000) low-power, multithreaded SDR processor. The proposed ISA extensions provide significant performance improvements with little hardware overhead, while improving the accuracy of the LMMSE Equalizer.
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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) | 0.004 | 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".