Joint channel estimation and data detection for OFDM systems via sphere decoding
Bibliographic record
Abstract
We develop blind and semi-blind joint estimators of the channel impulse response (CIR) and data symbols for orthogonal frequency division multiplexing (OFDM) systems over a frequency selective fading channel. Using the maximum likelihood (ML) criterion, we derive two estimators for the transmit data symbols that require minimizing a complex, integer quadratic x/sup T/Gx* where x is a data vector and the matrix G characterizes each estimator. Avoiding computationally prohibitive exhaustive search, we use both sphere decoding (SD) and V-BLAST algorithms. We also modify the SD algorithm for our complex OFDM system, to handle any M-PSK, and incorporate a reduced complexity SD into the OFDM system. The quadratic for the blind estimator suffers from rank deficiency. We give an efficient solution to the rank deficiency problem. Simulation results confirm the good performance of our proposed estimators.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".