Performance Analysis and Multi-Stage Iterative Receiver Design for Concatenated Space- Frequency Block Coding Schemes
Bibliographic record
Abstract
This paper presents performance analysis and computationally efficient receiver design for concatenated space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems. Firstly, we present a simple method to approximate the theoretical bit error rate performance of the optimal maximum likelihood (ML) receiver for concatenated SFBC-OFDM systems. Next, we propose a low complexity multistage iterative QR decomposition based successive interference cancellation (QR-SIC) detector for OFDM systems employing the concatenated SFBC strategy. The proposed detector utilizes a Turbo-like iterative QR-SIC algorithm that exploits both spatial and frequency diversities inherent in multi-input multioutput (MIMO) multi-path fading channels. The performance of the proposed QR-SIC receiver is evaluated via Monte Carlo simulations. Our results show that the performance of the proposed receiver can approach the theoretical BER performance of the optimal ML receiver at high signal-to-noise ratios. In addition, we also compare the performance and complexity of the proposed QR-SIC detector with a Turbo-based maximum aposteriori (MAP) demodulator. These comparisons show that the proposed QR-SIC scheme performs reasonably well with respect to the MAP demodulator at higher iterations while attaining a much lower complexity than the MAP demodulator.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".