Distributed fast decodable space-frequency coding for CoMP OFDM cellular networks
Bibliographic record
Abstract
This paper presents a distributed fast decodable space-frequency coding (FD-SFC) scheme suitable for Coordinated Multi-Point (CoMP) downlink transmission in an OFDM cellular wireless network to mitigate ICI while benefiting from spatial diversity by grouping cell-edge users (UE) and sharing sub-carriers among neighbor cells. A distributed decoder is proposed to reduce the UE decoding complexity to 50% of that of the optimal decoder for the same non-distributed FD-SFC. Achievable transmission rate (in b/s/Hz) versus UE relative distance to its base-station (eNodeB) of the proposed scheme is studied in comparison with the Alamouti CoMP and non-CoMP schemes, and its performance upper and lower bounds are derived. It is shown that the proposed scheme outperforms both Alamouti-CoMP and non-CoMP schemes in serving cell-edge UE's. The derived performance bounds are then used to establish the UE relative distance threshold for switching between the proposed CoMP and non-CoMP modes to enhance the overall performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".