Improved layered space time architecture over quasi-static fading channels with unequal power allocation and multistage decoding
Bibliographic record
Abstract
The Bell labs layered space-time (BLAST) architecture pioneered by Foschini was found to achieve high spectral efficiency with moderate complexity. However, the redundancy in error correcting codes in BLAST receiver is not used in detection improvement, as detection and decoding are carried out separately. In this paper, we investigate a new approach for improved performance of BLAST based on multi- stage decoding (MSD) and unequal transmit power allocation among layers, for transmission over flat quasi-static Rayleigh fading channels. The use of MSD exploits the inherent redundancy in the employed channel codes to improve detection in BLAST without the need for complex iterative decoding approaches. In addition, we investigate unequal transmit power allocation among layers for transmission over flat quasi-static Rayleigh fading channels. We first derive a theorem for power allocation that maximizes outage capacity. We then find the unequal power allocation required to guarantee equal outage capacities among layers in BLAST combined with MSD detection. The proposed power allocation simplifies implementation and improves error performance. Simulation results show that the proposed architecture significantly outperforms existing BLAST schemes in terms of error performance for transmission over flat quasi-static Rayleigh fading channels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".