An integrated design of STBC and signal alignment in MIMO Y channels
Bibliographic record
Abstract
This paper presents the integration of distributed space-time block coding (STBC) and signal alignment (SA) in multiple-input multiple-output (MIMO) Y channels where multiple terminals communicate with each other in a bi-directional manner via the relay. The objective is to take advantage of the STBC diversity gain without losing much of the high bandwidth efficiency in MIMO Y channel schemes with SA alone. The proposed deployment of STBC represents a form of cooperative coding between the terminals and is referred to as networked MIMO. Specifically, by a thoughtful design of pre-processing vectors and scheduling of transmissions, the signals with mutual information in bi-directional links between terminals are aligned into proper spatial dimensions according to the selected space-time block code. The transmission schemes are also optimized to reduce computational complexity and improve time slot utilization. It is demonstrated with simulations that the proposed design in a three-user MIMO channel offers significant coding gain of 25 dB at the bit error rate (BER) of 10-3over the conventional MIMO channel signaling in Rayleigh fading channels. This improvement is achieved at the expense of reducing the bandwidth efficiency by a factor of 7/2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".