Cross-Layer Criterion for MIMO Spatial Multiplexing Systems with Imperfect CSI
Bibliographic record
Abstract
In this paper, we investigate a cross-layer transmit antenna selection (T-AS) approach for multiple-input multiple-output spatial multiplexing (MIMO-SM) systems, that employ decision-feedback detector (DFD), over Ricean flat-fading channels. The selected transmit antennas are those that maximize the link layer throughput of MIMO channels. A closed-form expression for the system throughput with imperfect channel state information (CSI) is derived. Extensive simulation results are provided for the system performance assessment, showing that the cross-layer T-AS scheme always assigns the transmission to the antenna combination which sees better channel conditions, resulting in a substantial improvement over the optimal capacity- based T-AS approach. Our results show that the capacity-based T-AS is more robust to imperfect channel estimation. However, in all cases, the cross-layer T-AS delivers higher throughput gains than the capacity-based T-AS.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".