Antenna Selection for Space-Time Trellis Codes Over Block Rayleigh Fading Channels
Bibliographic record
Abstract
This paper examines the performance of space- time trellis codes (STTCs) over block Rayleigh fading channels with receive antenna selection. Antenna selection is performed based on maximizing the instantaneous received signal-to-noise ratio (SNR). We derive explicit upper bounds on the pairwise error probability and show that the resulting diversity order deteriorates with antenna selection and becomes a function of the number of selected antennas. We provide numerical examples and simulation results that validate these theoretical findings. We remark that the same result holds for fast fading channels, as well as for quasi-static fading channels when the underlying STTC is rank deficient. However, when the channel is quasi-static fading and the STTC is full rank, the diversity order is maintained with antenna selection. In contrast, when the underlying code is an orthogonal space-time block code (OSTBC), the diversity order is always maintained with antenna selection regardless of the type of fading involved. The same results hold when the OSTBC is concatenated with an outer channel code. These findings render STTCs when antenna selection is employed unattractive.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".