Design of an Adaptive MIMO System using Linear Dispersion Code
Bibliographic record
Abstract
In this paper, we present a new adaptation scheme for MIMO systems using linear dispersion code. The statistics of signal-to-interference-noise for a MIMO transceiver using linear dispersion code and linear minimum-mean-square-error (MMSE) receiver over a Rayleigh fading channel is studied. The associated probability density function of the signal-to interference-noise is derived and verified. The average BER over MIMO fading channel for a given constellation using the MMSE receiver is calculated numerically. The numerical and simulation results match very well. With the statistics as a guideline, we study new design of selection-mode adaptation using a linear dispersion code. A new adaptive parameter, called space-time symbol rate, can be applied due to the use of linear dispersion code. An adaptive algorithm for the selection-mode adaptation is proposed. Based on the proposed algorithm, two adaptive techniques using constellation and space-time symbol rate are studied, respectively. If constellation and space-time symbol rate are considered jointly, more selection modes can be available. Theoretical analysis demonstrates that the average transmission rate of selection-mode adaptation can be improved in this case. Simulation results are provided to show the benefits of our new design.
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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 | 0.000 |
| 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".