WLC32-1: ARQ for MIMO OFDM Systems with Non-Linear Preprocessing
Bibliographic record
Abstract
In this paper, we develop a selective-repeat automatic-repeat-request (SR-ARQ) transmission scheme for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with non-linear preprocessing assuming perfect channel state information at both the transmitter and the receiver. In such a system in every time slot for ARQ transmission, there are a group of parallel channels of equal rate and differing error probability available between the transmitter and the receiver. Based on the knowledge of the signal to noise ratio (SNR) of these parallel channels, which is known to both the transmitter and the receiver, we first propose a dynamic parallel channel grouping rule to group parallel channels into effective channels, over which one packet is transmitted in one time slot, and we prove that this dynamic channel grouping rule will achieve maximum throughput. Furthermore, we adopt the dynamic channel assignment rule introduced by Shacham and Shin to assign packets to the effective channels in order to reduce resequencing delay. simulations in a frequency selective fading channel demonstrate the advantage of the proposed dynamic SR- ARQ transmission scheme over systems with a static parallel channel grouping rule and a static channel assignment rule in terms of throughput and resequencing delay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".