SR-ARQ for MIMO OFDM Systems with Channel State Information Only at the Receiver
Bibliographic record
Abstract
In this paper, we compare two selective-repeat automatic-repeat-request (SR-ARQ) protocols for spatial multiplexintiplexingg multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexed (OFDM) systems assuming perfect channel state information (CSI) only at the receiver. These two SR-ARQ protocols differ in the number of packets transmitted simultaneously. In one protocol, a single packet is transmitted on all subcarriers from all antennae at the same time. In the other protocol, multiple packets are simultaneously transmitted. For the latter protocol, we also consider schemes that differ in the way that the subcarriers transmitted from different antennae are grouped to support the transmission of multiple packets in parallel. We compare the throughput and the resequencing delay of these SR-ARQ protocols in a frequency selective fading channel. Simulation results suggest that in such a system, the single ARQ protocol is the best choice. If we have to transmit multiple packets in parallel, each packet should be transmitted over a subband of adjacent subcarriers emitted from all transmit antennae.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".