Spectral Efficiency and Outage Performance for Hybrid D2D-Infrastructure Uplink Cooperation
Bibliographic record
Abstract
We propose a time-division uplink transmission scheme that is applicable to future cellular systems by introducing hybrid device-to-device (D2D) and infrastructure cooperation. We analyze its spectral efficiency and outage performance and show that compared to existing frequency-division schemes, the proposed scheme achieves the same or better spectral efficiency and outage performance while having simpler signaling and shorter decoding delay. Using time-division, the proposed scheme divides each transmission frame into three phases with variable durations. The two user equipments (UEs) partially exchange their information in the first two phases, then cooperatively transmit to the base station (BS) in the third phase. We further formulate its common and individual outage probabilities, taking into account outages at both UEs and the BS. We analyze this outage performance in Rayleigh fading environment assuming full channel state information (CSI) at the receivers and limited CSI at the transmitters. Results show that comparing to non-cooperative transmission, the proposed cooperation always improves the instantaneous achievable rate region even under half-duplex transmission. Moreover, as the received signal-to-noise ratio increases, this uplink cooperation significantly reduces overall outage probabilities and achieves the full diversity order in spite of additional outages at the UEs. These characteristics of the proposed uplink cooperation make it appealing for deployment in future cellular networks.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".