Bibliographic record
Abstract
Relay cooperation and integrated microwave and millimeter-wave (mm-wave) dual-band communication are likely to play key roles in 5G. In this paper, we study a two-user uplink scenario in such dual-bands, modeled as a multiple-access relay channel (MARC), where two sources communicate to a destination assisted by a relay. However, unlike the microwave band, transmitters in the mm-wave band must employ highly directional antenna arrays to combat the ill effects of severe path-loss and small wavelength. The resulting mm-wave links are point-to-point and highly directional, and thus used to complement the microwave band by transmitting to a specific receiver. For such MARCs, the capacity is partially characterized for sources that are near the relay in a joint sense over both bands. We then study the impact of the mm-wave spectrum on the performance of such MARCs by characterizing the transmit power allocation scheme for phase faded mm-wave links that maximizes the sum-rate under a total power budget. The resulting scheme adapts the link transmission powers to channel conditions by transmitting in different modes, and all such modes and corresponding conditions are characterized. Finally, we study the properties of the optimal link powers and derive practical insights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".