Power and resource allocation for orthogonal RDF and NDF relay systems with QoS and peak-power constraints
Bibliographic record
Abstract
The availability of a relay to assist a source in communicating with its intended destination has the potential to provide a significant reduction in the power required to achieve a specified level of quality-of-service (QoS). In this paper we consider the case in which the relay operates in a channel that is orthogonal in time to that used by the source, and employs regenerative decode-and-forward (RDF) or non-regenerative decode-and-forward (NDF) relaying. We assign a price to the power of the relay relative to that of the source, and we consider the problem of jointly optimizing the source power, the relay power and, in the case of NDF relaying, the fractions of the time block allocated to the source and relay, so as to minimize the total cost of the power required to achieve a specified target rate, subject to constraints on the peak power levels employed by the source and the relay. In the RDF case, we obtain a closed-form solution to the problem. This solution clearly identifies when the peak power constraints are too tight to enable the target rate to be achieved. In the NDF case, the natural formulation of the problem is not convex, but we show that it can be precisely transformed into a convex problem that can be efficiently solved. Our numerical examples show that in a Rician fading environment the flexibility in channel resource allocation inherent in the NDF relaying strategy enables the target rate to be achieved on a significantly larger proportion of the channels.
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 | 0.003 |
| 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 | 0.000 |
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".