Joint optimization of relay strategies and resource allocations in cooperative cellular networks
Bibliographic record
Abstract
This paper considers a wireless cooperative cellular data network with a base station and many subscribers in which the subscribers have the ability to relay information for each other to improve the overall network performance. For a wireless network operating in a frequency-selective slow-fading environment, the choices of relay node, relay strategy, and the allocation of power and bandwidth for each user are important design parameters. The design challenge is compounded further by the need to take user traffic demands into consideration. This paper proposes a centralized utility maximization framework for such a network. We show that for a cellular system employing orthogonal frequency-division multiple-access (OFDMA), the optimization of physical-layer transmission strategies can be done efficiently by introducing a set of pricing variables as weighting factors. The proposed solution incorporates both user traffic demands and the physical channel realizations in a cross-layer design that not only allocates power and bandwidth optimally for each user, but also selects the best relay node and best relay strategy (i.e. decode-and-forward vs. amplify-and-forward) for each source-destination pair
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".