Relay selection and resource allocation for multi-user cooperative LTE-A uplink
Bibliographic record
Abstract
Cooperative relaying is a promising technique for Long Term Evolution Advanced (LTE-A) networks to satisfy high throughput demand and support heterogeneous communication services with diverse quality-of-service (QoS) requirements. However, efficient relay selection as well as resource allocation are critical in such a network when multiple users and multiple relays are considered. In this paper, a resource allocation problem of maximizing the total achievable throughput for multi-user cooperative LTE-A uplink system considering heterogeneous services is investigated. An optimal joint relay selection, subcarrier assignment and power allocation scheme under total power constraint is proposed. The optimization problem is formulated as a convex optimization problem and solved by decomposing it into a hierarchy of subproblems with reduced computational complexity. The subgradient method is used to find the Lagrange multipliers, which helps to obtain the optimal solution. Numerical results show that our approach supports heterogeneous services while guaranteeing each user's QoS requirements with slight total system throughput degradation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".