Relay selection and resource allocation for multi-user cooperative LTE-A uplink
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it