Downlink Resource Allocation for OFDMA-Based Multiservice Networks with Imperfect CSI
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Bibliographic record
Abstract
This paper addresses practical implementation issues of resource allocation in OFDM A networks: inaccuracy of channel state information (CSI) available to the resource allocation unit (RAU) and diversity of subscribers' quality of service (QoS) requirements. The resource allocation problem in the considered point-to-multipoint (PMP) network is modeled as a network utility maximization (NUM) problem that allocates subcarriers, rate and power while satisfying orthogonal frequency division multiple access (OFDMA) constraints and QoS constraints defined in the service level agreement. Performance evaluation findings support our theoretical claims: a substantial data rate gain is achieved by considering the CSI imperfection and multiservice classes are supported with QoS guarantees by coordinating with a call admission control (CAC) scheme.
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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 | 0.000 |
| 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