Cross-Layer Scheduling for OFDMA Amplify-and-Forward Relay Networks
Bibliographic record
Abstract
In this paper, we consider cross-layer scheduling for the downlink of amplify-and-forward (AF) relay-assisted orthogonal frequency-division multiple-access (OFDMA) networks. The proposed cross-layer design takes into account the effects of imperfect channel-state information (CSI) at the transmitter (CSIT) in slow fading. The rate, power, and subcarrier allocation policies are optimized to maximize the system goodput (in bits per second per hertz successfully received by the users). The optimization problem is solved by using dual decomposition, resulting in a highly scalable distributed iterative resource-allocation algorithm. We also investigate the asymptotic performance of the proposed scheduler with respect to (w.r.t.) the numbers of users and relays. We find that the number of relays should grow faster than the number of users to fully exploit themultiuser diversity(MUD) gain. On the other hand, diversity from multiple relays can be exploited to enhance system performance when the MUD gain is saturated due to noise amplification at the AF relays. Furthermore, we introduce a feedback-reduction scheme to reduce the computational burden and the required amount of CSI feedback from the users to the relays. Simulation results confirm the derived analytical results for the growth of the system goodput and illustrate that the proposed distributed cross-layer scheduler only requires a small number of iterations to achieve practically the same performance as the optimal centralized scheduler, even if the information exchanged between the base station (BS) and the relays in each iteration is quantized, and the proposed CSI feedback reduction scheme is employed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".