Power Allocation for Decode-and-Forward Cellular Relay Network with Channel Uncertainty
Bibliographic record
Abstract
In this paper, we propose centralized and distributed power allocation algorithms for a multi-user, multi relay cellular network using decode-and-forward (DF) cooperation strategy taking channel uncertainty into account. The objective is to minimize the total uplink power of the network taking each user's target data rate as the quality of service (QoS) constraint under imperfect channel state information (CSI). We consider the worst-case optimization approach, in which QoS constraint is satisfied for all channels contained in some uncertainty region. First, a centralized power allocation scheme is developed to optimally allocate the power among the users and the relay nodes. Then, a suboptimal distributed algorithm is proposed based on a standard primal decomposition approach, where each relay can independently and separately minimize its own power. The proposed solutions are based on second order cone programming (SOCP), which is computationally efficient. Simulation results show that the performance of the suboptimal distributed solution is near-optimal and reveals the fact that DF cooperation strategy is more efficient in total power reduction and more robust under channel uncertainty over non-cooperative systems.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".