Resource Allocation for Selective Relaying Based Cellular Wireless System with Imperfect CSI
Bibliographic record
Abstract
In this paper, we propose robust power allocation and admission control schemes for providing probabilistically constrained quality of service (QoS) in selective relaying based decode-and-forward (DF) cooperative cellular systems. The proposed schemes are robust against imperfect channel state information (CSI) in slow fading while optimizing the total uplink transmit power in these cooperative wireless networks. At first, we derive novel closed-form solutions for the optimization problem, where the objective is to minimize total uplink transmit power while meeting the probabilistic QoS guarantees for a given number of admitted users. This is achieved by approximating the probabilistic optimization problem into a convex deterministic form and then by deriving closed form analytical solutions for power allocation using Karush-Kuhn-Tucker (KKT) conditions. The closed-form property of these solutions allows us later to develop a very low-complexity suboptimal algorithm for joint admission control and power allocation in presence of imperfect CSI and selective relaying. We also conduct comprehensive simulation experiments to demonstrate the effectiveness of our proposed schemes and to highlight the benefits gained from considering channel estimation errors in resource allocation for cooperative cellular systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".