A decision theoretic approach for clustering and rate allocation in coordinated multi‐point (CoMP) networks with delayed channel state information
Bibliographic record
Abstract
Abstract Coordinated multi‐point (CoMP) is a promising technique in next generation cellular networks. Compared with traditional mobile networks, one of the important design problems in CoMP is clustering, which decides how the base stations cooperate with each other. Channel state information (CSI) is needed in clustering decisions in CoMP. Most previous works assume that perfect CSI is available. However, practical systems suffer from constraints imposed by backhaul networks, which are used for CSI exchange. In this paper, we study the clustering and rate allocation problem in CoMP with delayed CSI. We present a decision theoretic approach to this problem. Specifically, we model such a system in the framework of networked Markov decision process (networked‐MDP) with delays, which is equivalent to a partial observable Markov decision process (POMDP). We derive an optimal policy for such POMDP with low computation complexity. Simulation results are provided to show promising gain achieved in the proposed scheme over existing schemes especially when the delay is large and the channel coherence time is small. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".