Channel quantization and bit allocation in multi-source multi-relay cooperative networks
Bibliographic record
Abstract
This paper investigates quantization of the channel state information (CSI) in a cooperative cellular network. This CSI, delivered to some central node, is to be used to allocate resources in order to maximize the sum-rate in a multi-source network assisted by multiple relays. We start by deriving tight bounds on the performance loss due to quantization and then, through minimizing these bounds, we propose an efficient quantization and bit allocation technique. To this end, we present the bound on the overall performance loss as the sum of individual terms where each term represents the loss caused by the quantization of the CSI for an individual link. Then we show that each of these terms can be written as the product of two important components: the standard quantization error, and the link coefficient which is only a function of the large scale fading parameters. The quantization error is similar for all links and leads to the optimal quantization problem. Then using a simple bound on the quantization error, and also considering the link coefficients, we allocate bits to quantize each link. Further, we discuss the candidates in the network to play the role of the central node. A numerical example shows that the overall sum-rate (overall CSI demand), is significantly increased (decreased) through bit allocation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".