Power Allocation and Asymptotic Achievable Sum-Rates in Single-Hop Wireless Networks
Bibliographic record
Abstract
A network of n communication links operating over a shared wireless channel is considered. Power management is crucial to such interference-limited networks to improve the aggregate throughput. We consider sum-rate maximization of the network by optimum power allocation when conventional linear receivers (without interference cancellation) are utilized. It is shown that in the case of n=2 links, the optimum power allocation strategy is such that either both links use their maximum power or one of them uses its maximum power and the other keeps silent. An asymptotic analysis for large n is carried out to show that in a Rayleigh fading channel the average sum-rate scales at least as log(n). This is obtained by deriving an on-off power allocation strategy. The same scaling law is obtained in the work of Gowaikar et al., where the number of links, their end-points (source-destination pairs), and the relay nodes are optimally chosen all by a central controller. However, our proposed strategy can be implemented in a decentralized fashion for any number of links, arbitrary transmitter-receiver pairs, and without any relay nodes. It is shown that the proposed power allocation scheme is optimum among all on-off power allocation strategies in the sense that no other strategies can achieve an average sum-rate of higher order.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".