Adaptive channel allocation for enabling target SINR achievability in power-controlled wireless networks
Bibliographic record
Abstract
This paper offers a new insight to the fundamental problem of efficient admission control in arbitrary power-controlled wireless networks with an unknown call arrival distribution. Active transmitter-receiver pairs are assumed to (i) communicate simultaneously over shared channels, (ii) define target signal-to-interference and noise ratios (SINRs) by nonlinear functions of channel interference, and (iii) use adaptive power control to maintain the actual SINR at the target level in response to interference variations. Unlike other studies, in this study, power control with limited dynamic range and both the discrete-time and the continuous-time dynamics is explicitly considered, as well as the effects of stochastic radio propagation phenomena. Without relying on a priori assumptions, we first define sufficient conditions for a channel allocation mechanism to ensure the SINR constraints in cooperation with the deployed power control mechanism. We use the concept of Lyapunov stability as a cross-layer optimization criterion. Subsequently, we focus on the widely assumed case of SINR targets being defined by linear functions of interference, and show that such targets can be achieved if hii> |Ai|¿ j¿i hij¿i, where hijis the channel gain between the transmitter of link j and the receiver of link i, and Ai is the slope of the linear definition of the target SINR. This knowledge allows us to propose a simple distributed algorithm for implementing an admission control mechanism that (i) uses interference and pilot signal measurements as its only decision-making input, and (ii) allows links to adaptively adjust the SINR targets within the system stability bounds. This mechanism is shown to outperform the carrier sensing approach (CSMA/CA) for admission control.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".