Distributed Uplink Power Control for Multi-Cell Cognitive Radio Networks
Bibliographic record
Abstract
We present a distributed power control algorithm to address the uplink interference management problem in cognitive radio networks where the underlaying secondary users (SUs) share the same licensed spectrum with the primary users (PUs) in multi-cell environments. Since the PUs have a higher priority of channel access compared to the SUs, minimal number of SUs should be gradually removed, subject to the constraint that all primary users are supported with their target signal-to-interference-plus-noise ratios (SINRs), which is assumed feasible. In our proposed algorithm, each primary user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm (TPC). Each transmitting SU employs the TPC as long as the total received power at the primary receiver is below a given threshold; otherwise, it decreases its transmit power in proportion to the ratio between the given threshold and the total received power at the primary receiver, which is referred to as the total received-power-temperature. We show that our proposed distributed power-update function has at least one fixed-point. We also show that our proposed algorithm not only improves the number of supported SUs but also guarantees that all primary users are supported with their (feasible) target-SINRs. Finally, we also propose an enhanced power control algorithm that achieves zero-outage for PUs and a better outage ratio for SUs. To this end, we provide a robust power control method that considers the uncertainties in channel gains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".