Capacity and power allocation for spectrum-sharing communications in fading channels
Bibliographic record
Abstract
This paper investigates the fundamental capacity limits of opportunistic spectrum-sharing channels in fading environments. The concept of opportunistic spectrum access is motivated by the frontier technology of cognitive radio which offers a tremendous potential to improve the utilization of the radio spectrum by implementing efficient sharing of the licensed spectrum. In this spectrum-sharing technology, a secondary user may utilize the primary user's licensed band as long as its interference to the primary receiver remains below a tolerable level. Herein, we consider that the secondary user's transmission has to adhere to limitations on the ensuing received power at the primary's receiver, and investigate the capacity gains offered by this spectrum-sharing approach in a Rayleigh fading environment. Specifically, we derive the fading channel capacity of a secondary user subject to both average and peak received-power constraints at the primary's receiver. In particular, considering flat Rayleigh fading, we derive the capacity and optimum power allocation scheme for three different capacity notions, namely, ergodic, outage, and minimum-rate, and provide closed-form expressions for these capacity metrics. Numerical simulations are conducted to corroborate our theoretical results.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".