QoS‐based power allocation for cognitive radios with AMC and ARQ in Nakagami‐<i>m</i> fading channels
Bibliographic record
Abstract
Abstract This paper presents power allocation schemes to maximize the effective capacity (EC) of a secondary user (SU) communications link using adaptive modulation and coding (AMC) in an underlay cognitive radio Nakagami‐m block‐fading environment to meet target quality‐of‐service (QoS) requirements in terms of delay‐outage probability and packet error rate constraints. The SU transmission parameters are chosen such that the primary user imposed interference power constraint (IPC) is satisfied. Three different types of IPCs, namely average interference power, peak interference power and interference power outage, are considered. For each IPC, the analytical solutions for choosing the AMC mode and power allocation in each fading block, and the corresponding SU achievable EC under given QoS requirements are derived. Furthermore, we investigate the performance of a hybrid automatic repeat request (ARQ)/AMC and obtain the closed‐form packet loss rate expression. Illustrative results show the effects of the IPC, fading duration and fading severeness on the SU achievable EC under given QoS requirements. It is shown that for loose delay‐outage requirements, average interference power and interference power outage constraints give higher SU EC than peak interference power constraint. However, for more stringent delay‐outage requirements, the SU achievable EC for the three IPC is significantly reduced. The results also indicate that ARQ is helpful to significantly reduce the packet loss rate for loose delay constraint. However, ARQ increases the delay and is not effective for stringent delay‐outage requirements. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".