End-to-End Performance of Cooperative Relaying in Spectrum-Sharing Systems With Quality of Service Requirements
Bibliographic record
Abstract
We propose adopting a cooperative relaying technique in spectrum-sharing cognitive radio (CR) systems to more effectively and efficiently utilize available transmission resources, such as power, rate, and bandwidth, while adhering to the quality of service (QoS) requirements of the licensed (primary) users of the shared spectrum band. In particular, we first consider that the cognitive (secondary) user's communication is assisted by an intermediate relay that implements the decode-and-forward (DF) technique onto the secondary user's relayed signal to help with communication between the corresponding source and the destination nodes. In this context, we obtain first-order statistics pertaining to the first- and second-hop transmission channels, and then, we investigate the end-to-end performance of the proposed spectrum-sharing cooperative relaying system under resource constraints defined to assure that the primary QoS is unaffected. Specifically, we investigate the overall average bit error rate (BER), ergodic capacity, and outage probability of the secondary's communication subject to appropriate constraints on the interference power at the primary receivers. We then consider a general scenario where a cluster of relays is available between the secondary source and destination nodes. In this case, making use of the partial relay selection method, we generalize our results for the single-relay scheme and obtain the end-to-end performance of the cooperative spectrum-sharing system with a cluster of$L$available relays. Finally, we examine our theoretical results through simulations and comparisons, illustrating the overall performance of the proposed spectrum-sharing cooperative system and quantify its advantages for different operating scenarios and conditions.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".