Cooperative AF Relaying in Spectrum-Sharing Systems: Performance Analysis under Average Interference Power Constraints and Nakagami-m Fading
Bibliographic record
Abstract
Since the electromagnetic spectrum resource is becoming more and more scarce, improving spectral efficiency is becoming extremely important for the sustainable development of wireless communication systems and services. Integrating cooperative relaying techniques into spectrum-sharing cognitive radio systems sheds new light on higher spectral efficiency. In this paper, we analyze the end-to-end performance of cooperative amplify-and-forward (AF) relaying in spectrum-sharing systems. In order to achieve the optimal end-to-end performance, the transmit powers of the secondary source and the relays are optimized with respect to average interference power constraints at primary users and Nakagami-m fading parameters of interference channels (for mathematical tractability, the desired channels from secondary source to relay and from relay to secondary destination are assumed to be subject to Rayleigh fading). Also, both partial and opportunistic relay-selection strategies are exploited to further enhance system performance. Based on the exact distribution functions of the end-to-end signal-to-noise ratio (SNR) obtained herein, the outage probability, average symbol error probability, diversity order, and ergodic capacity of the system under study are analytically investigated. Our results show that system performance is dominated by the resource constraints and it improves slowly with increasing average SNR. Furthermore, larger Nakagami-m fading parameter on interference channels deteriorates system performance slightly. On the other hand, when interference power constraints are stringent, opportunistic relay selection can be exploited to improve system performance significantly. All analytical results are corroborated by simulation results and they are shown to be efficient tools for exact evaluation of system performance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".