Joint Relay Selection and Opportunistic Source Selection in Bidirectional Cooperative Diversity Networks
Bibliographic record
Abstract
Relay selection (RS) has widely been studied in the literature, and an opportunistic source selection (OSS) protocol with a single relay has recently been proposed. Since RS and OSS could individually improve the performance of cooperative diversity networks, optimum combining of RS and OSS is an interesting topic. In this paper, we optimally combine RS and OSS in the sense that the mutual information is maximized, and we propose a joint RS-OSS protocol in an amplify-and-forward (AF)-based bidirectional cooperative diversity network, which consists of two different end-sources and multiple relays. In this network, a best source is selected to transmit data to the other source with the help of a selected best relay in an opportunistic manner, depending on channel conditions. Then, to show the performance of the joint RS-OSS, we derive the outage probability and the average bit error rate (BER) forM-quadrature amplitude modulation (QAM). Numerical results confirm that the derived outage probability and the average BER expressions are very accurate. In addition, we find that the proposed joint RS-OSS considerably outperforms both RS and OSS in terms of outage probability and average BER and that the performance is highly dependent on relay location. The obtained outage probability and average BER will help the design of reliable bidirectional cooperative diversity networks in determining the system parameters, such as relay location, and the transmission power at source and relay.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".