N Plus Normalized Threshold Opportunistic Relay Selection with Outdated Channel State Information
Bibliographic record
Abstract
In this paper, we propose a new multiple relay selection scheme with the availability of outdated channel state information (CSI) for decode and forward (DaF) relay systems, namely the N plus normalized threshold opportunistic relay selection (N+NT-ORS). In particular, we first opportunistically select N best relays out of all relays in the decoding subset with respect to relay-to-destination signal-to-noise ratios (SNRs). Then the ratios of the SNRs on the rest of the relays in the decoding subset to that of the N-th highest SNR are tested against a normalized threshold μ ∈ [0,1] and only those relays passing this test are selected in addition to the N best relays. Through the derivation of the moment generating function (MGF) of SNR, together with the relationship between MGF and outage probability, we obtain a closed-form expression for the outage probability. In addition to analyzing the asymptotic diversity order for N+NTS-ORS, we also outline a distributed N+NT ORS selection protocol without the need of global CSI at each relay. Numerical results confirm the correctness of the derivation and show how N+NT-ORS outperforms its existing counterparts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".