Max-Max Relay Selection for Relays with Buffers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we propose a new relay selection scheme for half-duplex relays with buffers. The proposed scheme is referred to as max-max relay selection (MMRS) since the relays with the best source-relay and the best relay-destination channels are selected for reception and transmission, respectively, which is only possible without data loss if the relays have buffers of infinite size. To relax this idealized assumption, we propose hybrid relay selection (HRS) for relays with buffers of finite size. HRS is a combination of conventional best relay selection (BRS) and MMRS and takes into account both the channel state and the buffer state for relay selection. We provide a comprehensive analysis of the outage and symbol error probabilities of both MMRS and HRS for a decode-and-forward protocol in Rayleigh fading. This analysis reveals that BRS, HRS, and MMRS achieve the same diversity gain. However, for N relays, MMRS achieves a signal-to-noise ratio (SNR) gain of 3(1-1/N) dB compared to BRS, and HRS closely approaches the SNR gain of MMRS for moderate buffer sizes (e.g. 30 transmission intervals).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it