Selective DF relaying in multi-relay networks with different modulation levels
Bibliographic record
Abstract
Despite the rich literature on cooperative networks, employment of different modulation levels by the source and relay terminals has not been investigated thoroughly from the physical layer perspective. In this paper, we investigate the bit error rate (BER) performance of selective relaying in a multi-relay decode-and-forward cooperative network where the source and the relays transmit using different modulation levels. Specifically, we derive a closed form expression for the end-to-end (uncoded) BER. To draw further insights on the BER performance, we also provide a simpler approximate BER expression that is accurate in the high signal-to-noise ratio regime. Finally, simulation results are presented to verify the analytical results. The derived BER expressions can be utilized in various other scenarios in which the destination selects the best signal (in terms of minimizing BER) among a set of signals which use different modulation levels. The set of signals to choose from may have already been received through orthogonal channels (selection combining), or this signal set may correspond to a set of “candidate” transmissions. The latter scenario is often referred to as selective transmission; applications of this scenario include selective relaying (the setting in this paper), fast base-station selection, and coordinated multipoint transmission and reception (CoMP).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".