Distributed Opportunistic Channel Access in Wireless Relay Networks
Bibliographic record
Abstract
In this paper, the problem of distributed opportunistic channel access in wireless relaying is investigated. A relay network with multiple source-destination pairs and multiple relays is considered. All source nodes contend through a random access procedure. A winner source may give up its transmission opportunity if its link quality is poor. In this research, we apply the optimal stopping theory to analyze when a winner source should give up its transmission opportunity. By assuming the winner source has channel state information (CSI) of links from itself to relays and from relays to its destination, the existence of an optimal stopping strategy is rigorously proved. The optimal stopping strategy has a pure-threshold structure. The case when a winner source does not have CSI of links from relays to its destination is also studied. Two stopping problems exist, one in the main layer (for channel access of sources), and the other in the sub-layer (for channel access of relays). An intuitive stopping strategy, where the main layer (for the first hop) and sub-layer (for the second hop) maximize their throughput respectively, is derived. The intuitive stopping strategy is shown to be non-optimal. An optimal stopping strategy is then derived theoretically. In either the intuitive stopping strategy or the optimal stopping strategy, the main-layer stopping rule has a pure-threshold structure, while the sub-layer stopping rule has a threshold determined by the channel realization in the preceding first-hop transmission. Our research reveals that multi-user (including multi-source and multi-relay) diversity and time diversity can be utilized in a relay network by our proposed strategies. The effectiveness of the strategies is validated by numerical and simulation results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".