Worst-case analysis of a dynamic channel assignment strategy (extended abstract)
Bibliographic record
Abstract
We consider the problem of channel assignment in cellular networks with arbitrary reuse distance. A simple and commonly used strategy is called fixed channel assignment, in which base stations can only use channels from fixed sets that are pre-computed to avoid interference with neighbors. On the other hand, dynamic channel assignment makes all radio channels available to all calls in principle: a station may use any channels that are currently unused in any neighboring cells with whom there might be a possibility of interference. In between are borrowing strategies, where a fixed number of channels is reserved per node, but borrowing of idle channels is allowed. While it has been shown that dynamic channel assignment can work better than fixed channel assignment in some situations, its worst case performance, in terms of the number of channels used, has not been studied. We show upper bounds and lower bounds for the competitive ratio of a previously proposed and widely studied version of dynamic channel assignment, which we refer to as the greedy algorithm, for arbitrary reuse distance r. Our main result is that this type of dynamic assignment, though generally regarded to provide higher capacity than fixed assignment or borrowing approach, has in fact, a poorer worst-case performance. For any r, the greedy algorithm is shown to have performance ratio at most 6. For r = 2, we give tight bounds on its performance, and for r = 2 or 3, we show that previously proposed borrowing strategies actually do better in the worst case. We also show a simple borrowing strategy for arbitrary reuse distance and prove bounds on its performance.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".