Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment
Bibliographic record
Abstract
In a cellular network, the channel assignment is a mechanism that assigns channels to mobile users in order to establish a communication between a mobile terminal and a base station. It is important to determine an optimal allocation of channels that makes efficient use of channels, which are limited resources, and minimizes call-blocking and call-dropping probabilities. In this paper, we present two efficient integer linear program (ILP) formulations that optimally allocate a channel to an incoming call from a pool of available channels such that the "hard" and "soft" constraints are satisfied. The first formulation does not allow channel reassignment of existing calls, while the second formulation allows such reassignment. Our formulations can handle hard constraints, that includes both co-site and adjacent channel constraints, in addition to the standard co-channel constraints. The simplified problem (with only co-channel constraints) can be treated as a special case of our formulation. In addition to the above hard constraints, we also consider soft constraints such as the packing condition, resonance condition and limiting rearrangements to further improve performance. Experimental results on a benchmark 49 cell environment with 70 channels are used to validate our approach.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".