Distributed Connection Admission Control and Dynamic Channel Allocation in Ad hoc-Cellular Networks
Bibliographic record
Abstract
Recently, we developed a framework, namely multi-hop TDD CAC and DCA (MTCD), for optimal centralized DCA in multi-hop 4G/4G+ networks. We also proposed two CAC schemes that use a predefined optimization algorithm to ensure that admission is based, to a large extent, on topology maintenance, energy conservation, load balancing, and fairness. In this paper, we address the centralized limitation of MTCD and its computational intractability by proposing a new distributed DCA scheme for autonomous, multi-hop 4G/4G+ networks. The proposed algorithm is termed distributed multi-hop CAC and DCA (DMCD). Unlike MTCD, DMCD allocates channels in a fully distributed manner. In DMCD, channel assignment is not solely contingent upon simple graph coloring, but is also based on the load factors and interference computed by the wireless stations. We also propose distributed per-hop throughput-based CAC (DPTC), which facilitates the gradual admission of the wireless stations pertaining to an A-Cell route in conjunction with DCA using DMCD. To our best knowledge, this is the first solution proposed for distributed CAC in multi-hop 4G/4G+ wireless systems
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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 | 0.000 | 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".