Cross-layer resource allocation approach for multi-hop distributed cognitive radio network
Bibliographic record
Abstract
In multi-hop distributed cognitive radio network, link layer resource allocation must consider the information about number of hops packets have already traveled in the network in order to optimize the overall resource utilization. The loss of a packet after traveling some hops results in waste of all the resources allocated to it in previous hops. The existing resource allocation schemes may not provide optimal resource utilization in such network as this issue has been greatly ignored. Therefore, in this paper, we propose a scheme to allocate transmit power to different packets favoring those which have traveled more hops before reaching a particular node. We present a cross-layer approach in which link layer gets the hop-count information from network layer module. Distributed implementation is possible with the proposed scheme because each node can access this information. We formulate the power allocation problem as a convex optimization problem and obtain its analytical solution by using Lagrangian duality. Simulation results show that the proposed scheme is capable of minimizing wastage of network resources used by packets in their previous hops without any degradation in throughput and outage performance.
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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 | 0.001 |
| 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".