Distributed Gateway Selection for M2M Communication in Cognitive 5G Networks
Why this work is in the frame
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Bibliographic record
Abstract
M2M communication is an important component for future wireless networks. M2M systems consist of a large number of devices that can operate with minimum or no human intervention. However, spectrum demand rises exponentially with the increase in the number of connected devices. Cognitive 5G networks are key to address the issue of spectrum scarcity. Further, use of multiple gateways in cognitive 5G networks for M2M communication can increase system throughput, coverage, and energy efficiency. Nevertheless, using multiple gateways for the secondary M2M devices may cause interference to the primary M2M devices. Existing gateway selection protocols for cognitive M2M communication mostly use single channel CSMA, and thus are not efficient in terms of reducing the interference. Thus, in this article, we propose a DGAP based on multi-channel CSMA for M2M communication in 5G networks. Further, we propose a Lo-DGAP, where each gateway transmits only the worst primary M2M device information rather than transmitting all neighboring primary M2M device information. The proposed Lo-DGAP increases the throughput of the system by reducing the message header payload and is also energy- efficient. Simulation results demonstrate the effectiveness of the proposed schemes in terms of network lifetime and energy consumption.
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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 it