Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks
Bibliographic record
Abstract
The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".