Design and Performance Analysis of MEC-Aided LoRa Networks With Power Control
Bibliographic record
Abstract
In this paper, we propose a single-cell mobile edge computing (MEC) assisted long-range (LoRa) network with power control, which includes a gateway, a MEC server, and many randomly distributed end-devices (EDs). In the proposed system, task packet messages can be computed locally through EDs and offloaded to the MEC server for edge computation; both computations are performed in parallel. Following the stochastic geometry theory, we adopt the homogeneous Poisson point process (PPP) to capture the randomness of the EDs' position and model the interference devices as PPP under the pure ALOHA. In this model, we consider both the interference caused by the same spreading factors (co-SF) and that caused by different spreading factors (inter-SF) during task offloading. Furthermore, we derive precise and approximated expressions of the computation offloading success probabilities for the proposed network, which are then verified by simulations. This is followed by the analysis of the impact of the power control on the network performance of the proposed network. The results reveal that power control can considerably improve the performance in the low-density EDs scenario, as well as slightly improve in the high-density EDs scenario. Finally, we investigate the performance of the proposed network by comparing three SF allocation schemes-namely, exponential windowing (EW), equal-interval-based (EIB), and equal-area-based (EAB) schemes. The results reveal that we can improve the performance by assigning a lower SF for a large number of EDs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".