Machine-to-Machine Communications With Massive Access: Congestion Control
Bibliographic record
Abstract
With the deployment of machine-to-machine (M2M) communications, it is expected that the number of devices will enormously increase. When these devices attempt to concurrently access the network, a radio access network overload problem arises. In this case, the conventional random access procedure used in Long Term Evolution-Advanced (LTE-A) networks is rendered inefficient due to the frequent collisions that lead to excessive delay and resource wastage. In this paper, we propose an efficient scalable overload control algorithm for M2M with massive access. The proposed algorithm can allocate the uplink resources within bounded contention time in a distributed manner. Hence, it can achieve full resource utilization that leads to reduced: access delay, energy consumption, and blocking probability. Additionally, we provide a method for estimating the number of backlogged devices in the network. The performance of the proposed algorithm is evaluated analytically and using simulations. To prove its effectiveness, the performance of the proposed algorithm is compared to the dynamic-access class-barring scheme, where the results depict the superiority of the proposed scheme. Finally, a binary integer programming problem is formulated, where we show that the achieved access delay using the proposed algorithm approaches the optimal value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".