Multi-objective MTC device controller resource optimization in M2M communication
Bibliographic record
Abstract
Machine to machine (M2M) communication has received increasing attention in recent years. It exhibits features such as large number of devices and low data rates. In order to accommodate massive, energy efficient M2M traffic and to reduce the access delay and signalling overhead, the recommendation is to introduce a clustered network structure. In this effort, we consider machines (or devices) in a macro cell divided into clusters. The machines belonging to a cluster communicate to the cluster head/controller, which then aggregates the traffic and relays to the eNB. Unlike related work that focuses on formation of clusters and their energy consumption, in this paper we investigate the multi-objective optimization problem of throughput maximization and power control of cluster heads in interference-limited M2M communication. Our objective is to maximize the number of admitted MTC device controllers with least interference caused to conventional (human) devices such that their quality of service (QoS) is not affected by the M2M communications. To maximize the number of machines that can communicate while meeting the interference constraints of human devices and machines themselves, we formulate a mixed-integer non-linear programming (MINLP) problem. OPTI toolbox is used to optimize the power, capacity and maximum number of admitted machines collectively.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.002 | 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".