Fountain Coded Cooperative Communications for LTE-A Connected Heterogeneous M2M Network
Bibliographic record
Abstract
Machine-to-machine communication over long-term evolution advanced (LTE-A) network has emerged as a new communication paradigm to support a variety of applications of Internet of Things. One of the most effective techniques to accommodate a large volume of machine type communication (MTC) devices in LTE-A is clustering where devices (nodes) are grouped into number of clusters and forward their traffics to the base station (e.g., LTE eNodeB) through some special nodes called cluster heads (CHs). In many applications, the CHs change location with time that causes variation in distances between neighboring CHs. When these distances increase, the performance of data transmission may degrade. To address this issue, we propose to employ intermediate non-CH nodes as relays between neighboring CHs. Our solution covers many aspects from relay selection to cooperative formation to meet the user's QoS requirements. As the number of total relay plays a significant role in cooperative communications, we first design a rateless-coded-incremental-relay selection algorithm based on greedy techniques to guarantee the required data rate with a minimum cost. After that, we develop both source-feedback and non-source-feedback-based fountain coded cooperative communication protocols to facilitate the data transmission between two neighbor CHs. Numerical results are presented to demonstrate the performance of these protocols with different relay selection methods under Rayleigh fading channel. It shows that the proposed source-feedback-based protocol outperforms its non-source-feedbackprotocol counterpart in terms of a variety of metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".