Delay Optimal Concurrent Transmissions With Raptor Codes in Dual Connectivity Networks
Bibliographic record
Abstract
Dual connectivity (DC) has been emphasized in both LTE and 5G networks to utilize a secondary evolved NodeB (SeNB) connected to the master evolved NodeB (MeNB) that can simultaneously serve the user equipment (UE) to improve the per-user throughput as well as the mobility support. However, the non-ideal X2 link between the MeNB and SeNB and the dynamic channel condition can cause severe out-of-order packet arrival problem when transmitting data to the UE via MeNB and SeNB concurrently, which leads to excessive delay and requires non-trivial effort to do the de-jitter queueing and retransmission. In this paper, we propose a Raptor codes based dual connectivity (RCDC) scheme to solve the out-of-order packet arrival problem with the reduced delivery delay. The source packets at the MeNB are coded and separately transmitted to the UE through the MeNB and SeNB. Due to the unique recover capability of Raptor codes, the UE can decode the original data if enough encoded packets are received from either the MeNB or SeNB, and thus the out-of-order problem can be effectively eliminated without a dedicated de-jitter process. Mathematical models are developed to analyze the delay performance and simulation results are provided to demonstrate that the proposed scheme can solve the out-of-order packet arrival problem with significantly reduced delay comparing with the conventional DC scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".