Multipath routing algorithm for device-to-device communications for public safety over LTE Heterogeneous Networks
Bibliographic record
Abstract
One advantage of integrating small cells in cellular networks is to achieve Device to-Device (D2D) communications. The aim of performing D2D over small cells is to offload the Macro cells and also to assure the Public Safety (PS) exchange information especially during Disaster Management (DM), while the radio resources are unavailable totally or partially, or when the macro cell coverage doesn't reach the emergency area. This paper presents a new solution for multipath routing for D2D communications for Public Safety over Heterogeneous Networks (HetNets). The proposed algorithm named Load Balancing Based Selective Ad hoc On-Demand Multipath Distance Victor (LBS-AOMDV) is an enhancement of the AOMDV scheme. One particularity of LBS-AOMDV is offering the information about the available bandwidth of each route within the multipath. Furthermore, it reduces the control traffic by decreasing the number of nodes receiving the RREQ requests. This is feasible since the RREQ senders select the node, which can receive the packets. In this way, LBS-AOMDV is a selective AOMDV. The simulation results show that LBS-AOMDV significantly reduces the number of RREQ in the network comparing with AOMDV. In addition, unlike AOMDV, only feasible paths are selected to form the multipath routes. In other words, the selected paths by LBS-AOMDV are able to meet the requirement of the Quality of Service (QoS) in the network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".