Mode selection map‐based vertical handover in D2D enabled 5G networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the promising features of 5G networks is device‐to‐device (D2D) communication that enables direct transmission between D2D user equipments (UEs). Besides the traditional cellular transmission mode, UEs can select between the reuse and dedicated modes. In this study the authors consider a scenario where a communicating D2D pair and a cellular UE that communicates with an evolved Node‐B can use the same spectrum. It is assumed that the cellular UE can move in the network while the D2D UEs are static. The movement of the cellular UE can affect the quality of the communication between the D2D pair. Therefore, the transmission mode between the D2D UEs might change to keep the best quality. In this study the authors propose a new mobility management and vertical handover algorithm that handles the transmission mode transition during the D2D connection to maximise the overall throughput. The algorithm uses distance from the border and critical direction set as mobility variables that are analytically determined. These variables are calculated using a mode selection map that is derived analytically when pathloss and fading models are used. Finally, in order to analyse the performance of the proposed handover algorithm, the authors analytically calculate handover rate and sojourn time metrics.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it