Algorithmes de gestion des ressources radio pour la \ncommunication périphérique à périphérique (D2D) dans \nles réseaux cellulaires sans fil.
Bibliographic record
Abstract
Device-to-device (D2D) communication has been recently proposed as an important technology \nfor the next-generation wireless cellular system, which promises to significantly improve \nthe system spectrum-efficiency, and energy-efficiency by exploiting the advantages of \nproximity communication. However, many challenges must be resolved to enable efficient \nintegration of D2D communication into the cellular networks. The overall objective of this \ndoctoral research is to develop novel and efficient resource allocation algorithms for D2D \ncommunications. \nToward this end, we investigate three key design issues to support the harmonious coexistence \nof D2D and existing cellular communications, namely spectrum and energy-efficient \nresource allocation for single hop D2D communication, mode selection and resource allocation \nfor relay-based D2D communication, and joint scheduling and resource allocation for \nD2D communication. These designs have resulted in several novel contributions, which can \nbe summarized as follows. \nFirst, we propose the spectrum-efficient resource allocation design for single hop D2D \ncommunication in the cellular networks, which is presented in Chapter 5. In particular, we \npresent a resource allocation model which allows dynamic power allocation and subchannel \nassignment for both cellular and D2D links. It is then demonstrated that the proposed algorithm \ncan improve the system spectrum-efficiency significantly in comparison with existing \nD2D resource allocation algorithms. \nSecond, we develop a general energy-efficient resource allocation framework for single hop \nD2D communication in cellular networks which targets to maximize the minimum weighted \nenergy-efficiency (EE) of D2D links while maintaining the minimum required data rates of \nthe cellular links. The research outcomes of this study are presented in Chapter 6. Particularly, \nwe propose a low-complexity power control and subchannel allocation algorithm, which \ncan approach the optimal solution of the underlying resource allocation problem. We also \npresent the distributed implementation for the proposed algorithm, which helps reduce the \ncomputation burden for the BS. \nThird, we study the resource allocation problem for relay-based D2D communications, \nwhich is covered in Chapter 7. The proposed design allows D2D links to dynamically choose \neither the direct or relay mode. We then propose an efficient mode selection and resource allocation \nalgorithm which optimizes the system spectrum-efficiency. We show that the proposed \nalgorithm can dramatically outperform the conventional resource allocation schemes. Finally, we consider the joint scheduling and resource allocation design for D2D communication \nin the cellular networks, which is described in Chapter 8. The proposed design \nframework allows to dynamically select the set of scheduled D2D links and optimize the system \nspectrum-efficiency. Toward this end, we develop a monotonic-based algorithm which \nasymptotically achieves the optimal solution. We then propose a low-complexity algorithm, \nwhich can perform much better than the conventional ones and approach the optimal solution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".