Cross-layer design for device-to-device communication
Bibliographic record
Abstract
Traditional communication systems were built using a layered structure to provide well-defined but limited interfaces among protocols in adjacent layers. The modular design of the layered structure, where the details of each protocol are hidden, promotes the interoperatability of the communication protocols. Although the protocol details (e.g., states and internal functions) are encapsulated in each layer, this structure prevents the protocols sharing, accessing, and controlling the operations of other protocols, which might be required for efficient data transmission. Therefore, the concept of cross-layer design has been introduced to allow tighter integration of different protocols. It is not necessary for these protocols to be in adjacent layers. The benefits of cross-layer design are that it allows one to improve both the flexibility of protocol implementations and network performance. Cross-layer design and optimization have been adopted in various wireless systems. A few surveys on cross-layer design exist in the literature, e.g., [190, 191, 192, 193]. The cross-layer design has been adopted for optimizing D2D communication. In this chapter, we give an overview of the cross-layer design by introducing its definition and different approaches. Then, we present one of the most commonly used cross-layer design models, i.e., the coordination model, which incorporates different functionalities. These functionalities are security, QoS, mobility, and wireless link adaptation. We next present the cross-layer implementation and challenges. We introduce cross-layer optimization, which is part of the cross-layer design. We provide examples of the cross-layer optimization problems, including opportunistic scheduling, OFDM resource allocation, and congestion control. We then review the cross-layer design framework proposed for D2D communications. They are information correlation routing, routing in sensor networks, and traffic scheduling for a video application. Finally, we outline some research directions of the cross-layer design and D2D communications. An overview of cross-layer design In this section, we first introduce the basic concepts including the definition of, and approaches to, cross-layer design. Then, we discuss the coordination model, which incorporates other functionalities (i.e., security, QoS, mobility, and wireless link adaptation) into the protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".