Embedded CR Assisted NOMA for IoT Resource Allocation: A Case Study of Vehicle Networks
Bibliographic record
Abstract
As an extension of the Internet of things (IoT), Internet of vehicles (IoV) paradigm plays a crucial role in advancing the development of smart cities. IoV relies on vehicular communication, enabling real-time interactions between vehicles, roadside infrastructure, and pedestrians. The main goal of IoV is not only to enhance road safety services but also to support time-sensitive IoT applications. In this context, cellular vehicle-to-everything (C-V2X) communication emerges as a prominent technology for achieving IoV goals. However, C-V2X communication system faces great challenges due to spectrum scarcity and the requirement for low-latency communication in high mobility and dynamic channel conditions. To meet these challenges, cognitive radio (CR)-inspired nonorthogonal multiple access (NOMA) has emerged as a promising solution to enhance the system capacity and spectrum efficiency. In this paper, we propose a novel CR paradigm, which utilizes the spectrum holes in an embedded mode. Namely, the secondary users (SUs) are allowed to access the holes released by idle primary users (PUs) without degrading the performance of active PUs. In addition, considering the channel aging phenomenon, we perform channel prediction to reduce the performance degradation. An online learning-based scheme that enables real-time resource allocation within the embedded CR assisted NOMA framework is then designed. Simulation results demonstrate superior performance gain of the proposed scheme. When compared with the conventional NOMA, the assistance of CR brings around threefold capacity gain, and when compared with the random allocation scheme, the capacity is increased by 21%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".