Multi-Operator Spectrum Sharing for Massive IoT Coexisting in 5G/B5G Wireless Networks
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Bibliographic record
Abstract
With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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