Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks
Bibliographic record
Abstract
The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for crosslayer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".