Energy- and Delay-Aware Two-Hop NOMA-Enabled Massive Cellular IoT Communications
Bibliographic record
Abstract
Providing energy-efficient and delay-aware channel access in cellular networks is essential to many anticipated massive Internet of Things (IoT) applications. However, as the number of devices increases, the contention over the limited network radio resources increases, leading to network congestion. The congestion increases the channel access delay and energy consumption of IoT devices, and reduces the number of supported devices. Node clustering and data aggregation are potential approaches to support the massive number of devices while meeting the various service quality requirements of diverse applications. As the number of devices increases, optimizing the node clustering and data aggregation process becomes critical as many tradeoffs arise among different network performance metrics. In this article, we present a novel nonorthogonal multiple access-enabled two-stage transmission architecture to enable massive cellular IoT communications. Concepts from queuing theory and stochastic geometry are jointly exploited to derive tractable models for different network performance parameters, such as coverage probability, two-hop access delay, and the number of served devices per transmission frame. The established models characterize relations among various network parameters, and hence facilitate the design of two-stage transmission architecture. The numerical results demonstrate that the proposed solution improves the overall access delay and energy efficiency as compared to traditional-orthogonal-multiple-access-based clustered networks. They also highlight that, for the scenario considered, there is an optimal number of aggregators at which the tradeoffs among the different network performance measures are optimized.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".