NOMA-Based IoT Networks: Impulsive Noise Effects and Mitigation
Bibliographic record
Abstract
The rise of the Internet of Things (IoT) presents important challenges for future radio networks. Non-orthogonal multiple access (NOMA), which allows the network to support more than one user per orthogonal resource element, was recently proposed as a promising solution that can ultimately support the daunting requirements of such networks including massive connectivity, high spectral efficiency, and low latency. Nevertheless, numerous ultra-high-reliability applications of IoT present environments that are hampered by impulsive electromagnetic interference, referred to as impulsive noise. Such noise is known to cause degradation to the overall system performance. Moreover, given the non-orthogonal multiplexing in NOMA, such noise is expected to have a relatively more pronounced impact on the system performance. Therefore, this article sheds light on the performance degradation and mitigation of impulsive noise in the context of NOMA-based IoT networks. It proposes a multistage nonlinear processing approach specifically designed for OFDM-based PDM-NOMA systems. To obtain the optimum threshold of the corresponding users, we propose a deep learning approach to estimate the impulsive noise parameters from the received OFDM symbol. This information can consequently be used to evaluate the corresponding optimal threshold using Siegert's ideal observer criterion. Finally, this work sheds light on potential opportunities and challenges that are expected to arise during the implementation of NOMA in impulsive environments.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".