FTN-Assisted SWIPT-NOMA Design for IoT Wireless Networks: A Paradigm in Wireless Efficiency and Energy Utilization
Bibliographic record
Abstract
In the next-generation wireless Internet-of-Things (IoT) networks empowered by modern communication technology, nonorthogonal multiple access (NOMA) and faster-than-Nyquist (FTN) signaling are purportedly two enabling technologies that enhance spectral efficiency (SE) without requiring additional spectrum resources. In addition, simultaneous wireless information and power transfer (SWIPT) technology enables IoT sensors and devices to harvest energy from radio frequency (RF) signals, effectively mitigating power supply limitations. This article proposes and investigates a novel SWIPT-NOMA system based on FTN technology, referred to as FTN-assisted SWIPT-NOMA, for IoT relay networks over Rayleigh fading channels. We provide a comprehensive analysis of the ergodic capacity and achievable rate of the FTN-assisted SWIPT-NOMA system applied in IoT relay networks. Specifically, we explore two distinct relaying architectures geared toward augmenting SE and energy utilization, i.e., power-splitting (PS) and time-switching (TS). We derive approximated expressions for the ergodic capacity and analyze high- signal-to-noise radio (SNR) slopes for sensor users in both architectures. Simulation results show that the ergodic capacity of the proposed system surpasses that of the conventional Nyquist SWIPT-NOMA system, with greater capacity improvements as the FTN acceleration factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> decreases. This highlights the substantial potential of FTN-assisted SWIPT-NOMA systems in enhancing the performance of IoT relay networks, particularly with respect to SE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".