Wireless Powered Cooperative Communication Network for Dual-Hop Uplink NOMA With IQI and SIC Imperfections
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) is currently one of the promising techniques for the 6th generation (6G) wireless mobile networks, which can be combined with different technologies, such as cooperative communications and radio frequency (RF) wireless power transfer. RF can transmit energy over a wireless medium and has been seen as an essential application of systems. On the other hand, due to mismatched components and poor circuit fabrications, the transceiver suffers from RF front-end effects in actual situations, such as in-phase and quadrature-phase imbalance (IQI) which degrade the performance of the system. In this paper, we investigate the harvest-then-cooperate assisted NOMA for a wireless-powered cooperative communication network (HTC-NOMA-WPCCN) with practical constraints such as IQI and imperfect successive interference cancellation (ISIC). We thereafter extend the analysis to the multi-helper-user scheme to improve the performance of our considered system. The linear and non-linear EH are considered in our proposed system. We analyze the outage probability (OP), ergodic capacity (EC) and throughput. We discuss the effect of the IQI, ISIC, image rejection ratio and power allocation on the proposed HTC-NOMA-WPCCN. Our theoretical analysis is validated by Monte Carlo simulations. The simulation results demonstrate that the IQI and ISIC can significantly degrade the OP, EC and throughput performances of HTC-NOMA-WPCCN. The linear EH achieves better performance gain than the non-linear EH. Furthermore, this latter is influenced more by IQI and SIC imperfections, which closely approach a practical transmission. On the other hand, we compare the proposed system with UL NOMA without EH, and the results clearly showed the superiority of the proposed system. Finally, the system performance is influenced by changes in image rejection ratio, power allocation and WPCCN parameters which effects the IQI and SIC on the RF impairments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".