A Unified Performance Analysis of Cooperative NOMA With Practical Constraints: Hardware Impairment, Imperfect SIC and CSI
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) has been a strong candidate to support massive connectivity in future wireless networks. In this regard, its implementation into cooperative relaying, named cooperative-NOMA (CNOMA), has received tremendous attention from researchers. However, most of the existing CNOMA studies have failed to address practical constraints since they assume ideal conditions. Particularly, the error performance of CNOMA schemes with imperfections has not been investigated yet. In this paper, we provide an analytical framework for error and outage performance of CNOMA schemes under practical assumptions where we take into account imperfect successive interference canceler (ipSIC), imperfect channel state information (ipCSI), and hardware impairments (HWI) at the transceivers.We derive analytical expressions of bit error rate (BER) expressions in CNOMA schemes whether the direct links between source and users exist or not which is, to the best of the authors’ knowledge, the first study in the open literature. We also derive the outage probability (OP) expressions for CNOMA schemes with and without direct links under practical assumptions. For comparisons, we provide BER and OP expressions for downlink NOMA with practical constraints which also have not been given in the literature, yet. The theoretical BER and OP expressions are validated with computer simulations where the perfect match is observed. Finally, we discuss the effects of the system parameters (e.g., power allocation, HWI level, ipCSI factor) on the performance of CNOMA schemes to reveal fruitful insights for society. The results demonstrate that the HWI, ipCSI and ipSIC have a significant effect on the performance of the 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".