On the Performance of Multi-Antenna IRS-Assisted NOMA Networks With Continuous and Discrete IRS Phase Shifting
Bibliographic record
Abstract
In this paper we study an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network where the direct link between the base station (BS) and one of the users is blocked and the IRS is deployed to serve the blocked user. The IRS designs under both the ideal IRS with continuous phase shifting and the non-ideal IRS with discrete phase shifting are considered. For both cases, by leveraging the isotropic random vector and the Laguerre series, we derive insightful results and closed-form expressions on performance measures including the average required transmit power, the outage probability, and the diversity order. Our analytical results show that the transmit power scales down linearly with the BS antenna number and quadratically with the IRS element number. The diversity order equals the smaller of the BS antenna number and the IRS element number with a scaling coefficient. Our results also reveal the effect of the phase quantization resolution to the system performance when non-ideal IRS is used. Numerical results are provided to validate the accuracy of our analysis and the non-ideal IRS with four or more bits for quantization is shown to achieve nearly the same performance as the ideal IRS.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".