Communication and sensing performance study of NOMA-ISAC system with IRS-assisted SWIPT
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) and non-orthogonal multiple access (NOMA) are critical technologies for beyond 5th generation (B5G) and 6th generation mobile communications owing to their exceptional spectral efficiency and efficient hardware resource utilization. These technologies are widely utilized in emerging industries such as intelligent transportation systems for smart cars. Based on this, this paper explores a single-lane scenario using a NOMA-ISAC network, complemented by the assistance of an intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). The purpose of this investigation is to jointly evaluate the performance of both radar and communication functions. That is, the base station (BS), the detection vehicle (Alice), and the target vehicle (Bob) form a NOMA-ISAC network, the network can achieve both energy harvesting with the assistance of an IRS, and sensing of Bob by Alice. In particular, an energy harvesting strategy with time switching is used to implement energy supply from BS to Alice. Closed-form expressions are derived to evaluate the outage probability (OP) for Alice and Bob. The probability of detection (PD) and joint detection communication coverage probability (JDCCP) at Alice is also analyzed.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".