Beamforming Techniques for NOMA-Based Integrated Sensing and Communication Systems
Bibliographic record
Abstract
In this paper, beamforming techniques are proposed for an integrated sensing and communication (ISAC) system based on non-orthogonal multiple access (NOMA). Specifically, a multi-antenna dual-functional base station simultaneously performs target sensing and serves multiple single-antenna NOMA communication users. To investigate the potential capabilities of this NOMA-based ISAC system, we first develop a beamforming technique for the max-min signal-to-interference-and-noise ratio (SINR) balancing problem. However, the original form is not convex regarding the design parameters. We propose an iterative algorithm that uses a bisection search to address the non-convexity problem and achieve a feasible solution to the original SINR balancing problem. This approach involves solving an equivalent power minimization problem, where we exploit the semidefinite relaxation technique. We also consider a robust design for the power minimization problem by taking into account inevitable imperfect channel state information. The numerical results show that the proposed NOMA-based ISAC performs better than the conventional orthogonal multiple access-based ISAC system in terms of transmit power consumption and balanced SINR while meeting the quality of service requirements regardless of the uncertainty of the associated channel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".