Cooperative NOMA Empowered Integrated Sensing and Communication: Joint Beamforming and User Pairing
Bibliographic record
Abstract
In this paper, we consider a downlink communication and sensing system where cooperative non-orthogonal multiple access (C-NOMA) is adopted as a multiple access technique to jointly provide communication functionality to a set of users and sensing functionality to targets. Specifically, we leverage the potential gains of cooperative links between far and near NOMA users in terms of reducing the power allocated from the base station (BS) to far NOMA users to dedicate more resources to the sensing function. In doing so, we formulate this framework as an optimization problem to maximize the achievable sum rate of the communication users by jointly optimizing the users’ pairing scheme, transmit beamforming at the BS, and near users’ transmit power while respecting the required communication and sensing quality of service (QoS) constraints. Owing to the non-convexity of the formulated problem, we divide this problem into two sub-problems, namely the user paring sub-problem and the power allocation sub-problem. To solve the first sub-problem, we present a novel pairing approach that exploits channel orthogonality and correlation among different users. Then, we define a double-layer penalty-based algorithm to handle the non-convex structure of the second sub-problem. Finally, the numerical results clearly showed the effectiveness of our adopted C-NOMA system over traditional baseline schemes, where our proposed scheme achieves gains reaching up to 20% compared to traditional NOMA, and 40% compared to spatial division multiple access (SDMA). Moreover, our pairing strategy achieved performance reaching 95% that of the optimal pairing scheme.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".