NOMA-Empowered Integrated Sensing and Communication With Movable Antennas
Bibliographic record
Abstract
Sixth-generation (6G) wireless networks have been driving growing demands for the full utilization of spectral efficiency and spatial degrees of freedom (DoFs). This paper investigates a non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) system assisted by movable antennas (MAs). We consider a dual functional radar and communication (DFRC) base station (BS) equipped with a two-dimensional (2D) MA array, which simultaneously senses multiple targets and serves users divided into multiple clusters. Successive interference cancellation (SIC) is employed within each cluster to suppress intra-cluster interference. To enhance the total illumination power at the sensing targets while guaranteeing the communication signal-to-interference-plus-noise-ratio (SINR) requirements at the users, we formulate an optimization problem for joint power allocation, beamforming, and antenna position design. To address this highly coupled and non-convex problem, an alternating optimization-based algorithm is proposed. We first determine the SIC decoding order by the equivalent-channel-to-interference-plus-noise-ratios (ECINRs), and derive the close-form solutions of the optimal intra-and-inter cluster power allocation coefficients. The sub-problems of beamforming and antenna position design are solved by semidefinite relaxation (SDR) and successive convex approximation (SCA) based schemes, respectively. Numerical simulation results are provided to verify the effectiveness of the proposed algorithm. The proposed algorithm significantly outperforms baseline schemes, which achieves approximately 2 dB illumination power gain compared to the conventional fixed position antennas (FPA), demonstrating the promising potential of MAs in wireless networks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".