Enhancing Sensing Capabilities in RSMA Downlink Networks through User-Assisted Beamforming
Bibliographic record
Abstract
This paper examines the downlink scenario where a transmitting base station (BS) provides communication services to a set of users by utilizing the rate-splitting multiple access (RSMA), while concurrently providing sensing functionalities. Owing to the available transmit power of the cellular users and their capabilities of decoding the RSMA common stream, we propose to leverage the users in the network to assist the sensing process by collectively forming a probing beam towards the target(s). Using this proposed system and to evaluate its potential gains, we formulate an optimization problem to jointly determine the beamforming design at the transmitting BS, the common stream split, and the distributed beamforming design at the users as well as at the receiving BS aiming to maximize the minimum rate of the users. Due to the non-convexity posed by the formulated problem, we perform rigorous mathematical operations and leverage the semi-definite relaxation (SDR) method to solve it using a successive convex approximation (SCA) algorithm. Our numerical results demonstrate the advantage of exploiting users' resources to assist in the sensing process which is reflected in an enhancement in the achieved rate by the users. Moreover, we present the advantage of our model in comparison to Spatial Division Multiple Access (SDMA) 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".