Leveraging RIS in Consumer-Centric 6G Networks: Efficient Resource Allocation in RSMA-Based SWIPT Systems Under Hardware Impairments
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Bibliographic record
Abstract
This manuscript proposes an efficient resource management strategy for a rate-splitting multiple access (RSMA) based simultaneous wireless information and power transfer (SWIPT) system by leveraging reconfigurable intelligent surface (RIS) in consumer-centric sixth-generation (6G) networks for industry 5.0, under residual hardware impairments (RHIs) both at the transmitter and receiver nodes. Specifically, we aim to maximize the sum-rate of a RIS-assisted RSMA-based SWIPT system by incorporating a practical non-linear energy-harvesting model, while adhering to the quality-of-service (QoS), power-budget, power-splitting ratios, energy-conservation, and energy-harvesting constraints of the system. Moreover, the presented optimization technique addresses the highly non-convex problem in four distinct steps. Firstly, the power-allocation for both common and private messages of RSMA users is determined by converting a significantly non-convex power-allocation problem into a convex one by exploiting the successive-convex approximation (SCA) technique. Secondly, power-splitting ratios for RSMA users are computed by using the interior-point method facilitated by the Mosek-enabled toolbox in CVX. Thirdly, it computes transmit passive beamforming of a transmitter equipped with a transmissive-RIS (T-RIS), by exploiting SCA and semidefinite relaxation (SDR) techniques. Finally, passive beamforming vectors for the transmission and reflection regions of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) node are determined by converting a non-convex problem into a standard SDP problem using SCA, SDR, and Gaussian randomization techniques. Additionally, numerical simulation results affirm the effectiveness of the proposed optimization strategy, indicating superior performance against benchmark techniques and fast convergence within a reasonable number of iterations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it