Total Computational Bits Maximization for STAR-RIS Aided Wireless Power Transfer Mobile Edge Computing Networks: TDMA or NOMA?
Bibliographic record
Abstract
Due to the limited battery capacity and computational power of Internet-of-Things (IoT) devices, emerging IoT applications depend on mobile edge computing (MEC) networks for computational offloading and wireless power transfer (WPT) to sustain energy levels, with transfer performance influenced by the propagation environment and transmission strategies. Compared to the traditional reflecting-only reconfigurable intelligent surface (RIS), simultaneously transmitting and reflecting (STAR)-RIS extends coverage from half-space to full-space, significantly enhancing transferring efficiency. This paper compares the performance of three different uplink transmission schemes on a STAR-RIS WPT MEC network. On this basis, for each scenario, a non-convex optimization problem is formulated to maximize the total computational bits for the system, where energy transfer, local processing, uplink transmission allocated resources, and phase shift vectors of STAR-RIS are jointly optimized. To address the non-convexity, each scenario is decoupled into two main subproblems: phase shift vectors of STAR-RIS optimization and joint power, time, and computing frequency optimization. While a closed-form expression is derived to optimize the phase shift vector of STAR-RIS in time division multiple access (TDMA), semi-definite relaxation is employed to optimize the phase shift vectors under Hybrid TDMA-non-orthogonal multiple access (NOMA) and NOMA. Furthermore, block coordinate descent is employed to address the remaining resources. Simulation results indicate the superiority of the proposed scheme for all scenarios in terms of total computational bits compared to benchmark schemes. Moreover, among the three uplink transmission schemes, TDMA performs the best in total computational bits and user fairness owing to its ability to flexibly adapt STAR-RIS phase shift vectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".