Secure layered transmission in multicast systems with wireless information and power transfer
Bibliographic record
Abstract
This paper considers downlink multicast transmit beamforming for secure layered transmission systems with wireless simultaneous information and power transfer. We study the power allocation algorithm design for minimizing the total transmit power in the presence of passive eavesdroppers and energy harvesting receivers. The algorithm design is formulated as a non-convex optimization problem. Our problem formulation promotes the dual use of energy signals in providing secure communication and facilitating efficient energy transfer. Besides, we take into account a minimum required power for energy harvesting at the idle receivers and heterogeneous quality of service (QoS) requirements for the multicast video receivers. In light of the intractability of the problem, we reformulate the considered problem by replacing a non-convex probabilistic constraint with a convex deterministic constraint which leads to a smaller feasible solution set. Then, a semidefinite programming relaxation (SDR) approach is adopted to obtain an upper bound solution for the reformulated problem. Subsequently, sufficient conditions for the global optimal solution of the reformulated problem are revealed. Furthermore, we propose two suboptimal power allocation schemes based on the upper bound solution. Simulation results demonstrate the excellent performance and significant transmit power savings achieved by the proposed schemes compared to isotropic energy signal generation.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".