Downlink power allocation for wireless information and energy transfer in macrocell-small cell networks
Bibliographic record
Abstract
Wireless information and energy transfer in multitier cellular networks is a new research paradigm in wireless communications. While interference mitigation is one of the major challenges in conventional multi-tier networks, wireless energy harvesting capability considers interference signal as a source of energy. In this paper, we consider simultaneous wireless information and energy transfer in two-tier cellular networks and perform downlink power allocation with two different objectives. To maximize the sum of energy harvesting rate of small cell users, we formulate a linear programming problem whereas to maximize the sum of their information rate, we formulate a non-convex optimization problem. We solve the non-convex optimization problem by using convex-concave procedure and dual decomposition method. Numerical results indicate that the small cell users are exposed to high interference signal when maximum energy harvesting rate is desired and that received interference contributes a large portion of their total harvested energy. The trade-off between information rate and energy harvesting rate is found to be more prominent in terms of the interference signal rather than the power splitting factor since both information rate and energy harvesting rate are maximized when infinitesimally small power is split to the information decoder circuit.
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 | 0.000 | 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 | 0.000 |
| 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".