On the Deployment of Energy Sources in Wireless-Powered Cellular Networks
Bibliographic record
Abstract
Wireless-powered cellular networks (WPCNs) are currently being investigated to ensure the reliability as well as improved battery lifetime of wireless devices. A WPCN leverages on a centralized base station (BS) that takes care of both wireless information and energy transfer. However, the harvested energy and, in turn, the spectral efficiency of uplink transmission of the users may significantly vary depending on the locations of the users and the channels used for energy and information transfer purposes. To this end, this paper theoretically characterizes the signal-to-noise ratio (SNR) outage zones in a WPCN and comparatively analyzes the performance of three useful configurations of dedicated energy sources that can potentially minimize the SNR outage zones. These configurations are: (i) harvesting energy from and information transfer to a full-duplex BS. This is considered as a baseline configuration; (ii) harvesting energy from symmetrically deployed power beacons (PBs) and information transfer to a conventional half-duplex BS; and (iii) harvesting energy from symmetrically deployed PBs that are colocated with the distributed antenna elements (DAEs) of a conventional half-duplex BS. For all the listed cases, we characterize the SNR outage probability and spectral efficiency of an arbitrarily located user within the cellular region. Based on the derived expressions, we also optimize the distance of the PBs from the BS to minimize the SNR outage probability and provide closed-form solutions for special cases. The optimum distance of the PBs is shown to be a function of the number of PBs and the coverage area of the BS. Numerical results validate the accuracy of the derived expressions, provide design insights related to WPCNs, and reveal the significance of the limited number of optimally placed PBs over a large number of randomly deployed PBs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".