Wireless-Powered Interference Networks: Applications, Approaches, and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interference is usually regarded as a detrimental factor that must be avoided or suppressed to achieve higher performance in traditional wireless communications. Wireless energy harvesting (EH) technologies have been found to be capable of converting such harmful interference into a feasible energy source for low-powered Internet of Things (IoT) devices that otherwise have limited lifetimes. In this context, we introduce a wireless-powered interference network (WPIN) in which interference is proactively controlled, considering the two opposing concepts of signal jammers and energy sources to improve the bidirectional transmission rate of IoT devices. First, an overview of WPIN applications is provided in various wireless topologies with complex cochannel interference. Then, a wireless interference harvesting protocol is presented to manage this cochannel interference for bidirectional communications in WPINs. We investigate coordinated resource management and beamforming schemes based on this interference harvesting protocol and demonstrate how these schemes improve the performance of WPINs. Simulation results show that the proper utilization of interference according to the channel structure decreases interference’s negative effects on information decoding and increases the amount of harvested energy, thereby simultaneously improving the downlink and uplink capacities. Finally, imminent research challenges and directions with regard to making WPINs more practical and useful are outlined.
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.
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 | 0.001 |
| 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 it