Wireless-Powered Interference Networks: Applications, Approaches, and Challenges
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.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".