Challenges of wireless power transfer for prolonging User Equipment (UE) lifetime in wireless networks
Bibliographic record
Abstract
Communication technologies are striving to provide ubiquitous and cable-free communication services to users while user devices are still limited with their batteries and need wires to recharge their batteries. The recent advances in Wireless Power Transfer (WPT) are promising to charge wireless sensor networks and on-body medical devices without the need of wires or battery replacement. One natural way of scavenging energy from the environment and providing ubiquitous power is electromagnetic radiation based WPT. Recently powering up Wireless Sensor Networks (WSNs) or RFID tags via omnidirectional radiation and beamforming has been studied in several studies. Yet, the potential of exploiting wireless networks to power User Equipment (UE) such as mobile phones or PDAs has been less explored. Long distances between wireless towers and UEs as well as their relatively low transmit power are among the major bottlenecks for WPT in wireless networks. In this paper, we consider dedicated energy transmission units (DETUs) to provide power to UEs. We show that although certain amount of power can be harvested by UEs, the cost of deploying DETUs dominates the design decision. As a transitional solution, power from relays, small cell towers and WiFi hotspots can be exploited. However when WPT is in-band with information transfer there may be interruption in connectivity. We discuss the challenges of WPT in wireless networks and propose several future directions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".