Ambient Wireless Energy Harvesting in Small Cell Networks: Performance Modeling and Analysis
Bibliographic record
Abstract
Introduction Small cell networks (SCNs) are envisioned as a key enabling feature of next-generation wireless networks that can meet the high capacity requirements in outdoor/indoor environments [1]. The successful implementation of SCNs faces several challenges. For instance, the increase in co-channel interference (CCI) due to densification of small cells can significantly degrade the achievable network capacity. Moreover, the subsequent increased energy consumption of the system is undesirable from both environmental and economical perspectives. Finally, providing grid power to all small cell base stations (SBSs) may not always be feasible due to their possible outdoor/remote/hard-to-reach locations. Thanks to the recent advancements in wireless energy harvesting (EH) techniques, it has become feasible to power small devices wirelessly. Wireless EH thus enables dense deployment of SBSs irrespective of the availability of power grid connections. It is important to note that dedicated EH leverages the deployment of dedicated energy sources. Therefore, additional resource/power consumption is unavoidable [2]. Consequently, ambient EH is crucial to reduce the grid power consumption of cellular networks. Unfortunately, owing to the dependence of energy harvested from renewable energy sources on temporal/geographical/environmental circumstances, consistent performance at the base stations (BSs) may not be guaranteed. Also, harvesting energy from renewable energy sources may require an extra hardware setup of solar panels and/or wind turbines. Thus, the significance of investigating other kinds of ambient sources in order to minimize the grid power consumption of cellular networks becomes evident. Motivated by the aforementioned facts, in this chapter, we focus on RF-based ambient EH small cell networks and highlight the corresponding challenges from implementation and operation perspectives. These challenges arise due to factors such as nondeterministic energy arrival patterns, EH mode selection, energy-aware cooperation among base stations, etc. Next, we provide a brief overview of the existing literature in the context of the challenges discussed. The review provided highlights the research gaps and points out future research directions. Finally, we investigate the performance of a K -tier uplink cellular network where cellular users harvest RF energy from the concurrent downlink transmissions from all network tiers. Then, each user stores the harvested energy in an attached battery until the amount of energy stored therein is sufficient to perform channel inversion power control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".