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Record W2910213495 · doi:10.1109/intlec.2018.8612322

A Case Study of a Remote Line Powered Small Cell Network

2018· article· en· W2910213495 on OpenAlexaff
Satheesh Hariharan, Kevin Borders, Brian McCrea

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsBackupElectrical engineeringTelecommunicationsComputer scienceEngineeringOperating system

Abstract

fetched live from OpenAlex

In 2016, a neutral host carrier in Southern California, USA initiated a wireless network densification project that would ultimately involve several thousand small cells. While the service provider grappled with the obvious issues of backhaul, siting, and permits, one of the biggest issues was getting a usable source of power to each site. The provider found that the optimal site for RF coverage did not always coincide with the availability of power. The service provider had limited options. Compromising on the RF coverage would mean more sites would be needed. Electrical utility construction projects to reach each site would have jeopardized the project timeline, plus the potential cost overruns would have crippled the return on investment. The service provider was forced to seek an alternative solution. The resourceful neutral host considered various means of powering the devices, eventually deciding on Remote Line Power (RLP) using Twisted Pair Copper Cable to energize the small cells. The RLP method used a centralized power source to deliver power to the remote devices over copper cables. Sometimes, the sources were located in buildings, but more often they involved an outside plant electronics cabinet. In either case, utility AC provided at the central site connected to a conventional -48Vdc rectifier plant. The rectifier output in turn connected to a special DC-DC converter, called an Up-converter, that elevated the voltage to ±190Vdc for transport over the copper cables. The rectifier also charged batteries when backup power was required for critical circuits. At the small cell site another special DC-DC converter, called a Down-converter, changed the ±190Vdc back to 48V for powering the remote device. There were several requirements for this alternative approach to work, including: (1) the RLP equipment had to supply enough power to energize the small cells and backhaul equipment; (2) the down-converter had to be small, lightweight and able to mount on a strand near or alongside the small cell equipment; (3) the RLP system had to reach the worst-case distances while using the smallest standard OSP telecommunications cable; (4) the copper cable had to be installed along with the fiber to reduce the installation labor costs; and (5) the centralized power had to be placed at intermediate sites to minimize cable runs and lower the overall cost of equipment. This paper describes how the carrier met these requirements and successfully deployed a remote line powered small cell network that now consists of over 5000 end points. It describes the overall network architecture, including placement of the centralized sources and mounting for the down-converters. The paper also depicts the methods for accessing the copper cable networks. It concludes with a discussion of how the RLP technique met the timeline requirements.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.022
GPT teacher head0.224
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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