MétaCan
Menu
Back to cohort
Record W2520906763 · doi:10.1109/wcnc.2016.7564760

Cell search evaluation: A step towards the next generation LTE-MTC systems

2016· article· en· W2520906763 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceSynchronization (alternating current)Mode (computer interface)Communications systemAntenna (radio)Signal-to-noise ratio (imaging)Real-time computingComputer engineeringElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The main features for future Machine Type Communication (MTC) cellular networks enable low-power, low-cost, narrow band, and extended coverage system. To support these features, new challenges for the system design have emerged. For instance, the device shall be able to setup a call at a signal to noise ratio (SNR) of -15dB in the extended coverage mode with only one receive antenna and almost no frequency diversity. For these reasons, we present an evaluation to the conventional cell search and initial synchronization algorithms subject to these new hard requirements. The performance of most of the algorithms can be enhanced by utilizing time averaging on the account of increasing the processing time. By simulating exact LTE-MTC system, the performance of various algorithms is obtained with the expected time budget to meet LTE-MTC specifications, if applicable.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.155
GPT teacher head0.302
Teacher spread0.146 · 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

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicIoT Networks and ProtocolsFrench-language works237,207