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Record W2012258707 · doi:10.1109/hmwc.2014.7000244

Millimetre wave bands for 5G wireless communications

2014· article· en· W2012258707 on OpenAlexaff
Vijay K. Bhargava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceWirelessBackhaul (telecommunications)GigabitTelecommunicationsAccess technologyBandwidth (computing)Wireless networkComputer network

Abstract

fetched live from OpenAlex

Summary form only given. Current interest in the millimeter wave technology is motivated by both old and new factors. Over the past decade, regulators have allocated up to 9 GHz of Spectrum near 60 GHz for license-exempt world-wide use. The emergence of numerous high-speed applications, including uncompressed HDTV, uncompressed multi-video streaming, very high-speed -file downloading and now potential use in wireless access and backhaul for future 5G heterogeneous networks, has provided the motivation for developing the technologies required to exploit this bandwidth. Moreover recent advances in realizing low-cost CMOS technology suitable for use at such high frequencies, improved algorithms for adaptively steering directive antenna beams; protocols for medium access control (MAC) and implementation of LDPC coding to improve link margins have made such exploitation a commercially viable prospect. In this talk we present technical challenges and opportunities for the application of millimeter-wave technologies in 5G era. It will be seen that it is a viable technology to provide multi-Gigabits per second access to mobile users and sustain the traffic growth.

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 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)0.000
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 score0.990
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2014
Admission routes1
Has abstractyes

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