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Record W1585374956

Asynchronous simultaneous small packet transmission in cellular wireless system

2013· article· en· W1585374956 on OpenAlexaff
Chandra S. Bontu, Jagadish Ghimire, Shalini Periyalwar, Mark Pecen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsBlackberry (Canada)
Fundersnot available
KeywordsComputer networkComputer scienceNetwork packetTelecommunications linkBase stationPreamblePacket analyzerCellular networkWireless networkReal-time computingWirelessChannel (broadcasting)Telecommunications
DOInot available

Abstract

fetched live from OpenAlex

Existing cellular wireless systems operate with a predominantly network controlled, connection oriented frame based protocol architecture. A User Terminal (UT) is required to signal the network frequently to maintain its connectivity, and makes requests for uplink bandwidth to transmit packets. In such systems, for emerging applications, such as machine-to-machine (M2M), where small packets are transmitted sporadically, the bandwidth required for signaling is much more than the actual bandwidth to transmit the data. In this paper, we propose a novel connectionless uplink simultaneous access technique without the need of strict synchronization. UTs pick an uplink (UL) resource which is advertised by the network as a common radio resource for small packet transmissions, and transmit the data packets. The data packet includes a preamble sequence and network assigned identity. The transmitted packets from each UT may not be in perfect synchronization with the UL system timing. The base station (BS) uses a novel multi-user detection technique to separate these transmissions. Simulation results of the proposed method for an OFDM based system are shown be promising.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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