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Record W2126816972 · doi:10.1109/milcom.2010.5680299

Multi-code wireless packet random access

2010· article· en· W2126816972 on OpenAlexaff
Christian Schlegel, Eric Bouton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceAlohaRandom accessComputer networkNetwork packetWireless sensor networkAsynchronous communicationCode division multiple accessBit error rateWireless networkSpread spectrumWirelessCapture effectReal-time computingChannel (broadcasting)ThroughputTelecommunications

Abstract

fetched live from OpenAlex

Spread spectrum multiple-access packetized wireless communications systems are considered. Although very efficient in low traffic (or low-rate) and low signal-to-noise ratio scenarios, the traditional and well-known spread ALOHA multiple access (SAMA) quickly collapses when both traffic and signal power increase. In certain situations, such as wireless sensor network traffic towards a sink, this severely limits the system data collection rates. Multi-packet reception (MPR), on the other hand, offers significant improvements in maximum throughput with respect to SAMA, whose performance is limited by the channel's collision mechanism. To enable MPR we propose to use multiple code CDMA, where different data packets are assigned individual (pseudo-random) spreading sequences. Several types of multiuser detector, such as the popular minimum mean-square error receiver are studied with respect to achievable system throughput and (system) power efficiencies. Due to the inherent asynchronicity of random access, we discuss asynchronous implementations of these receivers based on iterative cancelation processing. Basic queue behaviour utilizing a simplified MAC protocol, and analytical and numerical results are presented for a concept wireless sensor network with a central data collection sink, a setup that is also appropriate for last-mile relaying networks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
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 score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0060.002
Research integrity0.0000.001
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 head0.056
GPT teacher head0.355
Teacher spread0.299 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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