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Record W2147037449 · doi:10.1109/icc.2009.5199529

Throughput/Reliability Tradeoffs in Spread Spectrum Multi-Hop Ad-Hoc Wireless Networks with Multi-Packet Detection

2009· article· en· W2147037449 on OpenAlexaff
Dmitri Truhachev, S. V. Nagaraj, Christian Schlegel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkWireless ad hoc networkSpread spectrumNetwork packetThroughputCode division multiple accessWireless networkNode (physics)Transmission delayWirelessDistributed computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Wireless ad hoc networks with nodes capable of simultaneous multiple packet reception are considered. We focus on spread spectrum networks and address the relationship between the packet detection success, probability of the packet success over multiple hops, and asymptotic throughput capacity of the network in terms of power and bandwidth resources as well as the multi-packet detection capability of the nodes. In the second part of the paper we consider network with nodes employing partitioned code division multiple access (CDMA) transmission and joint iterative reception. We study local communication in the network and derive a relationship between the probability of detection success and a fraction of the multiple access channel capacity that can be achieved at any communicating node. We use this result to demonstrate that near optimum throughput and reliable end-to-end communication can be achieved in the network with use of a practical detection method. Finally, we present simulation results which demonstrate the advantage of partitioned CDMA with iterative receivers over CDMA with linear receivers in a network setting.

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)0.003
metaresearch head score (Gemma)0.014
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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