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Record W2156681981 · doi:10.1109/tvt.2009.2021180

System Design and Throughput Analysis for Multihop Relaying in Cellular Systems

2009· article· en· W2156681981 on OpenAlexaff
Kevin R. Jacobson, Witold A. Krzymień

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2009
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThroughputComputer networkRelayComputer scienceOverhead (engineering)Cellular networkPhysical layerReusePath lossSpectral efficiencyWirelessDistributed computingEngineeringChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Wireless multihop relaying can provide a great improvement in systemwide throughput and coverage in a cellular system. However, since additional resources (time or frequency slots) and overhead are required to perform relaying, the addition of relays into a cellular system must be carefully designed for the system's specific geometry, topology, and propagation environment. Maximizing the benefit of multihop relaying requires that the medium access control (MAC) layer scheduler be aware of physical (PHY) layer conditions. Multihop relaying increases networkwide throughput by enabling spatial reuse within a cell and by significantly reducing the path loss on each hop. The most significant gain results from adding enough relays to create line-of-sight (LOS) paths on all relay hops. To assist with system design, we present a methodology of analyzing network throughput and area-averaged spectral efficiency for multihop relay-enhanced cellular systems, including cross-layer considerations. In addition, we describe a technique that may be used to determine spatial reuse schedules. In this paper, we use a realistic model and typical cellular scenarios to show how relaying can be used to improve coverage and throughput in a real-world system. </para>

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.262
Teacher spread0.229 · 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
GenreMethods

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

Citations19
Published2009
Admission routes1
Has abstractyes

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