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Wireless Collaboration

2010· book-chapter· en· W2493114647 on OpenAlexaff
Patrick Tooher, Mohammad Soleymani

Bibliographic record

VenueAdvances in wireless technologies and telecommunication book series · 2010
Typebook-chapter
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceCooperative diversityWirelessComputer networkWireless networkAntenna diversityNode (physics)Channel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

To achieve performance gains in the wireless channel, spatial diversity is employed. These higher order transmit diversity gains generally require multiple transmit antennas at the source. This requirement is not always possible in real world applications, where practical concerns limit the number of antennas a wireless device can have. Recently, a new method to achieve transmit diversity has been proposed: collaborative communications. In this framework, a node in a wireless network can use the resources of other idle nodes and form what can be viewed as a virtual transmitting antenna array. This chapter presents an overview of the development of collaborative communications. Two-phase protocols that can achieve collaboration are presented. A discussion on the improvement of collaborative communications protocols is given. A broader perspective of collaborative communications is given by discussing ideas such as power allocation and multiple relays.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0030.002
Research integrity0.0010.001
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 head0.013
GPT teacher head0.259
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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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