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Enabling Multi-Hop Concurrent Transmissions in 60 GHz Wireless Personal Area Networks

2011· article· en· W1997920321 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2011
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkThroughputWirelessRelayWireless networkHop (telecommunications)TelecommunicationsPower (physics)

Abstract

fetched live from OpenAlex

Millimeter-wave (mmWave) communications is a promising enabling technology for high rate (Giga-bit) multimedia applications. However, because of the high propagation loss at 60 GHz band, mmWave signal power degrades significantly over distance. Therefore, a traffic flow being transmitted over multiple short hops can attain higher throughput than that over a single long hop. In this paper, we first design a hop selection metric for the piconet controller (PNC) to select appropriate relay hops for a traffic flow, aiming to improve the flow throughput and balance the traffic loads across the network. We then propose a multi-hop concurrent transmission (MHCT) scheme to exploit the spatial capacity of mmWave WPANs by allowing nodes to transmit concurrently in communication links without causing harmful interference. The analysis of concurrent transmission probability and time division multiplexing demonstrates that the MHCT scheme is capable of improving the time slot utilization. Extensive simulations are conducted to validate the analytical results and demonstrate that the proposed MHCT scheme can improve the average traffic flow throughput and network throughput.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.089
GPT teacher head0.269
Teacher spread0.180 · 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