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Record W2168733025 · doi:10.1145/1280940.1280947

Optimal relay station placement in IEEE 802.16j networks

2007· article· en· W2168733025 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRelayComputer scienceIEEE 802WiMAXComputer networkThroughputIEEE 802.11sResource allocationWireless networkWirelessWireless mesh networkTelecommunicationsQuality of service

Abstract

fetched live from OpenAlex

To make the WiMAX Point-to-Multi-Point (PMP) systems more competitive and applicable to the future metropolitan area networking scenarios, deploying relay stations (RSs) as defined in IEEE 802.16j has been considered a promising solution that can replace the 802.16e mesh mode for coverage extension and throughput enhancement. In this paper, we are committed to tackle the task of RS placement and relay time allocation in IEEE 802.16j Mobile Multi-hop Relay (MMR) networks, in order to meet the uneven distributed traffic demand of each subscriber station (SS) as well as the thirst for system capacity. By incorporating advanced cooperative relaying technology such as Decode-Forward (D-F) or Compress-Forward (C-F), the task of RS placement and relay time allocation is formulated into an optimization problem, aiming at finding the optimal location of a single RS and the resource allocation for all the SSs. Numerical analysis is conducted through a number of case studies to demonstrate the performance gain by using the proposed approach for relay placement and relay time allocation.

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)0.001
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 score0.891
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.299
Teacher spread0.265 · 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