MétaCan
Menu
Back to cohort
Record W2167990496 · doi:10.1109/twc.2007.060449

An Interference Aware Distributed Resource Management Scheme for CDMA-Based Wireless Mesh Backbone

2007· article· en· W2167990496 on OpenAlexaff
Hai Jiang, Ping Wang, Weihua Zhuang, Xuemin Shen

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsComputer scienceComputer networkInterference (communication)Wireless mesh networkOverhead (engineering)Radio resource managementWirelessWireless networkCode division multiple accessPower controlResource allocationDistributed computingTelecommunicationsPower (physics)Channel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, with a cross-layer design principle, we propose an interference aware distributed resource management scheme for a code-division multiple access (CDMA)-based wireless mesh backbone (consisting of a number of wireless routers at fixed sites). Specifically, benefiting from the fixed location of wireless routers, the power allocation is based on the length of the transmission path, so as to ensure a certain level of fairness among the routers. For a new call arrival, based on the maximum sustainable interference concept, each existing receiver (rather than the potential sender) estimates its experienced interference level under the hypothesis that the new call is admitted. If the interference is not tolerable, the existing receiver rejects the new call by sending a blocking-signal. The main advantages of our proposed scheme are the low control message overhead for easy implementation, and the accurate interference estimation. Simulation results are presented to evaluate the performance of our scheme.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.287
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

Citations27
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Wireless CommunicationsSame topicMobile Ad Hoc NetworksFrench-language works237,207