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Record W2529373704 · doi:10.1109/tccn.2016.2615943

An Online Admission Control Algorithm for Dynamic Traffic in Underlay Coexistence Paradigm

2016· article· en· W2529373704 on OpenAlexaff
Xu Yuan, Xiaoqi Qin, Feng Tian, Brian Jalaian, Yi Shi, Y. Thomas Hou, Wenjing Lou, Wade Trappe

Bibliographic record

VenueIEEE Transactions on Cognitive Communications and Networking · 2016
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsUnderlayComputer scienceComputer networkHop (telecommunications)Node (physics)Interference (communication)Distributed computingAlgorithmTelecommunicationsSignal-to-noise ratio (imaging)EngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Underlay is an aggressive spectrum sharing paradigm that allows secondary nodes to be active simultaneously with the primary nodes through interference cancelation (IC). In this paper, we design an online admission control algorithm to handle dynamic session arrival and departure in the underlay coexistence paradigm for multi-hop primary and secondary networks. For IC, we employ multiple antennas at each secondary node. Through distributed computation and degree-of-freedom (DoF) allocation at each secondary node, our algorithm ensures that all interference to/from the multi-hop primary network and interference within the multi-hop secondary network are canceled properly so that data transport is free of interference in both multi-hop primary and secondary networks. Further, we show that the DoF allocation by our algorithm is feasible (implementable) at the physical layer at all time. Through extensive performance evaluation, we find that our online admission control algorithm can offer competitive performance when compared to an offline centralized algorithm.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.305
Teacher spread0.261 · 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 designOther design
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

Citations0
Published2016
Admission routes1
Has abstractyes

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