MétaCan
Menu
Back to cohort
Record W1964526866 · doi:10.1109/glocom.2014.7037557

Joint power and channel allocation for multimedia content delivery using millimeter wave in smart home networks

2014· article· en· W1964526866 on OpenAlexaff
Bojiang Ma, Binglai Niu, Zehua Wang, Vincent W. S. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkInteger programmingConvex optimizationWirelessWireless networkResource allocationChannel (broadcasting)Channel allocation schemesDistributed computingTelecommunicationsRegular polygonAlgorithm

Abstract

fetched live from OpenAlex

Millimeter wave (mm-wave) communication has been considered as a promising technology for providing short range, high speed data service in wireless networks. In this paper, we apply the mm-wave technology for multimedia content distribution among different wireless devices in smart home networks. We study the resource allocation problem and propose a new multi-channel medium access control (MAC) protocol considering mm-wave channelization and various types of multimedia services. We define a set of utility functions for battery-constrained devices considering different types of services in smart home networks. We formulate a joint power and channel allocation problem to maximize the aggregate network utility, which is a non-convex mixed integer programming problem. We transform the problem into a series of convex mixed integer programming problems and develop an efficient algorithm to find the solution. Simulation results show that the proposed MAC protocol has superior performance compared to the existing single-carrier MAC protocol in IEEE 802.15.3c standard.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.512

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 studies0.0000.000
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 head0.059
GPT teacher head0.214
Teacher spread0.156 · 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 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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicMillimeter-Wave Propagation and ModelingFrench-language works237,207