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Record W2012071412 · doi:10.1109/glocom.2013.6831630

An efficient cross layer design for OFDMA-based wireless networks with channel reuse

2013· article· en· W2012071412 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer sciencePhysical layerWireless networkComputer networkScheduling (production processes)Mathematical optimizationNetwork topologyWireless ad hoc networkRelayConvex optimizationWirelessNode (physics)Optimization problemChannel allocation schemesChannel (broadcasting)Distributed computingPower (physics)Regular polygonAlgorithmTelecommunicationsMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper considers a joint design that incorporates the physical, medium access and network layers of a generic OFDMA-based wireless network with an ad hoc topology. The network employs channel reuse, whereby a frequency subchannel might be used simultaneously by multiple nodes. In addition to being a source and/or a destination, each node can act as a half-duplex relay to assist other nodes. The design objective is to determine the jointly optimal data routes and subchannel power allocations that maximize a weighted sum of the rates that can be reliably communicated over the network. Assuming that the signals transmitted by the nodes are Gaussian, the joint cross layer design of routing and power allocation is cast as an optimization problem. Unfortunately, this problem is non-convex, and hence difficult to solve. To circumvent this difficulty, an efficient technique based on geometric programming is developed to obtain a local solution that satisfies the Karush-Kuhn-Tucker necessary optimality conditions. Numerical results show that, despite the potential suboptimality of the obtained solution, for some network scenarios, it offers significant gains over optimal scheduling-based schemes in which a frequency band is allowed to be used by one node only at any time instant.

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 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 score0.769
Threshold uncertainty score0.787

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 head0.014
GPT teacher head0.231
Teacher spread0.218 · 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

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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