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Energy Efficient Quality of Service Traffic Scheduler for MIMO Downlink SVD Channels

2010· article· en· W2099671751 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

VenueIEEE Transactions on Wireless Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceQuality of serviceScheduling (production processes)Computer networkTelecommunications linkMIMOChannel (broadcasting)QueueNetwork packetEnergy consumptionThroughputReal-time computingMathematical optimizationWirelessTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

In this paper we focus on minimizing the long-term average power consumption of a single transmitter providing Quality of Service (QoS) enabled traffic to a single receiver. Both the transmitting and receiving stations are equipped with multiple antennas. First, we present a general {Kx M} system model where K is the number of independently buffered QoS streams and M is the number of parallel channels available through MIMO SVD eigenmode transmission. Through application of the constrained Markov decision process (MDP) framework combined with a novel MAC layer rate assignment scheme, a randomized per-buffer scheduling policy is obtained. The designed policy exploits queue state information to schedule traffic while meeting throughput, delay and loss constraints. Packets scheduled for transmission during each frame are mapped across the set eigenmode channels subject to available channel resources and the set of channel eigenvalues. Simulation results are provided for several scenarios. System drawbacks, limitations and extensions are also discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.277
Teacher spread0.250 · 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