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Record W2952685718 · doi:10.1109/glocom.2014.7416943

Efficient Heuristic for Resource Allocation in Zero-Forcing OFDMA-SDMA Systems with Minimum Rate Constraints

2014· article· en· W2952685718 on OpenAlexaff
Diego Perea-Vega, Jean François Frigon, A. Girard

Bibliographic record

Venue2015 IEEE Global Communications Conference (GLOBECOM) · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsComputer scienceSubcarrierResource allocationHeuristicsSpace-division multiple accessSpectral efficiencyMathematical optimizationOrthogonal frequency-division multiple accessHeuristicMaximizationDistributed computingOrthogonal frequency-division multiplexingReal-time computingComputer networkTelecommunications linkChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

Multi-antenna OFDMA-SDMA systems provide the required high spectral efficiency and flexibility to support the ever increasing data rates requirements of real-time multimedia applications in future wireless access systems. However, the resource allocation process becomes extremely complex because of the large number of degrees of freedom and the strict timing requirement of real-time traffic. In this paper, we propose heuristics to efficiently solve the zero-forcing OFDMA-SDMA resource allocation problem and provide, when feasible, guaranteed service to users with minimum rate requirements. The heuristics combine both rate-constrained power allocation and subcarrier reassignment algorithms. We compare the heuristics performance against an upper bound and other methods proposed in the literature and find that, although they have a slightly lower sum rate performance, they support a wider range of minimum rates while significantly reducing the computational complexity, making them suitable for usage in real-time systems.

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)0.001
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 score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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