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Record W2106827777 · doi:10.1109/icc.2011.5962906

Dynamic Resource Allocation in OFDMA Systems with Full-Duplex and Hybrid Relaying

2011· article· en· W2106827777 on OpenAlexaff
Derrick Wing Kwan Ng, Robert Schober

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceSubcarrierOrthogonal frequency-division multiple accessScheduling (production processes)Orthogonal frequency-division multiplexingOptimization problemResource allocationDuplex (building)Mathematical optimizationFrequency-division multiple accessBenchmark (surveying)Computer networkAlgorithmMathematics

Abstract

fetched live from OpenAlex

In this paper, we formulate a joint optimization problem for resource allocation and scheduling in full-duplex orthogonal frequency division multiple access (OFDMA) relaying systems with amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Our problem formulation takes into account heterogeneous data rate requirements for delay sensitive users. Besides, a theoretically optimal hybrid relaying, which allows a dynamic selection between AF relaying and DF relaying protocols with full-duplex relays or half-duplex relays, is also considered in the problem formulation and serves as a performance benchmark. A dual decomposition method is employed to solve the resulting optimization problem and a novel distributed iterative resource allocation and scheduling algorithm with closed-form power and subcarrier allocation is derived. Simulation results illustrate that the proposed distributed algorithm requires only a small number of iterations to achieves practically the same performance as the optimal 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.251

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 head0.035
GPT teacher head0.237
Teacher spread0.202 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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