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Record W2124757755 · doi:10.1109/icc.2008.569

Efficient Power Allocation in Cooperative OFDM System with Channel Variation

2008· article· en· W2124757755 on OpenAlexaff
Mëmëdhe Ibrahimi, Ben Liang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceOverhead (engineering)Resource allocationRelayChannel allocation schemesChannel (broadcasting)MultiplexingResource management (computing)Scheme (mathematics)WirelessDistributed computingPower (physics)Computer networkTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Cooperative communication is emerging as an effective approach for realizing efficient wireless networks. Performance of these networks has been shown to be enhanced significantly by dynamic resource allocation, especially in orthogonal frequency division multiplexing (OFDM) systems, where there are more degrees of freedom. On the other hand, dynamic resource allocation imposes signalling and computational overhead on the system. In this paper, a multi-relay OFDM system is considered, where the cooperation gain of distributed antenna array is exploited. First we introduce the optimal power allocation problem and discuss the signaling overhead for implementing the optimal solution. We then propose suboptimal schemes with considerably less overhead and study the conditions under which they perform close to the optimal scheme. Furthermore, we investigate how imperfect implementation of these scheme results in performance degradation. We also analyze how much feedback is needed to implement this scheme with a desirable accuracy.

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: none
Teacher disagreement score0.939
Threshold uncertainty score0.321

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 science0.0000.000
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 head0.026
GPT teacher head0.236
Teacher spread0.210 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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