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Record W2167558576 · doi:10.1109/wcnc.2007.198

A Capacity Achieving Precoding Scheme Based on Partial Channel Information for Broadcast MIMO Systems

2007· article· en· W2167558576 on OpenAlexaff
Hamid Reza Bahrami, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrecodingMIMOZero-forcing precodingComputer scienceChannel capacityErgodic theoryChannel (broadcasting)Limit (mathematics)Transmission (telecommunications)Interference (communication)Reduction (mathematics)Mutual informationDirty paper codingForcing (mathematics)Noisy-channel coding theoremMulti-user MIMOElectronic engineeringControl theory (sociology)Computer networkAlgorithmMathematicsTelecommunicationsDecoding methodsEngineeringBlock code

Abstract

fetched live from OpenAlex

This paper presents a zero-forcing algorithm based on the partial channel knowledge to select the best users for transmission without causing interference to others in a broadcast MIMO system. The proposed scheme preserves the optimal achievable ergodic sum capacity of zero-forcing techniques in limit of large number of active users with significant reduction in complexity and feedback load for channel information updates. Both analytical and simulation results on its advantages in feedback load, complexity and achievable sum rate are presented in different scenarios.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.681

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 communication0.0000.001
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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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
GenreMethods

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

Citations8
Published2007
Admission routes1
Has abstractyes

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