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Record W2019496522 · doi:10.1109/vetecs.2012.6240324

Sum Rate of p-Sphere Encoding for MIMO Broadcast Channels with Reduced Peak Power

2012· article· en· W2019496522 on OpenAlexaff
Mahmood Mazrouei‐Sebdani, Witold A. Krzymień

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMOPrecodingMathematicsNorm (philosophy)EncoderTransmitterPerturbation (astronomy)MinificationEncoding (memory)Topology (electrical circuits)AlgorithmComputer scienceControl theory (sociology)Mathematical optimizationChannel (broadcasting)CombinatoricsTelecommunicationsPhysicsStatistics

Abstract

fetched live from OpenAlex

Vector perturbation is a sum-capacity approaching non-linear precoding technique. Vector perturbation uses sphere encoding to perturb data such that the unscaled sum power is minimized. Unfortunately, this minimization does not guarantee that the peak to average power ratio (PAPR) remains in the acceptable range for the transmitter. A p-sphere encoder was proposed in literature to reduce the PAPR by using p-norm minimization instead of the 2-norm one used typically in vector perturbation and its performance was analyzed in terms of bit error rate (BER). In this paper we focus on the spectral efficiency of the p-sphere encoding and investigate its sum rate for multiple-input multiple-output broadcast channel (MIMO-BC) with multiple-antenna users and uniformly distributed input. The results show that for p=5, the PAPR is reduced by 25%, while the sum rate is just slightly less than that of vector perturbation employing 2-norm sphere encoding (only 1% reduced sum rate).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.445

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 designBench or experimental
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

Citations2
Published2012
Admission routes1
Has abstractyes

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