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DPC Rates and Multiplexing Gains for MIMO Broadcast Systems with Multi-Dimensional Space-Time Modulation

2011· article· en· W2104265071 on OpenAlexaff
Marthe Kassouf, H. Leib

Bibliographic record

VenueIEEE Transactions on Communications · 2011
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultiplexingMIMOTransmitterComputer scienceTransmission (telecommunications)Modulation (music)Spatial multiplexingSpace–time codeWirelessCoding (social sciences)Disjoint setsElectronic engineeringTopology (electrical circuits)TelecommunicationsMathematicsChannel (broadcasting)EngineeringPhysics

Abstract

fetched live from OpenAlex

This paper considers multiple-antenna broadcast channels (BCs) with multi-dimensional space-time modulation schemes, created by various allocations of signal dimensions to transmit antennas. The signal dimensions are divided into disjoint subsets, where one is shared between all users, and each of the others are assigned to different users. This model encompasses several practical single point to multi-point wireless communication systems employing multiple antennas. Assuming space and time dispersive channels, we consider the transmission rates that are achieved by applying dirty paper coding (DPC) at the transmitter, present inner and outer bounds for the DPC rate region, and study the effects of space-time modulation formats. At high SNR, we consider the limit form of these bounds, derive an achievable multiplexing gain region, and study the effects of sharing signal dimensions between users.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.137
GPT teacher head0.310
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2011
Admission routes1
Has abstractyes

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