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Record W2015563778 · doi:10.1109/glocomw.2013.6825171

Secure transmission in multi-cell massive MIMO systems

2013· article· en· W2015563778 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMIMOPrecodingTelecommunications linkComputer scienceBase stationTransmission (telecommunications)Artificial noiseUpper and lower boundsAntenna (radio)Computer networkSecure transmission3G MIMOTransmitter power outputTopology (electrical circuits)Ergodic theoryTransmitterElectronic engineeringTelecommunicationsMathematicsBeamformingEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we consider a multi-cell massive MIMO system with matched-filter precoding and artificial noise (AN) generation at the base station (BS) for secure downlink transmission in the presence of multi-antenna passive eavesdroppers. We derive two tight lower bounds for the achievable ergodic secrecy rate and a tight upper bound on the secrecy outage probability of the considered system. The analytical results are used to optimize the amount of power allocated for AN generation. Our results reveal that AN generation is not required in massive MIMO systems as long as the number of BS antennas is much larger than the number of eavesdropper antennas. However, as the number of eavesdropper antennas increases relative to the number of BS antennas, AN becomes beneficial and the amount of power optimally allocated to AN generation increases with the number of eavesdropper antennas.

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.

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 score0.848
Threshold uncertainty score0.405

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 head0.015
GPT teacher head0.226
Teacher spread0.212 · 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