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Multi-Antenna Covert Communication With Jamming in the Presence of a Mobile Warden

2021· article· en· W3168665672 on OpenAlexaff
Xinying Chen, Zheng Chang, Nan Zhao, Yunfei Chen, F. Richard Yu, Timo Hämäläinen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsJammingComputer scienceEavesdroppingTransmitter power outputTransmitterComputer networkCovertTransmission (telecommunications)ThroughputBase stationWirelessTelecommunications

Abstract

fetched live from OpenAlex

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the warden can move. In this paper, we propose a covert communication scheme against a mobile warden, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the covert outage probability (COP) limit. First, we analyze the monotonicity of the COP to obtain the optimal location the warden can move. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. Under this worst situation, we first maximize the connection probability over the transmit-to-jamming power ratio within the maximum allowed COP for a fixed transmission rate. Then, the Newton's method is applied to maximize the connection throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

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: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.190

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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