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Record W7015017120

Security enhancements for sparse MIMO systems: A compressive sensing - artificial noise technique

2018· dissertation· en· W7015017120 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysical layerArtificial noiseMIMOCompressed sensingAdversaryWirelessRandomnessSecrecyNoise (video)
DOInot available

Abstract

fetched live from OpenAlex

In wireless communication, it is possible to achieve high level of security by exploiting the random characteristics of wireless channels and the transmitted signal structure.Thus, applying security at physical layer gained much attention since this randomness can be utilized to provide infinite set of secret keys; unlike cryptographic techniques that are applied at the network layer and are limited by the finite set of available keys.Not only this, but also the emerging types of networks pose restrictions on applying security at higher level due to the light computational abilities of the devices used such as sensors, radio-frequency identification (RFID) tags and so forth.Thus, physical layer security (PLS) provides a way to utilize the physics of radio propagation to secure such networks.Furthermore, sparsity, that exists in many types of signals, can be used to achieve sampling and compression efficiently.In this thesis, we consider physical layer security through combining both compressive sensing (CS) and artificial noise (AN) to elevate secrecy of MIMO communication systems.Contrary to the classical methods of CS-PLS, this technique does not impose any restriction on the adversary except one.It is assumed that both the adversary and the legitimate receiver have access to the same information with only one imposed postulation that the adversary should possess fewer antennas than the transmitter.This is a valid assumption in many applications that include a powerful base station.First, we lay down the theoretical foundation for physical layer security with emphasis on both compressive sensing and artificial noise.Then, we modify the CS system model to work in MIMO environment to be able to include AN, where the latter requires a MIMO layout to guarantee the presence of a valid null space to inject the artificial noise to the system.Two methods are considered to perform such modification; the first one is CS by repetition and the other one is CS on Air.Furthermore, different techniques are used to inject AN to the CS-based system and the security performance of each method is evaluated.The difference between these techniques lies in the method of creating the artificial noise and where it is injected.Moreover, the secrecy performance is enhanced by using beamforming to direct the information-bearing signal towards the intended receiver and degrades it in other directions.Finally, we investigate the effect of choosing the perturbation parameter (), due to the presence of AWGN and AN, in 1 -minimization algorithms on the recovery performance at the eavesdropper and consequently the achieved secrecy. is related to the noise level and decides the accuracy of recovering the transmitted signal.Simulation results show that using Abstract iii beamforming, when combining CS and AN, enhances secrecy considerably while imposing no assumptions on the confidentiality of information from the eavesdropper including CSI, seeds (used for generating random keys) or keys.Furthermore, they show that the choice of significantly affects secrecy since it cannot be correctly estimated at the adversary and hence increases the errors in 1 -minimization techniques that are used for signal reconstruction.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.023
GPT teacher head0.265
Teacher spread0.243 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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