An Improved Spectrum Sensing for Cognitive PLC ... - Ciente Live

Outline

An Improved Spectrum Sensing for CognitivePLC Systems

Alam Silva MenezesYan Coutinho

Moises Vidal Ribeiro

Electrical EngineeringFederal University of Juiz de Fora

September 26th – 29th, 2021

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Outline

Outline

1 Introduction

2 PLC Spectrum Sensing Modeling

3 Improved PLC Spectrum Sensing

4 Numerical Results

5 Conclusions and Perspectives

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IntroductionPLC Spectrum Sensing ModelingImproved PLC Spectrum Sensing

Numerical ResultsConclusions and Perspectives

Introduction

PLC is being pointed out as one of the technologies to meetthe data communication demands related to Smart Grids(SG), Internet of Things (IoT), and Industry 4:0;

The hostile characteristics of electric power system fortransmitting carrying-signals information and the existingregulatory constraints motivate flexible spectrum access;

Several researches have been investigating the CR techniquesin PLC systems;

The power line has been used as a sensor to detect spectrumholes and to avoid possible interference in the radiocommunication systems.

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MotivationSystem Modeling

System Modeling

The baseband frequency range occupied by PLC:

0 ≤ f ≤ 100MHz

The signal at the input of only one sensor

y(t) =∑K

k=1 sk(t) + v(t)

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Acquisition Formulation

The acquisition of the signal can be modeled by:

y = Tm{y(t)}

= Tm

{K∑k=1

sk(t)

}+ Tm{v(t)}

= s + v ,

Where Tm ∈ {Ta, Tc} denotes the used sensor and ...

y, s and v ∈ RM×1

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Matrix Formulation

Rewritten in a matrix form as follows

Y = S + V

in which ...

Y = [y1 y2 · · · yM−N ]

S = [s1 s2 · · · sM−N ]

V = [v1 v2 · · · vM−N ]

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Matrix Formulation

The column vectors of each matrix are defined as

yn ,[y[n] y[n+ 1] · · · y[n+N − 1]

]Tsn ,

[s[n] s[n+ 1] · · · s[n+N − 1]

]Tvn ,

[v[n] v[n+ 1] · · · v[n+N − 1]

]Tfor n = 0, 1, · · · , M −N + 1

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Proposed Technique

Block diagram

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Proposed Technique

Time-Frequency Mapping

Time-frequency matrix formulation

Y = AY

= AS + AV

= S + V

Where A is a time-frequency transformation matrix

In this work we have used: DFT, DHT, MCLT, MTM and HMTM.

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Proposed Technique

Selection and Extraction of Quanta

The stochastic spectrogram of the monitored signal is given by

ΨY = Y∗ �Y

=(S + V

)∗ � (S + V)

= ΨS + ΨC + ΨV ,

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Proposed Technique

Selection and Extraction of Quanta

The PUs detection in the qth quantum is formulated by

H0 → ΨqY = Ψq

V

H1 → ΨqY = Ψq

S + ΨqC + Ψq

V

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Proposed Technique

Features Extraction and Selection

Features Extraction

rq = Υz{ΨqY}

where rq ∈ RK×1 and K = NFNT

Features Considered

Cumulatns: c2,rq c3,rq c4,rq

Skewness: γ3,rq

Kurtosis: κ4,rq

Energy: Erq

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Proposed Technique


Vector of Features

cq =[c2,rq c3,rq c4,rq γ3,rq κ4,rq Erq

]TFisher Discriminant Ratio (FDR):

υc = (E{cq|H0} − E{cq|H1})�[

12σ2

1· · · 1

2σ2Lf

]

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Proposed Technique


Features Selection:

gq = Sζmax υc{cq}

where Sζmax υc{·} represent the operation for selecting the ζfeatures associated with the highest values of the FDR

Exploring the diversity:

In order to explore the diversity of the means of propagating signalsthrough the air and the electric power cable, we propose to use the

signals from the antenna sensors and PLC coupler as follows:

zq ,

[gaqgcq

]

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Proposed Technique

Detection Techniques

Detection based on the Bayes’ theorem

p(zq|H0)P (H0) > p(zq|H1)P (H1) , zq → H0

or

p(zq|H0)P (H0) ≤ p(zq|H1)P (H1) , zq → H1

Detection based on MLP with one hidden layer and sevenactivation functions

uq = αT[

zq1

]

y = βT[

tanh(uq)1

]15/18



Setup

Performance Evaluation

Measured signals

Use of signals obtained from a measurement campaign carried outin several homes in the city of Juiz de Fora considering the

frequency band between 1.7 and 100 MHz

Time-frequency Mapping:

NT ×NF ∈ {1× 4 , 1× 8 , 4× 4 , 4× 8 , 8× 4 , 8× 8}

N = 128

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Setup

Results

PD

0

BDFT

0.5

BDH

T

1

BMCLT

BMTM

8x8

c

BHM

TM

8x8

a

8x4

c

MD

FT

8x4

a

4x8

c

MD

HT

4x8

a

4x4

c

MM

CLT

4x4

a

MM

TM

1x8

c

1x8

a

MH

MTM

1x4

c

1x4

a

PF

0

BDFT

0.5

BDH

T

1

BMCLT

BMTM

8x8

c

BHM

TM

8x8

a

8x4

c

MD

FT

8x4

a

4x8

c

MD

HT

4x8

a

4x4

c

MM

CLT

4x4

a

MM

TM

1x8

c

1x8

a

MH

MTM

1x4

c

1x4

a

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Conclusions

Conclusions and Perspectives

An improved spectral monitoring sensing for cognitive PLCsystems was proposed and formulated;

A data set obtained from a measurement campaign containingnoise measurements sensed by an antenna and a PLC couplerwas adopted;

The performance of the MLP-based technique configurationsare superior to the Bayes-based ones. This suggests that thestatistical distribution of the monitoring quanta may not bemodeled by a Gaussian distribution;

The PLC coupler quanta presented considerably higher valuesof false alarm rate when compared to the antenna quanta.

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