cognitiveradio_14052008_dipti.ppt

8/10/2019 CognitiveRadio_14052008_dipti.ppt

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Cognitive Capabilities

Awareness:should be aware of its

own abilities, the regulatingpolicies that govern it, its

neighbours and their abilities etc.


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Perception:should be able to

sense its environment


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Learning:consequent to

perception it should be able tolearn about the general

characteristics of its environment

and trends


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Reasoning:relationships between

the various entities should beunderstood and sound decisionsinferred, obviating theoverwhelming, and perhapsimpossible, task of enumeratingevery single alternative


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Memory:should demonstrate

improved performance afteroperating in the same environment

over an extended period of time


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Architecture

Software Radio(perception)

Cognition Engine Learning Engine

(learning)

Reasoning Engine

(reasoning)

Knowledge Base

(awareness, memory)

SoftwareRadio

Cognition Engine

Knowledge

Base

Reasoning

Engine

Learning

Engine


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Layer-wise distribution of Meters

and Knobs Contd.METERS

Interference,

BER, receivedsignal power,

noise power,

SNR, fading

statistics,

doppler spread,

delay spread,

angle of arrival,

dynamic range

KNOBSPower, frequency bandof operation, carriermodulation type,

baseband modulationtype, pulse shaping,data rate, number ofchannels, bandwidth,

equalization, antennatuning, antenna steering,antenna heightadjustment, type ofantenna (if more thanone type of antennaavailable)

Physical Layer

Data L ink Layer

(MAC + LLC)

Network

Transport

Security

Application


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Layer-wise distribution of Meters

and Knobs Contd.METER

Frame error rate

KNOBS

Frame format,

Frame size,multiple access,

duplexing, FEC,

ARQ

(enable/disable)

Physical Layer

Data L ink Layer

(MAC + LLC)

Network

Transport

Security

Application


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MTU Size vs ThroughputRef: Anna Calveras Auge, Josep Paradells Aspas. Performance Optimisation Evaluation of

TCP/IP over Wireless Networks., IEEE Performance, Computing and Communications, 1998

For each BER value,

an optimal MTU

exists!

BER: meterat the

physical layer. MTU: knobat the

network layer.Neural

Network

Classifier

BER

ThroughputMTU

Figure is a graph for Point to Point Protocol (PPP) anddeterministic errors.

Graphs would be different for other protocols like Frame

Relay, Asynchronous Transfer Mode (ATM), Ethernet etc.

They would also vary depending on the error type, namely,deterministic and burst.

It is proposed that the graphs can be learnt by a Multi-

Layer Perceptron (MLP) or some other suitable mechanism.

Reinforcement Learning mechanisms are also potentially

relevant.


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OFDM Transmitter

Modulator(QAMQPSKBPSK)

Parallelto

serial

incomingbits

C1

C2

C3

Cn

IFFT

f1

f2

f3

fn

OFDM transmission offers opportunity

for adaptation!


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OFDM Receiver

Serial toparallel

De-modulator

incomingbits

C2

C3

Cn

IFFT

f1

f2

f3

fn

C1


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Capacity maximization in a non-AWGN channel

can be modeled as a Reinforcement Learning

problem.

Mmodulation types:M1,M2, ,MM.

N coding types: C1, C2, , CN.

Mican transmit didata bits per symbol and has a

probability of bit error ei(S) for signal to noise

ratio S.

Coding type Cjhas rate rjand can correct cjbit

errors per block of size bjbits. TS: symbol time, a constant.

B: corrected bit error rate coming out of the

decoder; measured by the radio.


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For modulation typeMiand coding type Cj,

the resulting capacity is given by:

Ci,j= ((dirj)/TS ) (1-B)

Ci,j can act as a performance measure.

Objective function:

max f(Mi,Cj,B)= dirj (1-B)


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Knowledge Representation

Each node or relation is associated to an

ontology that defines the concept. An

ontology consists of slots representingvarious attributes of that concept. For e.g.

here is the Device ontology created in

OWL (Web Ontology Language). (We usethe SWOOP editor to create and edit ontologies

and RDF Gravity to visualize them.)


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Device Ontology


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Reasoning

Reasoning involves traversing the semanticgraphs to obtain relevant conclusions.

Some ontologies for the cognitive radio: radio,channel, spectrum, power, coding, modulation,etc.

Some inferences:frequency fcis sparsely used

from time t1to time t2; for channel c, capacity ismaximized with modulation type miand codingmethod cjand so on.


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A Simple Example using

Predicate Logic (papers include authors from US DoD) Software Radio (SR) exports predicates to the

knowledge-base regarding detected signalss1,s2,,sN, of the form

signalFreq(si,fi) signalBW(si,Wi)

Goal: To find somefcand Wthat does not overlapany detected signal, while maximizing W andhence the radios capacity.

Define, notOverlap(fc,W,si)

= (fi+Wi/2fc+W/2)


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A Simple Example using

Predicate Logic Define predicate:

action: moveBand(fold,Wold, fnew, Wnew)

precond: i

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