Cognitive Radio - Startseite TU Ilmenau

Introduction to cognitive radioDr.-Ing. Mohamed Kalil

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Prof. Dr.-Ing. habil. Andreas Mitschele-ThielIntegrated Communication Systems Groupwww.tu-ilmenau.de/ics

Cognitive Radio Cross layer, adaptation and optimization

Dr.-Ing. Mohamed Kalil

01.12.2011


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Outline• Introduction • Cross layer design

– Traditional layer design– Migration from traditional layered to cross layer

• Cognitive radio operating parameters– Transmission parameters – Environmental measurements

• Radio performance objective– Single radio performance objectives– Multiple objective

• Cognitive Adaptation Engines– Expert Systems– Genetic Algorithms– Case-Based Reasoning Systems

• Summary


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Introduction • Within the evolution of wireless communication systems, two major

developments are most prominent: 1. Addition of new features (e.g. cell phone transmits voice and simple

text only cell phone with operating systems run multiple application 2. Improvement of already existing capabilities (Using the available

resources efficiently: adaptation and optimization are needed)

• Traditional layered approach still remains the same

• With the emergence of cognitive radio technology, the perception of– Cross layer design– Adaptation – Optimization gained new dimensions and perspectives.


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Cross layer design


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Traditional Layered Design and Its Evolution


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Traditional Layered: Advantages and disadvantages+/- Explanation Effect

Advantages

Modularity Each layer can be designed independent of others Simpler design

Standardization Design only requires to have the knowledge of explicit definitions and abstractions

Interoperability

Expandability Layers can be updated, altered, or expanded “independently”

Individual flexibility

Disadvatages

Ordering Execution of any process in any layer has to be after the execution of previous processes in former layers

• Inefficiency• Latency

Interaction Due to strict isolation, information cannot crossother layers

• Unawareness• Redundant processes• Sub-optimal performance

Adaptation In wireless communications, rapid channel variations cannot be responded immediately

• Decrease in capacity• Sub-optimal performance

Topologies Some of the network topologies need flexible layer architecture

Inefficiency


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Migration from traditional layered to cross-layer• Cross-Layer Design

– “Any kind of innovation on the traditional structure that blurs, changes, or even removes the boundaries between layers”

– Approaches:• Some of the designs only allow the information to flow upward and/or

downward direction• Some of them are based on merging some adjacent layers

– However, these approaches create new problems such as• More complicated design• Violating the independence of layers introduces additional dimensions to the

tasks of other layers

• Optimization– Single layer optimization– Multi-layer optimization


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Migration from traditional layered to cross-layer• Cross-layer architecture with optimization is not going to be

sufficient for ultimate system design goal

• Adaptation:– Complex problem in the cognition cycle– Cognitive radio needs to consider several requirements simultaneously

such as• User and application preferences• Its own capabilities such as battery status• Environmental conditions such as the availability of spectrum and

propagation characteristics, and so forth– Cognitive radio needs an overall adaptation that covers multiple layers

with the aid of optimization

Adaptation is needed


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Cross–Layer Adaptation and Optimization

Mem

ory

Sensors

Cognitive Engine


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Cross–Layer Adaptation parametersLayer Parameters

RF Antenna powersPre-distortion parameter…

Physical layer Transmit powerCarrier frequencyOperation bandwidthProcessing gain…

Data link layer Channel coding typePacket sizePacket type…

Network Routing algorithm/metricClustering parametersNetwork scheduling algorithm

Transport Congestion control parametersRate control parameters

Upper Communication modes (simplex, duplex, etc.)Source coding…


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Cognitive radio operating parameters


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Cognitive radio operating parameters• For cognitive radios to properly reconfigure, adapt, and optimize the

system, several key parameters of the system must be identified such as:– Transmission controls – Environment measurements

• In addition, an optimization methods, or “intelligent” control methods that– Can be run practically in real time– Meet quality-of-service (QoS) requirements


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Cognitive radio operating parameters• Environmental parameters:

– Path loss– Noise power– …

• Communication objectives:– Minimize bit error rate– Maximize throughput– Minimize power consumption– Minimize interference– Maximize spectral efficiency– ..

• Transmission parameters:– Transmit power– Modulation type– Modulation index– Frame size– Symbol rate – …

Transmission parameters

Environmental parameters

Communicationobjectives


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Transmission Parameters• Transmission parameters is refer to the list of parameters used to

control the individual radio components

Parameter Name Description

Transmit Power Raw transmission power

Modulation Type Type of modulation format

Modulation Index Number of symbols for given modulation scheme

Bandwidth Bandwidth of transmission signal in Hertz

Channel Coding Rate Specific rate of coding scheme

Frame Size Size of transmission frame in bytes

Time Division Duplexing Percentage of transmit time

Symbol Rate Number of symbols per second


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Environmental Measurements• Environmental measurements inform the system of the surrounding

environment characteristics

Parameter Name Description

Path Loss Amount of signal degradation lostdue to the channel path characteristics.

Noise Power Size in decibels of the noise power.

Battery Life Estimated energy left in batteries.

Power Consumption Power consumption of current configuration.

Spectrum Information Spectrum occupancy information.


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Single Radio Performance Objectives• In a wireless communications environment, the radio system may want to

achieve several desirable objectives such as:

Objective Name Description

Minimize Bit-Error-Rate Improve the overall BER of the transmission environment.

Maximize Throughput Increase the overall data throughput transmitted by the radio.

Minimize Power Consumption

Decrease the amount of power consumed by the system.

Minimize Interference Reduce the radios interference contributions.

Maximize Spectral Efficiency

Maximize the efficient use of the frequency spectrum.


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Example: Maximize throughput• Throughput definition is equivalent to the goodput, or the amount of good

information received at the receiver• The single objective function for maximizing the throughput:

_ 1

where– represents the raw bit rate of the system in bits per second– represents the frame length size in bytes– represents PHY layers overhead– is the MAC and IP layer overhead– is the probability of a bit error


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Multiobjective fitness function • Multiobjective fitness function problem

– Mapping of a set of m parameters to a set of N objectives– Ex.

, , , … ,Subject to

, , , … , ∈

, , , … , ∈Where– is the set of decision variables with as the parameters space– is the set of decision variables with as the objective space– represents the fitness function for a single objective


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Fitness Objective Representation• Fitness function guides the system to one optimal parameter set• Preference information (objective weighting)

– is used to rank the objectives in order to help the fitness function guide the evolutionary algorithm to one optimal solution

• Weighted sum approach– Suits the cognitive radio scenario well since it provides a convenient

process for applying weights to the objectives and more importantly provides a single scalar value

• Multiple objective fitness function of the parameter set solution by the following weighted sum of objectives:

with , … , satisfy the following constraints:1 0for 1,2, … ,

⋯ 1


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Multiple objective optimization

Throughput

Interference

PowerSpectraleffeciency

SINR

Direct objective dependencyIndirect dependency through Knobs

BW

BER

Computationalcomplexity


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Cognitive Adaptation Engines


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Cognitive Adaptation Engines• Cognitive adaptation engine:

– The core of the cognitive radio– The intelligence that drives the decision-making process– Its importance because of time-varying radio channel characteristics

and spectrum band availability

• Artificial intelligence techniques:– Expert systems– Genetic algorithms – Case-based reasoning– …


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Expert systems• An expert system uses

nonalgorithmic expertise to solve certain problems

• Expert systems components:– Each piece of expertise is typically

termed a rule and represented using an IF/THEN format

– Domain expert creates rules– Knowledge engineer is used to

encode the expert’s knowledge into a form that can be used by the expert system

Ex. IF frequency band of interest is currently in use THEN alter frequency

Say specifically what frequency is the optimal to use


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Expert systems– Working storage holds the problem-

specific information (facts) for the problem currently being solved

– Knowledge base is the representation of the expertise

– Inference engine includes the code that combines the information from the working storage and the knowledge base to find the solution

– User interface is simply the code that controls the dialog between the user and the system

Information (facts) such as Battery life, Channel noise figure, SNR frequency in use


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Genetic Algorithms• GA is a search technique inspired by biological and evolutionary

behavior• GAs are particularly well suited for applications like cognitive radio

where the search space can be time varying and require constant evolution, because– GAs work with a representation of the parameter set, not the

parameters themselves– GAs search from a population of points, not a single point– GAs use payoff (objective function) information, not derivatives or other

auxiliary knowledge GAs use probabilistic rules, not deterministic rules• Major steps:

– Reproduction– Selection– Crossover– Mutation– Expression


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Genetic Algorithms• Components:

– Chromosomes, which may be representations of a multi-dimensional solution search space

– Chromosomes are comprised of numerous individual “genes” which represent problem variables

– Each gene each of which may take on different “allele” values which represent the variable scope

Chromosomes

Gene

Allele

Solution

Problem variables

Variable space


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Genetic Algorithms: The Knapsack Example• The knapsack problem is defined by

– The task of taking a set of items, each with a weight, and – Fitting as many of these items into the knapsack while coming as close to, but not

exceeding, the maximum weight the knapsack can hold

• Mathematically the knapsack problem can be represented as follows

max

subjectto:

Where– is the maximum weight the knapsack can hold– is the number of items in the set, – is a weight vector– is a vector of 1 and 0 that indicates whether an item is present in the knapsack


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Genetic Algorithms: The Knapsack Example• Step 1. Initialize Chromosomes

• Step 2a. Choose• Step 2b. Crossover• Step 2c. Mutate• Step 2d Evaluate• Step 2e Replace

• Step 3. Results: Choose Best Chromosomes


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• Fitness function:– Represents how closely that chromosome solution solves the problem

at hand– The most fit chromosomes survive and are “reproduced” and the rest

are discarded• Goal:

– Minimum power

• Objective Name• Description

Genetic Algorithms: example

Genes Power Frequency Code Rate Modulation

Chromosome 1 0 dBm 2 GHz 1/2 QPSK

Chromosome 2 6 dBm 3 GHz 3/4 BPSK


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Genetic Algorithms: exampleSelection of chromosome 1 and 3 based on minimum power fitness function



Chromosome 2 9 dBm 4 GHz 2/3 64-QAM


Crossover at power gene




Selection of chromosome 1, minimum power




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Case-Based Reasoning (CBR) systems• A case can be defined as follows: “contextualized piece of

knowledge representing an experience that teaches a lesson fundamental to achieving the goals of the reasoner”

• CBR refers to the reasoning process based on previous recorded experiences (cases)

• Components of CBR1. Case Representation and Indexing2. Case Selection and Retrieval3. Case Evaluation and Adaptation4. Case Learning and Case Library/ Database Maintenance


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Case representation and indexing• A case usually consists of two parts:

– Content which records the experience or the lesson it teaches and – Indexes describes the context where this experience is gained and

where this experience might be useful in the future• The content contains the following information:

– Problem description:• Describes the relevant experience.• Specifies the detailed information about the problem, including the radio

environment and the service request with its QoS requirement– Solution:

• Explains how the problem was solved in the past• Specifies a possible radio configuration for the problem specified in the problem

description– Outcome:

• Records the result of applying the solution• Specifies the feedback from the real environment (e.g., success or failure) after

the solution was applied to the CPE• Indexes specify the context where the content of a case is gained

and where it is useful and describe the distinguishing features of a case


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Case representation and indexing

Problem Solution Outcome

Interleaving Power BandwidthEvent State Modulation and coding Feedback

Channel Condition

QoSRequirement

Case Structure


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Case selection and retrieval• The case selection and retrieval module

– Searches the case database for cases that satisfy the request to a certain extent

– First, the cases are retrieved from the cases database– Then, the retrieved cases are checked against the policy vector– The selection is based on some utility metrics such as

• Bit error rate• Data rate• Transmission power• …

– One or multiple valid cases with highest utility are returned


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Case evaluation and adaptation• The case adaptation module

– Evaluate the performance of the retrieved case from the case selection and retrieval module

• By using a performance model or• By applying the solution and observing the outcome

– The retrieved case is recorded as a solution to a previous problem and retrieved as a possible solution to the new problem due to the similarity between the old problem and the new one

– If the performance of the retrieved case is not satisfactory, the case is modified by the case adaptation module


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Case learning and case library/database maintenance

• A case library is an ensemble of similar cases• Experiences are remembered by the CBR system as cases in the

case database• CBR gains additional information or learn by solving new problems

or receiving feedback• As the experience increases, more cases are accumulated in the

case database• Case database maintenance

– Remembering solution for future use– Remembering the outcome of applying this solution


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Case-based reasoning cognitive engine framework

• Main components:– Spectrum Manager (SM)

• Monitors the radio environment and interfaces with the physical radio hardware• Abstracts the information from the spectrum sensing module • Exchanges information with the radio environment map module• Allocates resources including channels and subcarriers according to the solution

from the multi objective optimizer module– Policy Engine (PE)

• Specifies the general policies including the standard, the regulation, …• Guides the operation of the case-based reasoning module

– Case-Based Reasoner (CBR)• Provides candidate solutions based on the request from the PE module

– Radio Environment Map (REM) Database• Stores scenario specific parameters about the system such as network and

service availability, policies,..– Multi objective Optimizer (MOO)

• Adapts the solutions returned by the CBR to satisfy QoS requirement of the new problem

– Expected Radio Performance Indicator (RPI)• Evaluates the anticipated performance for a given solution with the utility function

before applying it in the real environment


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Case-based reasoning cognitive engine framework

• Functional processing flow:

Spectrum Manger

Policy Engine (main decision maker)

Case‐based Reasoner

REM Database

Multi‐objective Optimizer

Expected Radio Performance Indicator (RPI)

1Query for

solution/action2 Return solution3

4

5

Execute MOO6Return solution78


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Summary• Cognitive radio needs an overall adaptation that covers multiple

layers with the aid of optimization• Migration from traditional layer approach to cross layer is a must in

cognitive radio • Several key parameters of the system must be identified such as:

– Transmission controls – Environment measurements– Communication objective

• Cognitive adaptation engine is the core of the cognitive radio• Several AI approaches can be used in the cognitive engine:

– Expert systems– Genetic algorithms – Case-based reasoning– …


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References• A. M. Wyglinsk, M. Nekovee and Y. T. Hou “Cognitive Radio Communications and Networks

Principles and Practice” Academic Press, 2010• H. Arslan “Cognitive radio, software defined radio, and adaptive wireless systems” Springer 2007• B. A. Fette “Cognitive Radio Technology” Newness, 2006• T. R. Newman “Multiple Objective Fitness Functions for Cognitive Radio Adaptation” PhD thesis,

University of Kansas, 2008• C. J. Rieser “Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic

Algorithms for Secure and Robust Wireless Communications and Networking”, PhD thesis,Faculty of the Virginia Polytechnic Institute and State University, 2004

• A. He, J. Gaeddert, T. R. Newman, J H. Reed, Lizdabel Morales, K. Kyoon Bae and C. Park“Development of a Case-Based Reasoning Cognitive Engine for IEEE 802.22 WRANApplications” Mobile Computing and Communications Review, Volume 13, Number 2, 2009

• A. He,…. “ A Survey of Artificial Intelligence for Cognitive Radios” IEEE Transactions on VehicularTechnology, Vol. 59, 2010

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