università degli studi di genova dipartimento di ingegneria biofisica ed elettronica prof. c.s....

Università degli Studi di GenovaDipartimento di Ingegneria Biofisica ed Elettronica

Prof. C.S. Regazzoni

DIBE

Sistemi di Radiocomunicazione

From Software Defined Radioto Cognitive Radio

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Outline

• Introduction From Software Defined Radio to Cognitive Radio Software Defined Radio vs Cognitive Radio Cognitive Radio

• Cognitive Cycle MItola’s CC, Haykin’s CC, simplified CC Knowledge representation Embodied Cognition Bio-inspired Model

Outline

• Application examples in CR E2R Infomobility framework

Cognitive Cycle in practice

Analysis Phase

Decision Phase

Results

Remarks

Introduction

From SDR to CR – SDR Technologies

• Historically, radios have been designed to perform a given task

• As upgrades were desidered to increase capability, reduce life cycle costs, and so forth, software was added to the system design for increased flexibility

• In 2000 a SDR has been defined by FCC as:

A communication device whose attributes and capabilities are developed and/or implemented in software

From SDR to CR – SDR Technologies

• The required additional flexibility and addiotional capabilities have been provided step by step

• The radio system capabilities can evolve to accomodate a much broader range of awareness, adaptivity and learning

Software Capable Radio

• A software capable radio has the following characteristics

Fixed modulation capabilities Manage a small range of frequencies Limited data rate Ability to handle data under software control

Software Programmable Radio

• A software programmable radio has been designed upon a software capable radio and it has the following additional characteristics:

Ability to add new functionalities through software Advanced networking capabilities

Software Defined Radio

• Software Defined Radio (SDR) systems main characteristic is the complete adjustability through software of all radio operating parameters.

• Required reconfigurability is provided thanks to software management of the considered system

• It is a practical reality today, thanks to the convergence of two key technologies: digital radio, and computer software.

Aware, Adaptive and Cognitive Radios

• Radio that sense all or part of their surrounding environment are considered aware systems

• A radio must additionally autonomously modify its operating parameters to be considered adaptive

If a radio is reconfigurable, aware, adaptive and learns

COGNITIVE RADIOCOGNITIVE RADIO

Aware Radio

• It is equipped with sensors able to gather environmental information

• In general many kind of sensors can be considered in Aware Systems, e.g. antenna, microphone, camera, probes, etc.

• The key characteristic that raises a radio to the level of aware is the consolidation of environmental information not required to perform simple comms

Adaptive Radio

• Frequency, istantaneous bandwidth, modulation scheme, error correction coding, channel mitigation strategies, data rate, transmit power, etc, are operating parameters that may be adapted

• Example: A FHSS radio is not considered adaptive because once

programmed for a hop sequence it is not changed. A FHSS radio that changes hop pattern to avoid/reduce

collisions may be considered adaptive.

Cognitive Radio

• A CR has the following characteristics: Sensors creating awarness of the environment Actuators enabling interaction with the environment Memory and model of the environment Learning capability that helps to select a specific action or

adaption to reach a specific goal Autonomy in action (unsupervised system) An engine able to take constrained decisions

Comparison: from SDR to CR

Cognitive Radio

Cognitive Radio

• Cognitive radio designed upon SDR, has been proposed as the means to promote the efficient use of the spectrum by exploiting the existence of opportunities

Cognitive Radio

• According to the Encyclopedia of Computer Science cognition encompasses the following steps: Mental states and processes intervene between input stimuli

and output responses The mental states and processes are described by

algorithms The mental states and processes lend themselves to

scientific investigations.

Cognitive Radio

• Moreover, the interdisciplinary study of cognition is concerned with exploring general principles of intelligence through a synthetic methodology termed learning by understanding.

• Putting these ideas together and bearing in mind that cognitive radio is aimed at improved utilization of the radio spectrum, Haykin offers the following definition for cognitive radio

Definition

• CR is an intelligent wireless communication system

that is aware of its surrounding environment, and

uses the methodology of understanding-by-building to

learn from the environment and adapt its internal

states to variations in the perceived RF stimuli by

making corresponding changes in operating

parameters in real-time, with two primary objectives

in mind:

highly reliable communications whenever and

wherever needed;

efficient utilization of the radio spectrum.

Definition

Common keyword in this context are:• Spectrum awareness

the understanding of what is happening in the electromagnetic spectrum

• Self Adaptation the capability to adapt the system parameter according to an

evolving external scenario

• Intelligence or cognition the capability to learn from the interaction with the

environment

• Efficiency The capability to efficiently exploit the available radio

spectrum

Cognitive Radio - models

• In general the Cognitive Systems can be characterized by different cooperative phases, which, together with continous learning, are really powerful tools for all kind of applications.

• CR behavior can be modeled as a cognitive cycle• In the literature different cognitive cycles have been

provided in order to describe CR behavior

Mitola’s Cognitive Cycle

According to Mitola it is possible to model the CR behavior as follow:

• Stimuli enter the CR as sensory interrupts, dispatched to the cognitive cycle for a response

• CR sequentially observes (senses and perceives) the environment, orients itself, creates plans, decides, and then acts.


• Observe (Sense and Perceive) The CR observes its environment by parsing incoming RF

stimuli.

• Orient determines the significance of an observation by binding the

observation to a previously known set of stimuli

• Plan reasoning over time


• Decide selects among the candidate plans

• Act initiates the selected plans using actuators which access the

external world or the CR’s internal states.

• Learning Learning information and experiences, together with

decision, is the most important capability for a cognitive system

Haykin’s Cognitive Cycle

Haykin focuses on three on-line cognitive tasks:1. Radio-scene analysis:

estimation of interference temperature of the radio environment;

detection of opportunities2. Channel identification:

estimation of channel-state information (CSI); prediction of channel capacity

3. Transmit-power control and dynamic spectrum management.


• The cognitive process starts with the passive sensing of RF stimuli and culminates with action.

• Tasks 1) and 2) are carried out in the receiver, and task 3) is carried out in the transmitter.

• the cognitive module in the transmitter must work in a harmonious manner with the cognitive modules in the receiver


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Cognitive Cycle: a comparison

• Haykin defines the behavior of CR system through a Cognitive Cycle, similar to Mitola's one, but much more clustered in macro-phases

• Mitola is much more interested on the impact of the cognitive capabilities onto the communications market, Haykin faces the problem from a more general point of view.

• Conversely, both researches agree on the fact that the SDR systems are the natural platform for the implementation of CR devices.

• While in the Mitola’s vision the CR is suited to realize the user’s preferences, in the Haykin’s one it is well explained a cognitive communication between a transmitter and a receiver.

• In both of the previous visions it is clear the effort to model the CR s an entity able to

reason about and analyze the external world modify its internal configuration to reach the best

solution

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Cognitive Cycle: a comparison

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In general, the behavior of a Cognitive Systems can be characterized by four sequential cooperative phases, which, together with continuous learning, are really powerful tools for all kind of applications.

These phases constitute the four main capabilities of the Cognitive Cycle:

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Cognitive Cycle for CR apps: A simplified vision

ACTION

SENSING

DECISION

ANALYSIS

LearningPhysical

World

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Cognitive Cycle: A simplified vision

Sensing

is a passive interaction component: the system has to continuously acquire knowledge about the interacting objects and its own internal status

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E.g.

Sensing process can be view as the scan of the electromagnetic environment by an antenna or the acquisition of an image sequence by a camera

http://ginevra.dibe.unige.it/ISIP/Projects/imageH3O.jpg

http://www.rainbird.com/about/imagelibrary/central/images/Computer%20Monitor.jpg

http://ginevra.dibe.unige.it/ISIP/Projects/imageH3O.jpg

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Analysisperceived raw data need an analysis phase to represent them and extract interesting filtered information

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E.g. Analysis process extracts information of interest like users’ positions in an angle-frequency map or features used for classification or tracking

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Decision

the intelligence of a CS is expressed by the ability to decide for the proper action, given a basic knowledge, experience and sensed data

It is one of the most critical and complex phase of the cycle

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Decision phase

There are many different approaches to decision phase:♦ Rule Based algorithm They are based on the paradigm IF ….

THEN …

♦ Semantic Networks It’s a graph designed by an ensemble of nodes linked each other by arches: nodes represent objects, situation or events, while arches mean their relations

♦ Decision Trees It’s a framework designed on a tree where every internal node represents an adjective and every leave represents a label of class

♦ Memory Based Reasoning With this technique it is possible to classify basing on previous experiences

We will focus on bio-inspired algorithms for knowledge representation and decision phase

EMBODIED COGNITION APPROACH

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Action

expresses the active interaction the CS can take in relation to its decision. The system tries to influence its interacting entities to maximize the functional of its objective

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E.g.

This phase represent how the CS interacts with the environment with actuators and communication systems likes antennas

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Learning

Learning information and experiences, together with decision phase, is the most important capacity for a cognitive system.

There are many different approaches to this task that can be generally divided in:

♦ Supervised Algorithms (Artificial Neural Network, Support Vector Machine, Bayesian Learning)

♦ Unsupervised Algorithms (Self Organizing Maps, Radial Basis Function Network, Reinforcement Learning)

We will focus on a bio-inspired learning algorithm

AUTOBIOGRAPHICAL MEMORY

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The cognitive cycle represents a general framework It is necessary to specify how the knowledge is managed and processed within each stage of the cycle

A knowledge representation and organization is necessary

Knowledge representation and organization

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In general the knowledge managed by the cognitive cycle can be:

♦ an a-priori identification, at a symbolic level, of all the knowledge necessary to perform the different phases of the cycle;

♦ acquired through experiences.

It can be organized according to two principal models:• The former model tries to describe the knowledge in a symbolic and semantic way i.e. the classical rule-based approach for AI (Expert Systems); • Embodied cognition.

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Knowledge representation and organization

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Rule-based approaches

A Rule-based expert system is a representation of the human beings natural reasoning and problem-solving paradigm. It models the human’s production system using the following modules:

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Rule-based approaches

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Knowledge base - models a human’s long term memory as a set of rules.

Working memory - models a human’s short term memory and contains problem facts both entered and inferred by the firing of the rules.

Inference engine - models human reasoning by combining problem facts contained in the working memory with rules contains in the knowledge base to infer new information.

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Embodied Cognition

• Embodied Cognition approach takes inspiration from Robotics works of Rodney Brooks and looks at intelligence as to an emergent behavior of a set of agents.

• This approach is based on a model of representation of the knowledge which describe in a priority manner the physical capabilities of action of the entity where happen decision and action.

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Knowledge can be viewed as organized following spatial maps centered on the entity, where information are represented as created by processes of analysis and decision at different semantic levels.

To mantain this separation between decision and action it is necessary a mechanism related to perception of events derived by actions (endo-sensors)

Embodied Cognition

Action

Decision

Endo-sensors Entity

Spatial internal

map

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Evolution following Embodied Cognition approach

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DecisionAction

DecisionInternal sensor Action

Feedback

Command

Map of feedbacks

EvolutionCommand

Feedback

Map of commands

Stage1: Decision and Action use only internal information (endo-sensors)

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DecisionVirtual internal sensor Action

Feedback

Map of commands

Commands

Analysis

map

Ext. sensor

Int. analysis

Ext. analysis

Int. sensor

External

Analysis

Map

Internal

Analysis

Feedback


Stage2: Evolution leads to distinguish between internal and external state Decision and Action use internal information (endo-sensors) and external (eso-sensors)

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Command Feedback

External spatial map

Interacting Entity


Stage3: Distinction between internal and external state leads to generation of a consciousness (distinction between itself and other from itself)

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Embodied cognition: a possible definition

Following Anderson definition:

“it focuses the attention on the fact that most real-world thinking occurs in very particular (and often very complex) environments, is employed for very practical ends, and exploits the possibility of interaction with and manipulation of external props. It thereby foregrounds the fact that cognition is a highly embodied or situated activity and suggests that thinking beings ought therefore be considered first and foremost as acting beings ”

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Recent studies of neurophisiology have confirmed, ata biological level, the effectiveness of this approach.

Eg. one of the primary goal of intelligent multicellular organisms evolving toward higher level organisms is to use contextual information obtained through sensing to move in the surrounding environment to reach a safer or a food reacher point.

In the human brain, these kind of motions are generated by specific groups of neurons called Fixed Action Patterns (FAPs), whose output is able to modulate motor muscles actions according to a codified sequence of effector signals. Sequences are modulated by FAPs, basing on the contextual information acquired through the senses.

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Evaluation of Embodied Cognition

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Embodied Systems

The representation of the internal knowledge in embodied systems, and hence the description of context, is strictly linked with the motion possibilities of the entity itself.

In general the body of the system has an important role in the evolution of the entity The body can be considered not only as the instrument to perform the only action phase but this concept can be extended for every phase of the cycle.E.g. Make more fine the sensing phase lead to a different representation of the knowledge (more information to manage) respect to a coarse sensing phase

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A Bio-inspired Model

Up to now some fundamental concepts have been pointed out:

How a conscience can be represented Damasio’s approach core self, proto self, autobiographical memories, autobiographical self

The relations between actions and consciousness Llinas approach FAP

How the knowledge representation can influence the cognition process embodied cognition

How can be represented a living entity related to its environment cognitive cycle

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A Bio-inspired Model

All of this concept can be fused together to supply a coherent model to represent a cognitive system

Application examples in cognitive radio

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Application example in cognitive radio - Motivations

Growing success of wireless communications systems

Fundamental problem: lack of available spectrum

A lot of studies supported by FCC: • point out a scarce effective utilization of the wireless

spectrum • encourage new solutions to exploit underutilized

bands• demand for the overcoming of the exclusive use of

the allocated frequencies

• COGNITIVE RADIO (CR) paradigm is the proposed solution

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Which are the possible applications of a CR system?

• Exploitation of unused frequencies (or opportunities, in general) e. g. spectrum holes

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Which are the possible applications of a CR system?

• Self-interconnection and self-management of different systems with different standards

• Homeland security• Signal interception and identification• Military applications• Emergency situations

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Application example in cognitive radio - Apps

One of the most important characteristics of a CR is theLEARNING CAPABILITY

Provide the CR with a sort of intelligence increase its adaptivity and flexibility

Hence the CR can be used not only in well known situations but also in unexpected or unforeseen scenarios

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Application example in cognitive radio

Haykin and Mitola’s cognitive cycles can be simplified in order to obtain the proposed bio-inspired cognitive cycle:

LEARN

ACTION

SENSING

DECISION

ANALYSIS

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Application example in cognitive radio – Cognitive Cycle

It is also important to underline that the flexibility guaranteed by the previously described bio-inspired approaches can be extended not only at the physical layer of the communication (as in Haykin’s vision) but at the entire ISO-OSI communication model.

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These kinds of applications are aimed for obtaining a global optimization of the configuration parameters of the wireless systems by

overcoming the traditional limits among different levels realizing a cross layer optimization management systems

This is the case of the most important european project E2R.

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E2R

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E2R

End-to-End Reconfigurability (E2R) is the key enabler for providing a seamless experience to the end-user and the operators:

Managing and increasing resilience growingly complex architectures

Reducing costs deployment, evolution and operation of large communication systems

Providing opportunities develop and experiment rapidly new services and applications

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Most of the developed CR project are characterized by fixed or slightly flexible models.

In general, these models has to be accurate and often it is necessary to include in them some severe constraints.

To overcome these limits it is possible to apply the theory related to bio-inspired cognitive radios

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• The chosen cognitive system is composed by a Cognitive Base Transceiver Station (CBTS) for mobile applications.

• Task of CBTS is to manage communication with a set of mobile stations in a vehicular context

• CBTS is equipped by a “smart antenna” system

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Application example in cognitive radio: Infomobility

The cognitive cycle proposed in the general theory is well suited to chosen application

ACTION

SENSING

DECISION

ANALYSIS

LearningPhysical

World

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Application example in cognitive radio : Infomobility

Let us describe the cognitive cycle mapped for the chosen application:

• Sensing: the smart antenna system performs a scanning of the environment

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• Analysis: extract from the context the presence of the users in the domain of interest

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• Decision: allocate available resources

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• Action: reconfigure the beamformer in order to provide a reliable communication link to the a user

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• In the next few slides we’ll focus our attention on two important phase of the cognitive cycle:

• ANALYSIS PHASE

• DECISION PHASE

Analysis Phase

• Most important phase for radio scene analysis is carried out by ANALYSIS PHASE

• Input: oversampled filtered received signal (from Sensing phase)

• Output: transmission mode of each user enriched with context information (to Decision phase)

• Objective: identify the transmission mode of each user

Analysis Phase

• The analysis phase of the proposed Cognitive Cycle is composed by two sub-phases:

FEATURES EXTRACTION

CLASSIFICATION

Analysis Phase

• Different algorithm can be applied to analysis phase• Stand-alone techniques

Energy detector Matched Filter Feature detection (the one applied in this example) Transformation

• Cooperative and Distributes Distributed Detection with Fusion Distributed Detection without Fusion

Analysis Phase – features extraction

• Features extraction: Discrete Fourier Transform (DFT) of the input signal

• normalized DFT is used as probability density function (pdf)

• extracted features x are conditional moments of evaluated pdf mean standard deviation kurtosis

The analysis map:

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Analysis Phase – map

The features space:

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The extracted features: kurtosis

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Analysis Phase – classification

• In order to classify signal based on extracted features it is possible to apply different existing tools:

Bayesian/Hidden Markov Models Neural Networks Support Vector Machines K-NN, Parzen Windows

Analysis Phase – classification results example

• For the proposed intelligent system a Reinforcement Learning (RL) approach it is chosen for decision decision phasephase.

• RL is a machine learning technique that unlike other machine learning approaches is: model free unsupervised able to learn on line

• RL approach allows the system to learn the correct strategies for interactions with the environment

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Decision Phase – Algorithm

Decision phase, carried out using the RL, can be described by using:•Input: high level description of the environment status xc (from analysis)•Output: establish new configuration xp (to action)

•Objective: choose action that maximizes r (reward)•Experience = capability of predicting r given xc and xp

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Decision Phase – General Algorithm

Decision Phase – Algorithm in the proposed apps

• DECISION AGENT provides intelligent control for the system

• Input: external state number of detected users associated direction SNR of each established link Transmission modality used

• Output: new internal state beamformer configuration power for each link

• Goal: choose an action that maximizes the reward• Experience is the capability to predict the reward

given an internal and an external states

Learning task: estimation of the Q-function

Q(xc, xp) = E{ r | xc, xp} :

at the beginning Q = 0 for every couple (xc, xp);

if you encounter (xc, xp) and receive a reward r then

Q(xc, xp) = αr + (1 - α )Q(xc, xp)

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Decision task: balance exploration and exploitation ε-greedy policy is the chosen strategy:

with probability 1 - ε, exploits: choose

xp = arg[maxxp (Q(xck, xp))]

with probability ε, explores: pick xp randomly

It is a simple algorithms but theoretical results guarantee convergence

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Reward: maximize the SNR (equally minimize the steering error)

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Decision Phase – results

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Decision Phase – remarks

• Since the reward does not penalize the used power the base station maximize the SNR using almost all available power

• It is possible to change the reward thanks to adaptivity provided by reinforcement learning approach

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Reward: maximize the SNR (equally minimize the steering error) keeping

as low as possible tx power

Reward: maximize the SNR (equally minimize the steering error) keeping

as low as possible tx power91


Decision Phase – conclusive remarks

• In this case results regarding steering error are omitted because they are similar to the previous one

• Changing the reward the system maximize the steering error as before keeping as low as possible the used power

università degli studi di genova dipartimento di ingegneria biofisica ed elettronica prof. c.s....

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