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Cognitive Radio Architecture Evolution

Karol SchoberBased on paper by Joseph Mitola, III, Senior Member IEEE

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Introduction

CR prototype introduced 1998-1999 Exchanging of business cards (‘May, I introduce’) Intelligence involved

Past five years - SDR & CR under research concerning : spectrum allocation, market, bussiness and

open architecture Described is architecture for evolving heterogeneous

networks cellular merged with hot spots wireless technologies merged with human interface

technology

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Outline

Cognitive Radio ArchitecturesArchitecture and Use Case EvolutionSensory Perception in the Evolving CRAQuality of information

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SDR architecture

•speech

•internet access

•multimedia content

Radio architecture: framework by which products maybe integrated in to evolving sequence of designs with specific rules - public/proprietary

•GSM 900/1800

•CDMA

•Bluetooth

•PSTN….

Most of functionality can be

synthesized in software-based

chips like FPGA, Single-chip

Arrays and Blade servers

AD/DA

convertors are an issue

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Complexity of SDR

To manage complexity object and layer oriented programming has been adopted

CR in addition to SDR is capable of Sensing the environment and fit content to user

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Why are resources constrained?

Regulation is due to historical reasons Small/large bands were dedicated to public interest

with respect to economic aspect TV, 1st and 2nd generation cellular networks

Nowadays the radios are capable of multiband transmission and they are practically everywhere (ubiquity)

Heterogeneity of UE allows new way to go – Dynamic Spectrum Allocation

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Dynamic Spectrum Access

Process of increasing spectrum efficiency employing real-time adjustment of radio resources A real-time spectrum auction between systems with different

purpose, e.g. cellular network( stolen spectrum during peak hours) public safety (more access points for public safety)

Countries treat CR differently TV spectrum in US, European conservativeness, EU direction

for secondary training, Ireland DySPAN 100MHz The ideal iCR is difficult to implement thus XG defined

Simplified CR, simple rule-engine that controls radios air interface to conform to spectrum used policies (policy language) according to Haykin

Near-Future

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Haykin’s DSA

Integration of cognitive nodes into the Network

Key enabler for DSA

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How is iCR different?

Self-awareness, user awareness and machine learning

CR prototype – Wake epoch

Sensory perceptions:

•RF

•Location

•Motion

•Temperature

•Vision

•Speech

•….

Planing technologies

To identify changes in RF scene (IMEC Belgium)

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Epochs

Wake epoch see previous slide e.g. detection of new RF networks

Sleep epoch Computationally intensive pattern analyses, self-

organizing, autonomous learningPrayer epoch

interaction with higher authorities such as cognitive networks about restrictions , advice

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Networking and CRA evolution

Neither DSA , not iCRA provide architecture for cognitive wireless networks (CWN’s)

No real-time spectrum auction (standardization necessary) Supportive distributed network Policy language Method of payment

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Proposals – Fritzek and Katz

CWN CRA characterized by cooperation among intelligent entities

Cooperation considering : game theory, relays power allocation, diversity, cross-layer optimization, stability and security, distributed antennas, cooperative header compression, coding, distributed spatial channel control

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Proposals- Osaka University

Biological systems –robustness to catastrophe Molecular processes, immune system, social

insects, prey-predator relations Posses : Membership perception, network awareness,

buffer management, message filtering

Strassner’s key issues with lingua francaPlacing the cognition to layers (e.g. to sense

congestions)

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Architecture and Use case evolution

Media services and Internet changed the use case for Cellular networks

HOW? Product Differentiation

present Multimedia services competition User specific services

Protocol stack IPv4 should be adopted even if not ideal and later drift towards IPv6

OA&M Network management and administration based on self-awareness Agent-based evolved CRAs may assist to overcome misconfigurations

Location Awareness Not a “killer app” , but e.g. Google Maps, MapQuest will be able to move QoS to QoI

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Continue

Spectrum Awareness before network ordered the UE what band to listen to now the awareness comes to play, switching between WIFI, 3G,

Bluetooth recognition of Emergency calls in D-band U.S 700Mhz

Spectrum auction recently, cross-licensing of small parts of band Jondral’s group: leasing of BW chunks for 5s for browsing and email

for few cents would increase Spectrum utilization by 15-25% (how about revenue?)

User expectations Users adjust their expectations and usage (low/high mobility, hotspot,

3G) Operators move towards femtocells Femtocells recognized by GPS(not indoors ), computer vision,

speech

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The use case for Femto-cell handover

SAS hotel in Stockholm

The call are dropped when entering

GPS cannot say whether you are inside (glass foyer)

Camera would be a solution

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First Responder Situation awareness

the prioritizing of transmission (resource management) should be based on users situation, e.g. specific location surrounding (smoke) Movement (trapped) intent (rescue someone, escape)

Radio should be aware of user’s physical setting when getting resources

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Commercial Sentient Spaces

Confluence of technologies (3G, Wi-Fi, ….) Interference suppression Cognitive load balancing Cooperative power management

Elder/child care with speech and coputer vision Turning off stove Eating pills

Input of data Moving service from administrator to user (car-rental check-in)

Near future with IPv6 -> Users of wireless become devices rather than people

The smart homes are apart of radio, but can help to autonomously adjust radio resource priorities to changing needs of users.

Radio is as well mean to fast deploy the services Radio may be a control for large number of objects in smart-homes.

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Sensory Perception in the Evolving CRA

the point is to identify aspect of sensory technologies for future cognitive radio

Computer vision Surveillance (fall in parking lot) Internet retrieval The Video scene API should assist to CR to determine proper

speed and priority (car accident) Human Language Translation

For unburdening the user from contact with HMI Can assist to identify user needs better (e.g. interact with

medical devices)

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Computer speech

with Windows XP does not appear to be in wide use Precision [raw error rate]

3-10% in home office 25% 14.7% topic spotting

Speaker identification Background noise disturbing the model Thus only soft biometric measure (contribute to overall

Authorization )

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Text Understanding

Used in Business intelligence markets (source WIKI) refers to skills, technologies, applications and practices used to help a business acquire a better

understanding of its commercial context. Business intelligence may also refer to the collected information itself.

Common functions of business intelligence technologies are reporting, OLAP, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Causal relationship between unstructured customer contact reports (uCCR) Takes a lot of labor work mix word sense disambiguation, named entity detection,

sentence structure analysis Google depends on the laws of very large numbers

Typically there are small amount of uCCRs Android – statistical machine learning

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Text Understanding -continue

Query tool based on ALICE – AI better in answering than Google

Unstructured comments in wireless networks service, maintenance record CR communities may analyze themselves, optimize

and save costs Functional description languages

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Quality of Information

QoI=Quantity*Precision*Recall*Accuracy*Detail*Timeliness*Validity

Paremeters are Real [0..1] Are best at 1 Monotonic and approach 0 to degrade the QoI

Quantity No information for given situation, quantity is 0 Older information is better than no (maps) This was economically stupid, in future needed (congestion of

networks)

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QoI continue

Precision and Recall Degree to which information correspond to user’s need Recall 1 = all relevant documents retrieved Precision 1 = no irrelevant documents retrieved user may provide feedback by rejecting or ignoring

Accuracy Spelling the president name wrong Numerical accuracy Dependence (quadratic, linear, exponential, fractal ….)

Timeliness User’s time when the information is to be employed If needed now: then 1/ε is a good measure There might be window when Timeliness fall to zero immediately (death ) Wake up call should not be 15minutes earlier

Detail If sufficient detail then information provided is complete (I get the directions to

Restaurant) Promoting User as the 8th layer

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Challenges and opportunities

Some spectrums as GPS, UWB cannot be detected other than with correlation

Some bands are occupied 0.1% time and are necessary 100% (radar bands, airport) → noway to share

Emergency channels have to be clear to have maximal SNR possible (“Mayday” must be heard)

CRA should posses knowledge of forbidden bands Event though many paper exists on spectrum-

auctions and web-page rental of spectrum exists, there is no architecture deployed for real-time auctions

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Conclusion in the box

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Questions?

Please send to Mitola