Cognitive Radio from a Mobile Operator’s Perspective:
System Performance and Business Case Evaluations
PhD Dissertation, 1...
Mobile operators need to solve the key challenges for
future wireless access
Massive growth in

Massive growth in

Wide ra...
Power Spectral Density

… but not well utilized

0

3

1

2

3

4

5

6 GHz
Cognitive radio is an “intelligent” and flexible radio
system that can observe and learn from the environment
and adapt ac...
Cognitive Radio can be used to dynamically access
spectrum that is underutilized
Spectrum Holes
(White Spaces)

Frequency
...
The problem statement
Increased performance
Lower costs, increased revenue
New business models, services

How can a mobile...
Cognitive Radio brings threats and opportunities for the
mobile operator

Threats
• Reduced value of spectrum licenses
• I...
The methodology focused on both technical and
economical evaluation of Cognitive Radio systems

8
We studied three important areas for Cognitive Radio with
focus on the mobile operator's perspective

Dynamic spectrum acc...
Outline

Dynamic spectrum access in primary
OFDMA systems
(Paper A)

Sensor Network Aided Cognitive
Radio Systems
(Papers ...
Dynamic spectrum access in the time dimension in primary
OFDMA networks can be possible

…but, our results show that coope...
Outline

Dynamic spectrum access in primary
OFDMA systems
(Paper A)

Sensor Network Aided Cognitive
Radio Systems
(Papers ...
Sensor Network aided Cognitive Radio (SENDORA)
system

13
Three business case scenarios were studied for the
SENDORA concept

Spectrum
owner 1

Spectrum
owner 2

Spectrum
owner N

...
The “spectrum sharing” business case is probably one
of the best cases for a SENDORA system
It has free access to spectrum...
A SENDORA system was implemented in a simulator to
evaluate the capacity for different cell sizes
Primary System
Inter-BS-...
Cognitive radio is best suited for smaller cells such as
WiFi access points and femtocells
… but relaxed interference requ...
Offloading the LTE network using Cognitive Femtocells
aided by a sensor network

1) deploy
cognitive
femtocells
2) deploy
...
We compare with the case of using conventional
femtocells and additional base stations

1) deploy
conventional
femtocells
...
Offloading LTE with cognitive femtocells can be more cost
effective than using conventional femtocells and additional
base...
Outline

Dynamic spectrum access in primary
OFDMA systems
(Paper A)

Sensor Network Aided Cognitive
Radio Systems
(Papers ...
We implemented a detailed simulator to evaluate performance
of the first standard for Cognitive Radio, IEEE 802.22

System...
Performance for different sensing strategies should be
considered dependent on required Quality of Service (QoS)

Scenario...
Spectrum selection (SSE) functions that utilize sensing results
to provide long term statistics can increase performance
S...
In conclusion, a mobile operator can use Cognitive Radio to achieve
well performing technical and economic viable solution...
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Cognitive Radio from a Mobile Operator's Perspective: System Performance and Business Case Evaluations (PhD defense Pål Grønsund 18.oct 2013)

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Presentation for PhD defense by Pål Grønsund:
Cognitive Radio from a Mobile Operator's Perspective: System Performance and Business Case Evaluations

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  • JointVenture:Easy to implement from a regulatory point of view since only the joint venture owners’ own spectrum are usedThe scenario is an example of spectrum sharing, which can be seen as a natural extension of infrastructure sharing: The joint venture is a good way to share the expenses and incomes between the companiesThe joint venture can be composed in a way that makes it very probable that:at least some unused spectrum is available at all timeslittle new cognitive radio access infrastructure is requiredSpectrum Broker:New Entrant:
  • PS: Point ontheusabilityoftheresults:Sensor network planning (density of sensors and coordination between fixed and integrated sensors)Sensor design to minimize CAPEX and OPEXSolutions that allow re-use of existing infrastructure and require few new sites
  • - Secondary cell size = 1/2 primary cell size (rs=0.575km)Secondary system performed well with high throughput.However, 75% of secondary BSs will not be co-located with primary BSs, leading to high costs for the establishment of new sites.This points in the direction of smaller and less expensive BSs such as WiFi access points and femtocells.Secondary cell size = primary cell size (rs=1.15)100% BS co-location will not be achievedPotential solutions which could be studied for future work:Cell sectorizationRelaxed requirement to allow secondary operation, and dynamic requirements when primary nodes have good connectivityDynamic transmit powers- comesto the costs of a decrease in primary system performance with a slight reduction in throughput and increased packet loss with avg. 2%.-This can be considered as tolerable for business models where the primary operator has an economical benefit in improved secondary system performance.
  • Two-stage: coarse sensing of 1 msec every 2nd frame, (pd =0.9, pf =0.1), fine sensingof 30 msec (pd =0.99, pf =0.01).Two-stage consecutive: same as two-stage, but 3 consecutivecoarsedetectionsareneeded to trigger fine sensing.Single-stage-30: fine sensingof 30 msecevery 0.5 sec (pd =0.99, pf =0.01).Single-stage-100: fine sensingof 100 msecevery 0.5 sec (pd =1, pf =0).
  • By characterizing spectrum usage and analyzing potential capacity in primary OFDMA networks, we showed that there is a potential for CR systems to utilize white spaces.We found that one of the most promising business cases for an operator using a sensor network aided CR system is that of a joint venture that gets the rights to use the ``unused'' spectrum resources of spectrum owners. The most business critical parameters were found to be the fixed sensor density, the fixed sensor OPEX and the number of new base station sites required.By using technical simulations we found that high reuse of existing base station sites is difficult to achieve, which points in the direction of shorter range and less expensive access points such as femtocells.However, we found that full reuse of base station sites can be achieved by relaxing interference requirements for the CR to the primary network.We showed that a promising business case is to use cognitive femtocells aided by a sensor network to offload the LTE network. The most business critical parameters were found to be the price for backhauling the cognitive femtocell and the number of supported users by the cognitive femtocell.We evaluated performance of a CR system based on the IEEE 802.22 standard with spectrum sensing and found that the activity of wireless microphones as the primary users should be quite high to reduce throughput and delay.Interference from IEEE 802.22 devices to the wireless microphone was found to be low in general and to occur only for short periods when using novel sensing strategies.We showed that the guaranteed bit rate QoS service for VoIP can be prioritized in IEEE 802.22, though the sensing strategy is important to satisfy strict QoS requirements for throughput and delay.We showed that spectrum selection functions that utilize sensing results to provide long-term spectrum usage statistics as basis for channel selection can enhance performance in IEEE 802.22.
  • Cognitive Radio from a Mobile Operator's Perspective: System Performance and Business Case Evaluations (PhD defense Pål Grønsund 18.oct 2013)

    1. 1. Cognitive Radio from a Mobile Operator’s Perspective: System Performance and Business Case Evaluations PhD Dissertation, 18.october 2013 Pål Grønsund Supervisors: Paal E. Engelstad, Przemyslaw Pawelczak, Audun F. Hansen, (Ole Grøndalen)
    2. 2. Mobile operators need to solve the key challenges for future wireless access Massive growth in Massive growth in Wide range of Traffic Volume Connected Devices Requirements • • • • • • Data rate Latency Coverage Energy Device cost …. Spectrum Available Network Traffic capacity = Spectrum X Density X Efficiency (MHz) (sites/km2) (Mbps/MHz/site) 2
    3. 3. Power Spectral Density … but not well utilized 0 3 1 2 3 4 5 6 GHz
    4. 4. Cognitive radio is an “intelligent” and flexible radio system that can observe and learn from the environment and adapt accordingly 4
    5. 5. Cognitive Radio can be used to dynamically access spectrum that is underutilized Spectrum Holes (White Spaces) Frequency Power Cognitive Radio Time t1 t2 t3 Spectrum occupied by licensed users 5
    6. 6. The problem statement Increased performance Lower costs, increased revenue New business models, services How can a mobile operator benefit from using cognitive radio to opportunistically access white spaces, with the potential to enable sustaining and disruptive innovation? 6
    7. 7. Cognitive Radio brings threats and opportunities for the mobile operator Threats • Reduced value of spectrum licenses • Increased interference if other cognitive radios uses • the mobile operator’s own spectrum Increased (unfair) competition Opportunities • Access to more spectrum in existing networks • Opportunity to access spectrum in new markets 7
    8. 8. The methodology focused on both technical and economical evaluation of Cognitive Radio systems 8
    9. 9. We studied three important areas for Cognitive Radio with focus on the mobile operator's perspective Dynamic spectrum access in primary OFDMA systems (Paper A) Sensor Network Aided Cognitive Radio Systems (Papers B - E) Performance of the first Cognitive Radio Standard IEEE 802.22 (Papers F - I) 9
    10. 10. Outline Dynamic spectrum access in primary OFDMA systems (Paper A) Sensor Network Aided Cognitive Radio Systems (Papers B - E) Performance of the first Cognitive Radio Standard IEEE 802.22 (Papers F - I) 10
    11. 11. Dynamic spectrum access in the time dimension in primary OFDMA networks can be possible …but, our results show that cooperation with the primary operator is important to reduce interference and increase capacity 11
    12. 12. Outline Dynamic spectrum access in primary OFDMA systems (Paper A) Sensor Network Aided Cognitive Radio Systems (Papers B - E) Performance of the first Cognitive Radio Standard IEEE 802.22 (Papers F - I) 12
    13. 13. Sensor Network aided Cognitive Radio (SENDORA) system 13
    14. 14. Three business case scenarios were studied for the SENDORA concept Spectrum owner 1 Spectrum owner 2 Spectrum owner N Business case II Spectrum broker Business case I Joint venture “Spectrum Sharing” New entrant New entrant Business case III 14 New entrant
    15. 15. The “spectrum sharing” business case is probably one of the best cases for a SENDORA system It has free access to spectrum from the mother companies, and good possibilities for re-using existing infrastructure. The most critical aspects for profitability are: • Fixed sensor density • Fixed sensor OPEX • Subscription fee (service offered) • Share of new sites A “new entrant” cognitive radio operator might get a positive business case if it gets the spectrum for free, otherwise it will be difficult. 15
    16. 16. A SENDORA system was implemented in a simulator to evaluate the capacity for different cell sizes Primary System Inter-BS-dist: 2km Radius rp =1.15km Secondary System Radius rs = ? Wireless Sensor Network*: 65 sensors/km2 Sensor radius rws=87.7m (*values from business case analysis) Primary Base Station Sensor Primary terminal Secondary Base Station Secondary terminal 16 Fusion Centre
    17. 17. Cognitive radio is best suited for smaller cells such as WiFi access points and femtocells … but relaxed interference requirements to the primary user can increase cell size 25% co-location PN=90% 100% co-location rs=1.15 km Access rule: the interference generated to the primary system should correspond to an increase of the noise floor of less than 0.5 dB with a certain probability PN%. 17
    18. 18. Offloading the LTE network using Cognitive Femtocells aided by a sensor network 1) deploy cognitive femtocells 2) deploy sensors 3) increase power 18
    19. 19. We compare with the case of using conventional femtocells and additional base stations 1) deploy conventional femtocells 2) deploy macro base stations 19
    20. 20. Offloading LTE with cognitive femtocells can be more cost effective than using conventional femtocells and additional base stations The most business critical parameters for the cognitive femotcell: • cost for backhaul • number of users supported • coverage radius
    21. 21. Outline Dynamic spectrum access in primary OFDMA systems (Paper A) Sensor Network Aided Cognitive Radio Systems (Papers B - E) Performance of the first Cognitive Radio Standard IEEE 802.22 (Papers F - I) 21
    22. 22. We implemented a detailed simulator to evaluate performance of the first standard for Cognitive Radio, IEEE 802.22 System Model It provides fixed wireless broadband in rural areas It uses two-stage spectrum sensing NO detection NO detection YES Coarse sensing stage detection Fine sensing stage (tc=1ms, at end of frame) (ts=30ms) 22 YES detection Switch channel
    23. 23. Performance for different sensing strategies should be considered dependent on required Quality of Service (QoS) Scenario 3 users receiving Video with Best Effort QoS profile 1 user receiving Voice over IP (VoIP) with Guaranteed Bit Rate QoS profile 23
    24. 24. Spectrum selection (SSE) functions that utilize sensing results to provide long term statistics can increase performance SSE-OnOff: selects the channel with highest probability of being available. SSE-Distance: selects the channel with shortest distance to WMs. SSE-Hybrid: uses the optimal of SSE-Distance and SSE-OnOff depending on distance to WMs. 24
    25. 25. In conclusion, a mobile operator can use Cognitive Radio to achieve well performing technical and economic viable solutions There is a potential to utilize white spaces in primary OFDMA networks, but cooperation with the primary is important. Operators can get access to more spectrum, increase capacity and reduce costs significantly by using sensor network aided cognitive radio systems. Spectrum selection functions that utilize sensing result statistics to predict primary user behavior can increase performance in IEEE 802.22. 25 Pål Grønsund (Pal.Gronsund@telenor.com)

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