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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Information about Cognitive Radio from a Mobile Operator's Perspective: System Performance...
Technology

Published on November 12, 2013

Author: paalrg

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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

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)

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

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 accordingly 4

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

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

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

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 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

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

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

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

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 Business case II Spectrum broker Business case I Joint venture “Spectrum Sharing” New entrant New entrant Business case III 14 New entrant

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

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

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

Offloading the LTE network using Cognitive Femtocells aided by a sensor network 1) deploy cognitive femtocells 2) deploy sensors 3) increase power 18

We compare with the case of using conventional femtocells and additional base stations 1) deploy conventional femtocells 2) deploy macro base stations 19

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

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

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

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

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

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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