Dynamic Spectrum Access (DSA) Wireless Networking

R. Chandramouli (Mouli)

Thomas E. Hattrick Chair Professor

Department of ECE

Stevens Institute of Technology

Spectrum Regulatory Models

• Command and Control (traditional model) – Allowable spectrum use is limited by the regulatory

policy – Only licensed users are allowed to use the spectrum

• Commons Model Unlicensed secondary users can share spectrum subject to spectrum etiquettes – No guarantees on protection from interference

• Exclusive Use Model – Market-driven model – Spectrum license holder (“primary user”) can sublease

unused spectrum to a non-licensed user (“secondary user”) in time and space

– Sublease can be short term to long term – Predicted to be 70% in the near-future

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Is Spectrum Scarce?

Spectrum measurement (54-88MHz) in NY City shows “white spaces” or unused spectral bands

Unused spectrum “white space”

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Related Worldwide Regulatory Activities

• FCC

– Unlicensed operations in T.V. white space

• Second Report and Order and Memorandum Opinion and Order, 23 FCC Rcd 16807, Nov. 2008

• Second Memorandum Opinion and Order, FCC 10-174, Sep. 2010

• Ofcom (UK)

– T.V. white spaces • Digital dividend: Cognitive access, statement—Consulatation on

lincense-exempting cognitive devices using inter-leaved spectrum, Feb. 2009

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Related Worldwide Regulatory Activities

• FCC

– ET Docket No. 10-237 (Nov. 30, 2010), NOI • Promoting more efficient use of spectrum through dynamic

Spectrum use technologies

– Incentives for dynamic spectrum use?

– Create test beds or change policies for DSA in licensed and unlicensed bands?

– Is spectrum sensing a viable technology for some bands?

– ET Docket No. 10-236 (Nov. 30, 2010), NPRM • Comments on expanding Experimental Radio Service rules to

promote research

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Economics of DSA

• 85%-95% of spectrum under 3GHz is under-used (several spectrum measurement studies)

• Transition to digital TV transmission opens up prime spectrum for opportunistic use – Fewer households rely on over-the-air TV – $10 Billion/year market opportunity in TV white space

DSA+WiFi – Useful for long range wireless networks – Spectrum Bridge’s ShowMyWhiteSpace

• Low cost inter-operable first responder communications

• Co-existence among heterogeneous wireless networks (dynamic spectrum sharing/access)

• … 6

Use Case: Emergency Interoperable First Responder Multi-band DSA Network

WiFi 4.9GHz 3G LTE

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Two Basic Ideas in DSA

• Secondary use of licensed spectrum – Primary user gets highest priority – When primary user is not using the spectrum how can

secondary detect it and opportunistically use it? – Secondary must leave the spectrum as soon as primary

user transmission begins in order to protect primary from interference

• Unlicensed spectrum (“open spectrum”) – All the users that have similar rights to spectrum – How can they detect each other’s transmission to

peacefully co-exist?

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Example : Dynamic Frequency Selection in WiFi Channels 1,6,11

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Protocol Stack Issues for DSA

• PHY layer

– Spectrum sensing to detect white spaces, primary user, and interference

• Detection delay (e.g., more sampling) vs. accuracy trade-off

• Detecting low SNR signals (e.g. -107dbm for wireless microphones)

– Channel bonding and fragmentation • Bond adjacent channels to obtain higher bandwidth

• Fragment a wideband channel into smaller channels

• MAC layer

– Spectrum aggregation • Aggregate non-contiguous channels for higher bandwidth

– Spectrum etiquettes • No zero-rate transmission; listen before talk, …

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Protocol Stack Issues for DSA • IP layer

– Maintain IP connectivity during dynamic frequency or network switching operating in different bands

• Application layer

– Learn application traffic statistics and adapt

– Support for video streaming, VoIP etc.

– Robustness against uncertainties in spectrum availability

• Policy layer

– How to represent spectrum and usage policies?

– Policy language

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Spectrum Sensing

• Sense time-varying unused spectrum – Energy detection: simple but not very reliable

– Cyclostationary detection: complex but reliable

• Requires network wide quiet periods

• Collaborative sensing – Distributed spectrum sensors detect white spaces

– Sensor decision fusion for final decision

• Wideband accurate sensing incurs delay cost

• Narrow band sensing is faster

• IEEE 802.22: coarse wideband sensing and fine narrowband sensing

• Probability of detection (90%), false alarm (10%), detection time (2s) and time to vacate channel (2s)

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Soekris Engineering net5501

500 MHz AMD Geode LX CPU,

512 MB DDR-SDRAM,

4 VIA 10/100Mb Ethernet Port

2 Serial,

USB connector,

CF socket,

44 pins IDE connector,

SATA connector,

1 Mini-PCI socket,

3.3V PCI connector.

Operating System

Ubuntu 8.04

Modified open source

MadWifi drivers for

cognition enabled DSA

SpiderRadio: DSA Radio Prototype 4.9GHz public

safety band 5GHz WiFi

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Exploit WNIC for Sensing? • Commercial WNICs output observed PHY errors

(comes free) – Treat WNIC as a blackbox

– PHY errors reported by WNIC when packets/signal without the intended PHY preamble is observed

• When primary user is present and transmitting – Secondary user radios present in the channel observe packets due to

different packet preamble or corrupted packet preamble (known as observed PHY errors)

– Exploit this to sense primary user transmission

• Advantage: unlike energy detection, the DSA radios need not forcefully quiet down periodically to observe/detect PHY errors

– Many practical optimization, algorithmic and implementation challenges

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Normal WiFi Performance under Interference

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SpiderRadio DSA WiFi under Interference

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Operational Capabilities

Average Synchronization Time

Sensing 10 – 50ms, depend on precision requirement

Synchronization 4 – 18ms, depend on Network traffic congestion

Channel Switching 0.5 – 1.5ms

Channel bonding/ fragmentation

0.5 – 1ms

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MAC Protocol Issues

• Multi-channel MAC : DSA radios may

operate on different channels

• Dynamic channel bonding of contiguous channels

and aggregation of non-contiguous channels

• Spectrum information distribution for MAC – Control channel based MAC

– Spectrum database based MAC

• Control channel incurs significant overhead

• Spectrum databases have to be updated constantly

• QoS guarantees very challenging

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MAC Issues

• Avoid spectrum starvation (e.g., mix of broadband and narrowband users)

• Spectrum packing

• Channel bonding, fragmentation, aggregation

• Multi-MAC in a radio equipped with multiple PHY layers

• Synchronizing Tx and Rx after channel switching

• Co-existence of legacy radios and DSA radios

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Channel Bonding and Fragmentation

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Application Layer

• Application layer QoS may suffer

• Example: – TCP application may continue to transmit while the

physical layer tries to switch to another band

– Physical link is lost during switching band

• Erasure codes – Lost packets are erasures during channel switching

– Digital fountain codes for erasure correction

– Application layer bonding – Decide optimal channel to object mapping

– E.g., from a web page, send videos on a wideband channel and text on a narrowband channel

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Medical Image Transmission

Normal WiFi under channel interference

SpiderRadio under channel interference – sense and switch

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Modeling and Simulation Issues

• Lack of reliable modeling and simulation tools for DSA networks

• Few DSA network pilots and large scale field tests

• Data driven modeling and simulation of interaction from PHY to policy layer

– E.g., traffic in DSA networks : i.i.d., short-term memory, long term correlations?

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Multi-radio DSA

• Devices are equipped with multiple radios • E.g., 3G and WiFi

• Current DSA technologies allow a device to connect to only one wireless network at a given time • Leads to wastage of

spectrum resources, frequent connection loss, no support for inter-operability across networks, etc.

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Multi-radio DSA

• SpiderRadio prototype (multi-network aggregation)

• Enables a device to connect to multiple wireless networks simultaneously for increased reliability, data rate, security, etc.

• Uses standard WNICs

• Dynamic access to different wireless networks, different channels in a wireless network, aggregate channels across networks, etc.

• Network level sensing for DSA

• SINR

• Traffic congestion

• Security

• Cost (e.g., free WiFi vs. 3G access)

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Multi-radio Multi-network Aggregation

Courtesy: Google images

One virtual aggregated broadband wireless network

LTE

WiFi

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4.9GHz

Internet

Cloud

IP Layer Network Aggregation with Channel Bonding/Fragmentation

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DSA Interference Mitigation in Aggregated Network

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Multi-Network Aggregation Performance:

Two WiFi

2.6Mbps

without

aggregation

5Mbps

with two

WiFi network

aggregation

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Security Issue Example

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• Bonded 5.24 and

5.26 GHz channels

• Significant leakage

into other channels

• How can this be

analyzed for service

disruption attacks?

Two Types of Attacks

• Maximum impact attack (MAXIMP)

– Attacker tries to maximize average power leakage in each fragment

– Constraint on maximum power

– Reduces the channel capacity for the users

• Use minimum power (MINPOW)

– Attacker uses minimum power to create at least a certain level of leakage in each fragment

– Reduces the signal-to-interference-noise ratio (SINR), which, in turn, reduces throughput

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Numerical Results: MAXIMP

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IEEE 802.22 networks with N=3 Channels, K=3

fragments each, i.e., NK=9

Can reduce

capacity by

16% (almost

100 Kbps)

Other Challenges

• Cross-layer optimization

– How can information about the application, network and channel be used together to jointly optimize the DSA network?

• Soft-handoff capabilities

– Sensing based dynamic load balancing between the multiple bonded/aggregated wireless networks

• Underlay transmission for dynamic spectrum access

• Channel fragmentation to minimize need to move to other channels within a network

– Minimizes delay cost due to channel hopping

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Research Challenges

• DSA aggregation of 3G/4G LTE, 4.9GHz, 900MHz and WiFi

• Support for simultaneous VoIP and video streaming over DSA networks

• Security features such as VPN (virtual private network)

• Support for robust DSA connectivity in mobile networks

• Low cost platform

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Paper downloads from: http://www.ece.stevens-tech.edu/~mouli

mouli@stevens.edu