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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
2
Is Spectrum Scarce?
Spectrum measurement (54-88MHz) in NY City shows “white spaces” or unused spectral bands
Unused spectrum “white space”
3
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
4
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
5
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
7
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?
8
Example : Dynamic Frequency Selection in WiFi Channels 1,6,11
9
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, …
10
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
11
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)
12
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
13
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
14
Normal WiFi Performance under Interference
15
SpiderRadio DSA WiFi under Interference
16
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
17
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
18
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
19
Channel Bonding and Fragmentation
20
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
21
Medical Image Transmission
Normal WiFi under channel interference
SpiderRadio under channel interference – sense and switch
22
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?
23
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.
24
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)
25
Multi-radio Multi-network Aggregation
Courtesy: Google images
One virtual aggregated broadband wireless network
LTE
WiFi
26
4.9GHz
Internet
Cloud
IP Layer Network Aggregation with Channel Bonding/Fragmentation
27
DSA Interference Mitigation in Aggregated Network
28
Multi-Network Aggregation Performance:
Two WiFi
2.6Mbps
without
aggregation
5Mbps
with two
WiFi network
aggregation
29
Security Issue Example
30
• 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
31
Numerical Results: MAXIMP
32
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
33
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
34
Paper downloads from: http://www.ece.stevens-tech.edu/~mouli