Transcript A Generalized Method for Quantification of Cognitive Radio

Quantifying Aspects of Cognitive
Radio and Dynamic Spectrum Access
Performance; and Interference
Tolerance as a Spectrum Principle
Preston Marshall
University of Southern California
Viterbi School of Engineering
Information Sciences Institute
pmarshall @isi.edu
Centre for Telecommunications Value Chain Research,
Electrical Engineering Department
Trinity College, Dublin, Ireland
pmarshal @tcd.ie
Presentation Topic
• General Trend to View DSA as:
– Of Benefit to Unlicensed, Secondary Users of Spectrum
– Not Particularly Beneficial to Primary Users Already Provisioned
with Spectrum
• Present Alternative Vision
– DSA is Highly Beneficial to Environmentally Stressed Devices
– Existing “Mission Critical” Primary Users Could Most Benefit from
DSA, Even if they Have Adequate Spectrum Access
– Interference Tolerance Can Be More Effective Than Interference
Avoidance
• Implication:
– Instead of Relocating Existing Services, We Could Provide Mutual
Benefit By Transitioning Them to DSA
– Applicability:
– Emerging Self-Forming Networks, Many Hub-Spoke Systems
Instead of “Interference Avoiding” DSA, Transition to
DSA-Enabled Interference-Tolerance
Agenda
• A Model for Spectrum Density and Energy
• Front End Overload and Non-Linearity Issues:
– Reliability Issues with Fixed Spectrum Assignments
– Improvement in Likely Front End Performance with DSA
– Reduction in Required Linearity for Equivalent Performance
• What Density an Be Achieved if a Device Can Assume
Other Devices are Interference Tolerant?
– The Impact of DSA + Propagation Exponent Awareness
– Selection of Optimal Constellation Depth
• How Can Topology Management Enable Density?
• Fungibility of Benefits
• Implications on Spectrum Management Policy
Objectives of Closed Form
Expression of Spectrum
Enable Cognitive Radio and Dynamic Spectrum
Access Researchers to:
1. Simulate a wider range of spectrum environments
than can be sampled and analyzed;
2. Perform analysis of radio performance, without
researchers having large databases of environments;
and
3. Provide provable assertions about cognitive radio
performance in a range of potential environments.
Examined from Two Perspectives:
1. Low Signal Levels and Fixed Bandwidths for
Signaling Channels
2. High Energy, Proportional to Frequency Bandwidths
for Effects on Front End Linearity
Spectrum Analysis
Methodology
• Used Six NSF Spectrum Measurements Reported by McHenry
(Shared Spectrum and IIT)
– All Had Consistent Methodology, Instrumentation and Reporting
• Developed Closed-Form Cumulative Distributions for the Signaling
(Fixed b0) and Pre-Selector (BW) Bandwidths
• Developed Estimators to Synthesize Arbitrary Environments in
Terms of Density and Intensity Variables
• Bandwidth Treated as Independent to Recognize Correlation
Between Adjacent Frequencies
Sample
Chicago
Location
Illinois Institute of Technology, Chicago, IL
Date(s)
November 16 to 18, 2005
Riverbend Riverbend Park, Great Falls, Virginia
April 7, 2004
Tysons
Tysons Square Center, Vienna, Virginia
April 9, 2004
New
York
Republican National Convention, New York
City, New York (Day 1 and Day 2)
August 30, 2004 September 2, 2004
NRAO
National Radio Astronomy Observatory
(NRAO), Green Bank, West Virginia
October 10 -11, 2004
Vienna
Shared Spectrum Building Roof, Vienna,
Virginia
Dec. 15-16, 2004
A Total of 52,436 MATLAB Files and 1,073 MB of Data
Monotonic Estimator
Intended to Provide a
Mechanism to Synthesize
Spectrum Distributions for
Arbitrary Environments
– Like Chicago, just …
1 MHz Used for Indices
Two Indices:
IDensity
IIntensity
Mean Signal Level of the
Median Energy
Range from Weakness
to Strongest Signal
(25kHz)
Importance of Front End
Energy Effects
• The Last Slides Show that High Energy Signals Are Rare in terms of
Frequencies Containing them, but Common in Terms of
Frequencies Impacted
– A High Power 100 kHz Signal may impact only 4 of 10,000s of possible
25 kHz Channels, but
– It can Dominate the Energy in 20% of the Pre-Selector Settings
• All these Frequencies May be Unusable, Even through they are
“White Space” Due to the Effect of Limited Receiver Dynamic Range
– AGC No Help, since this is Adjacent Channel
• Looking at Spectrum Occupancy Alone Does Not Paint a Sufficient
Picture of the Interaction of a Cognitive Radio and its Environment
• DSA Bands Are More Likely to Stress Linearity than Current
Allocations as We Go Beyond the Wi-Fi Bands!
– No Longer Segregated with Low Power Sources
– Sharing Bands with High Power Sources, Like Broadcast
– 10 Times More Density → 10 dB Increase in Energy → 30 dB Increase in
3rd Order Intermodulation
• Reduced RF Performance of Low-Dynamic Range CMOS RF
Circuits and Digital Filters
RF Environment Energy Management
Key to Robust Operation and
Affordability
• Even Open Frequencies Not Usable in
High-Energy RF Environments, ex.
Co-Site
– Frequencies can be “Perfectly
Assigned”, but RF Cannot Deal with
Energy Density
– Even Ultra-High Quality Front Ends,
Experience 20+ dB Increase in Noise
due to Inter-Modulation
• “Better” Frequency Management not
an Answer
INPUT SIGNALS
LNA OUTPUT
– Intractable Problem for Centralized
Management
• “Better” Technology not an Answer
– Can Not Throw Linearity at the Problem
– Energy Costs of High Linearity
Unacceptable in Battery Devices
Example is input power = IIP3
More “Nextel-Public Safety” interactions due to Non-Linear Effects
(Co-Site) Make Frequency Management Inadequate in Some Dense
Spectrum
Mapping Input Energy to IMD
Noise Energy
• Analysis of over 90 Million Spectrum Measurements
yield expected relationship of Input and Output Energy
• Order is 3.25, Reflecting Higher Degree of Correlation
at Upper Energy Range
• Mean 11 dB Below Pure Two Tone IMD Product
IMD3 = k1 Pin - 2 IIP3 -k2
k1 = 3.25, k2 = 11.8
where IMD3, IIP3 and Pin
are in dBm
Only 1 in 10-4 points shown
Noise Floor Elevation
Non-Cognitive Radio Has
Significant 3rd Order
Intermodulation Noise
Elevation, Even for High
Performance Filters
Non-Cognitive Radio Noise Floor Elevation
for IIP3 = -5 dBm in Chicago Spectrum
Cognitive Radio, Even with
Poor Filters, Has Very Low
Noise Elevation
Probability Distribution of Intermodulation
Induced Noise Floor Elevation when using Pick
Quietest Band First Algorithm
With Reasonable Filter (<20%
bandpass) there is
Essentially Zero Chance of
Noise Floor Elevation
Comparison of IMD3 Noise for a
Range of IIP3 Points (90% Case)
Cognitive Radio (ideally) Enables a 30 dB Reduction in
Required IIP3 Performance, and Creates a Lower Noise
Floor Simultaneously, even for Moderate Filter
Selectivity (20%)
Noise Floor
Reduction at the
Same IIP3 Level
Non-Cognitive
Cognitive
Lower Intermodulation
Noise Floor and Major
Reduction in Required
Linearity
Benefits are a Function of
Required Reliability
• The Benefits of Front End Loading Adaptation is Driven by the Environment
and the Level of Reliability
• As Reliability Needs Increase, the Benefits of Adaptation Increase
Accordingly
• Intensity Can Be Handled, But At Extreme Values of Density, Even
Cognitive Adaption Has Constraints on Performance Enhancement
– Not Surprising, a Few Strong Signals are OK, but Many Strong Signals Have a Chance of
Hitting all Pre-Selector Candidates
• Note that if DSA Succeeds, Most RF Environments Will Become Denser,
and More Like the Urban Environments
90% Environments
99% Environments
Front End Performance
Conclusions
• Linearity and Filtering Are Major Cost Drivers in
Reasonable or Better Performing Wireless Devices
• Integration of Dynamic Spectrum and Cognitive Radio
Offers a Unique Opportunity to Address one of the
Critical Analog Circuit Limitation in Wireless Systems
– Significant Anecdotal Evidence of Severity
– Will Become More Significant as Density Increases
• Offers Designers Opportunity to Both Significantly
Increase Reliability and Performance and Reduce High
Analog Performance Requirements
• New Business Case for DSA: It Can Be Less Expensive
than a non-DSA Device of Equivalent Performance
Interference Tolerance Requires We
“Break Up” Network into Small,
Interconnected Sub-Networks
Today’s Mesh or MANET
Multi-Frequency Network
Color Depicts all radios on
the same frequency
• Low Reliability Due to Single Link Routes
• All Radios Interfere with Each Other,
Even if they can not Communicate
• Bandwidth Drops as More Radios Added
to Network
• Bandwidth Constrained by Mutual
Interference – More Nodes do Not Create
More Capacity
• Large Number of Nodes on Single
Frequencies
Color Depicts sub-net
Frequencies
MIMO Mode Not
Depicted
• Multiple Links and Routes Provide High
Reliability
• No Single sub-Network is Large Enough
to Have Scaling Issues
• More Sub-Networks are Created as More
Nodes Join the Overall Network
• Bandwidth Increases as More Radios
Added to Network
• Diversity in Frequency Avoids
Interference
A Fundamentally New Approach to Network Organization
Was Needed to Ensure Scalability
Dynamic Adaption as Enabler
of Dynamic Networks
Topology
Planning
Re-plan
Topology
Spectrum
Planning
Network-Wide
Each Technology Can Throw
“Tough” Situations to other
More Suitable Technologies
without Impact on User QOS
Re-plan
Across
Network
Spectrum
Too Tight
MIMO
No Good
MIMO Need
Dynamic
Spectrum
Paths More
Relocate
Radio Device
Range
Around
Move to New
Spur
Preselector
Band
Device
Beam
Spurs, …
Link
Strong
Neighbor
Signal
Forming
Unavoidable
Strong
Signal
Nulling
Interference Avoidance vs.
Interference Management
Interference Avoidance
(Evolving Dynamic
Spectrum Access)
Interference Tolerance
and Management
Application
Presentation
Session
Delay Tolerant
Applications
Delay Tolerant
Transfers
Multiple Routes
Available
Transport
Network
Link
Avoid
Interference
Physical
Manage Collision
Events
MIMO for Nulling
Sense and Balance
Interference
Interference Tolerance Essential to
Maximize Network Capacity
Network Interference Tolerance vs.
Node Interference Avoidance
•
•
•
•
We Imagine A Mobile Operating Area
When Interfered with, Nodes Respond by Relocating
Closed Form, with Probability Distribution of Propagation
as in Anderson
Index of DSA Performance (IDSA):
–
–
–
•
•
•
•
Exponent Modeled
(Event Time+ Relocation Time)/Event Time
Used Worse Case: Each Sensing Event is Independent
Used Reported XG Performance for an IDSA of 1.75 (100 ms Sensing, 175 ms relocation)
Optimal Interference Rate is Orders of Magnitude Higher than Typically “Acceptable”
Resulting Aggregate Throughout is Orders of Magnitude More
Increase in Density Is More than Results from Just “Finding” Open Spectrum
Conclusion: Interference is Best Solved as a Network Issue, Not a Link Issue
Probability of Interference vs. Density
Maximum Aggregate
Throughput Occurs at
High Interference Rate
Aggregate Throughput vs. Density
Interference
Tolerant
Operating
Point
Typical Manual
De-confliction
Throughput Benefit in
Moving from Manual
to Maximal Aggregate
Throughput Operating
Points
Density Benefit in Moving from
Manual to Maximal Aggregate
Throughput Operating Points
α-Aware, Optimal Bits/Hertz
• Optimal Spectrum Usage Does Not Occur With Maximal Bits/Hertz –
WHEN SPECTRUM RE-USE IS INCLUDED IN CONSIDERATION!
• Optimal Modulation Depth is a Function of the Propagation Exponent –
Situational, rather than Specifiable
• Cognitive Radio Can Increase Density of Usage by Factor of 5, or More,
if it Adjusts Modulation Based on Actual Propagation (But Uses More Hz)
Optimal Bits/Hertz is a
Function of Propagation α
Bits/Area vs. Bits/Hertz
Showing Bits * Area with More than 3 dB
Interference vs. the Bits/Hertz for a Range
of Propagation Constants (α)
The root of the derivative of SIE
((bits/Hertz)/ Area) ratio yields
the optimal operating point
Consistent
Reference Point
is 1 Bit/Hertz
Published Papers
Summary of Generalized Cognitive
Radio Functionality
Cognitive Radio Environments
Front-end Linearity Management
Minimization of Interference
Effects through
Interference Tolerant
DSA Mechanisms
Spectral Footprint
Management
Extension of Principles to Network
Level Decision Making
Overall
“Extending the Reach of Cognitive Radio,” Proceedings of the IEEE,
Vol. 97, No. 4, pp. 612-625, Apr. 2009.
“Closed-Form Analysis of Spectrum Characteristics for Cognitive
Radio Performance Analysis,” in 3rd IEEE International Symposium on
New Frontiers in Dynamic Spectrum Access Networks, 2008.
“Dynamic Spectrum Management of Front End Linearity and Dynamic
Range,” in 3rd IEEE International Symposium on New Frontiers in
Dynamic Spectrum Access Networks, 2008.
“Cognitive Radio as a Mechanism to Manage Front-End Linearity and
Dynamic Range,” IEEE Communications Magazine, Mar. 2009.
“Spectrum Awareness and Access Considerations,” in Cognitive Radio
Technology, 2nd Edition, B. Fette, Ed. Academic Press, 2009.
“From Self-Forming Mobile Networks to Self-Forming Content
Networks,” in Association of Computing Machinery Mobile
Communications Conference, Sept. 2008.
“Progress towards Affordable, Dense, and Content Focused Tactical
Edge Networks, in 2008 IEEE Military Communications Conference,
2008.
“Recent Progress in Moving Cognitive Radio and Services to
Deployment,” in 9th IEEE International Symposium on a World of
Wireless, Mobile and Multimedia Networks, June 2008.
Spectrum Policy Implications
• DSA is Highly Advantageous, Even if You “Own”
Spectrum
• Current “Relocation” Approach Fails to Recognize
Advantages of DSA to Incumbent Users
• New Concepts Possible
• Instead of Relocation” Trust; Have “Interference
Tolerance Trust”
– Fund Transition to Interference Tolerant Systems by Current
Primary User
– Enable Secondary Use of DSA, Subject to Aggregate Loading
which Impacts Primary’s Performance
– Primary and Secondary Benefit!
– No Need to Change “Ownership”
– There is a Win-Win Available (for Primary Users that Can Create
Interference Tolerant Modes)
P. Marshall, “A Potential Alliance for World-Wide Dynamic Spectrum Access: DSA as an Enabler of
National Dynamic Spectrum Management”, New America Foundation Issue Paper #25, June 2009.
Questions?
Preston Marshall
University of Southern California
Viterbi School of Engineering
Information Sciences Institute
pmarshall @isi.edu
Centre for Telecommunications Value Chain Research,
Electrical Engineering Department
Trinity College, Dublin, Ireland
pmarshal @tcd.ie