Maximizing Cognitive Radio & Interference Tolerance: Spectrum Principles

1. 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 University of Southern California Viterbi School of Engineering Information Sciences Institute pmarshall @isi.edu

2. 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

3. 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

4. Objectives of Closed Form Expression of Spectrum Enable Cognitive Radio and Dynamic Spectrum Access Researchers to: • Simulate a wider range of spectrum environments than can be sampled and analyzed; • Perform analysis of radio performance, without researchers having large databases of environments; and • Provide provable assertions about cognitive radio performance in a range of potential environments. Examined from Two Perspectives: • Low Signal Levels and Fixed Bandwidths for Signaling Channels • High Energy, Proportional to Frequency Bandwidths for Effects on Front End Linearity

5. 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 A Total of 52,436 MATLAB Files and 1,073 MB of Data

6. Monotonic Estimator • Two Indices: • IDensity Mean Signal Level of the Median Energy • IIntensity Range from Weakness to Strongest Signal (25kHz) Intended to Provide a Mechanism to Synthesize Spectrum Distributions for Arbitrary Environments • Like Chicago, just … 1 MHz Used for Indices

7. 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

8. RF Environment Energy Management Key to Robust Operation and Affordability INPUT SIGNALS • 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 • 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 LNA OUTPUT 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

9. 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 k1 = 3.25, k2 = 11.8 where IMD3, IIP3 and Pin are in dBm Only 1 in 10-4 points shown

10. 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 With Reasonable Filter (<20% bandpass) there is Essentially Zero Chance of Noise Floor Elevation Probability Distribution of Intermodulation Induced Noise Floor Elevation when using Pick Quietest Band First Algorithm

11. 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%) Non-Cognitive Noise Floor Reduction at the Same IIP3 Level Cognitive Lower Intermodulation Noise Floor and Major Reduction in Required Linearity

12. 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

13. 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

14. Interference Tolerance Requires We “Break Up” Network into Small, Interconnected Sub-Networks Today’s Mesh or MANET Multi-Frequency Network Color Depicts sub-net Frequencies MIMO Mode Not Depicted 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 • 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

15. Dynamic Adaption as Enabler of Dynamic Networks Each Technology Can Throw “Tough” Situations to other More Suitable Technologies without Impact on User QOS Topology Planning Re-plan Topology Re-plan Across Network Spectrum Planning Network-Wide MIMO Spectrum Too Tight No Good MIMO Paths Dynamic Spectrum Need More Range Relocate Around Spur Radio Device Unavoidable Strong Signal Move to New Preselector Band Device Spurs, … Beam Forming Nulling Strong Neighbor Signal Link

16. Interference Avoidance vs.Interference Management Interference Tolerance and Management Interference Avoidance (Evolving Dynamic Spectrum Access) Delay Tolerant Applications Application Delay Tolerant Transfers Presentation Session Multiple Routes Available Transport Manage Collision Events Network MIMO for Nulling Link Avoid Interference Sense and Balance Interference Physical Interference Tolerance Essential to Maximize Network Capacity

17. 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 Exponent Modeled as in Anderson • Index of DSA Performance (IDSA): • (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 Maximum Aggregate Throughput Occurs at High Interference Rate Probability of Interference vs. Density Aggregate Throughput vs. Density Interference Tolerant Operating Point Throughput Benefit in Moving from Manual to Maximal Aggregate Throughput Operating Points Typical Manual De-confliction Density Benefit in Moving from Manual to Maximal Aggregate Throughput Operating Points

18. 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

19. Published Papers “Extending the Reach of Cognitive Radio,” Proceedings of the IEEE, Vol. 97, No. 4, pp. 612-625, Apr. 2009. • 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 “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.

20. 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.

21. 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 University of Southern California Viterbi School of Engineering Information Sciences Institute pmarshall @isi.edu