Understanding the Issues in Software Defined Cognitive Radios

1. Understanding the Issues in Software Defined Cognitive Radios Jeffrey H. Reed Director, Wireless @ Virginia Tech Bradley Dept. of Electrical and Computer Engineering reedjh@vt.edu (540) 231 2972

2. Faculty Doing Cognitive Radio Research Jeff Reed (Director, W@VT) Software Radio Claudio da Silva Spectrum Sensing Allen MacKenzie Cognitive Networks Jung-Min Park Security Tamal Bose (Assoc. Director, W@VT) Digital Signal Processing Luiz DaSilva Cognitive Networks Sanjay Raman RF Design Charles Bostian RF Design, Cognitive Engine Tom Hou Resource Management Yaling Yang Routing Mike Buehrer Cognitive MIMO Michael Hsiao Software Verification • Research faculty: Carl Dietrich, Kyung Bae • Students: approximately 40 graduate students

3. Army Research Office BBN Bradley Fellowships CAER DARPA DISA ETRI ICTAS Sponsors of Cognitive Radio Research • Intel • Motorola • National Institute of Justice • National Science Foundation • ONR • Rockwell • - Tektronix • - Texas Instruments

4. Outline • Goal: Give a broad overview of research issues in Cognitive Radio and some of VT’s research in these areas • CR Development -- Selected Materials from these research projects • Cognitive engine • Spectrum sensing • Cognitive networks • Game theory • Dynamic spectrum allocation • Dynamic spectrum sharing • Verification • Security • Cognitive PA control • Radio environment maps • Cognitive routing • Cognitive MIMO • Experimental CR Deployment • - VT Public Safety Cognitive Radio • Cognitive radio network test bed • Lessons Learned See the April 2009 Proceedings of the IEEE for an overview of VT’s cognitive wireless networking efforts

5. Cognitive Radio Concept Cognitive radios are flexible and intelligent radios that are capable of… … and can be realized as a cognitive engine (intelligent software package) controlling a software defined radio platform.

6. Spectrum Management Advanced Networking for QoS Power Consumption Reduction Femto cells and spectrum management Cognitive MIMO, e.g, learning the best spatial modes Cellular Radio Resource Management Maintenance and Fault Detection of Networks Public Safety Interoperatiliby Emergency Rapid Deployment and Plug-and-Play optimization Multibanding, e.g., mixing licensed and unlicensed spectrum or protected and unprotected Cognitive Routing and prioritization Enhanced security Anticipating user needs – intersystem handoff and network resource allocation Smart Antenna management Location dependent regulations Collaborative Radio – Coverage and capacity extensions Social networking support -- context sensitive connections Revolutionary Applications

7. Cognitive Engine Provides radio platform-independent distributed cognitive control of a software defined radio. Internal components include signal recognition and synchronization, link optimization, and dynamic spectral allocation. United States Patent Published Application 20060009209 Cognitive Radio Engine Based on Genetic Algorithms in a Network Faculty: Bostian, MacKenzie

8. VT Open Source Cognitive Radio System • Primary goals of this effort are: • Identify the key components in a Cognitive Radio System • Define the information exchange between core components in a Cognitive Radio System (APIs) • Provide a reference implementation! • Socket based communication between components • Components can be developed in many different programming languages • Optimization can be done remotely from the actual radio hardware. • Available by June 2009. http://www.wireless.vt.edu

9. VT Cognitive Radio System

10. VT Open Source Cognitive Radio System Details • Complete system consists of multiple mandatory components • Shell • Typically co-located with radio hardware • Routes messages between other components and interfaces • Cognitive Engine • Performs the low level optimization of parameters • Multiple engines can be connected at the same time • Hardware Front End • Radio Digital and RF hardware layer

11. VT Open Source Cognitive Radio System Details • Optional System Components • Policy Engine (optional) • Validates output of cognitive engine against pre-loaded policies • Service Management Layer (optional) • Manages available services based upon the current “mission” • Example: Signal classification could be done many ways. SML determines which method to use based upon current “mission”.

12. VT Open Source Cognitive Radio System Details • Reference implementation • Multiple Case-based reasoning Cognitive Engines • Sector-based pre-processing Policy Engine • Compatible with standard xG policies • USRP2 Digital and RF Front End • Custom USRP2 daugherboards • Freq Range: 100 MHz – 4 GHz • 20 MHz instantaneous bandwidth • Flexibility enabled by the Motorola RFIC4

13. Spectrum Sensing Le and Bostian, Adaptive Signal Classification for Cognitive Radios covers an automatic system for classifying the modulation of and synchronizing (carrier and symbol rate) any analog or digital waveform without a-priori knowledge of the carrier frequency and symbol rate over a wide range of signal-to-noise ratios. Faculty: Bostian

14. Spectrum Sensing • Signal Detection and Classification Using Spectral Correlation and HMM • Hidden Markov Model (HHM): A convenient and analytically tractable tool to • describe the statistical behavior of complicated random time-series • - Reveals unique feature for input modulated signal in cyclic frequency domain Faculty: Reed

15. Distributed Spectrum Sensing • Distributed spectrum sensing: • Spectrum utilization is a spatial phenomenon • Take advantage of the radio signal variability • Tradeoff: number of sensors vs. radio complexity • Node-processing – cyclic analysis, wavelets • Information reduction/compression • Data fusion – message passing • Temporal/spatial association of sensing data Faculty: C. da Silva

16. Specific Emitter Identification (SEI) Faculty: Jeff Reed SEI utilizes emitter specific non-intentional emission (or radiated emission) Typical parameters such as time of arrival (TOA), angle of arrival (AOA), pulse repletion interval (PRI), and pulse width are not enough for the process of source identification New method exploits the cyclostaionarity of transmitted signal 16

17. Problem statement: What mechanisms can be used to support fair and distributed sharing of the available spectrum? One approach: Look for conditions under which the Nash Bargaining Solution is achievable Develop a distributed algorithm to reach the NBS Game-Theoretic Model of Spectrum Sharing The efficiency of the spectrum usage for the NBS increases with the number of channels, the algorithm converges in relatively few iterations and the size of the interference zone is about 2 hops. Faculty: L. DaSilva, MacKenzie, and Reed

18. Each radio wishes to minimize the interference its signal experiences A network of cognitive radios where each adaptation decreases the sum of each radio’s observed interference is an IRN Properties: Steady-state exists Network converges Good, though not necessarily optimal performance Dynamic Frequency Selection

19. Distributed Wireless Computing Technical Objective • Develop an architecture to support cooperative software radio and cognitive radio applications • Exploit remote reconfiguration and adaptive properties while balancing tradeoffs between latency, range, power, and energy Technical Approach • Analyze fundamental limitations and tradeoffs between RF and computational radio segments using theoretical models • Establish a practical way of monitoring and managing resources within the distributed radio architecture • Introduce network-based methods in a SDR prototype • Leverage COTS components and extend existing open software architectures for communication and heterogeneous network systems • Requires cognitive principles for resource allocation

20. Potential Applications of Distributed Wireless Computing Cluster of micro UAVs equipped with SDR for geolocation and distributed remote imaging • Image processing • Distributed sensing • Coordinated jamming • Geo-location • Secure systems • Cooperative MIMO • Direction finding Relaying Relaying Distributed signal detection and classification enabled through handheld SDRs

21. High Level Overview of Architecture Project Design Workflow Wireless Network Distributed Computing (WNDC) Framework • Based on required service constraints and current system state, WNDC engine optimizes use of resources across subsystems • Power management block applies power saving strategies and jointly optimizes power supply to subsystems • WNDC environment is monitored regularly

22. Theoretical Performance of Distributed Wireless Computing • Modeling power & energy aspects of the power, computation and communication subsystems • Develop generic high level models of computation and computation subsystems suited for system analysis • Validate models with experimental measurements • Modeling various power sources such as battery and solar power and study impact on system design • Models will help drive distributed architecture

23. Verification of Cognitive Radio • Implementation of cognitive & software radios can be error-prone • Attackers can take advantage of this vulnerability • Our solution: ensure the software radio never enters a region/space wherein illegal parameters are allowed • Verification of software radio: model-checking → Can software produce erroneous outcome • Current Breakthroughs: Orders of magnitude improvement over existing methods • Model Construction • Challenge:Software in general has a large or infinite number of states. • Solution: Automatic Abstraction • Model Verification • --Reachability analysis • -- Conformance checking • Goal: find a fast, smart traversal algorithm Faculty: Hsiao

24. Verification of Cognitive Radio • Abstraction: Reduce program as much as possible • Verification: The abstract model is checked against the specified property. • Validation: The abstract Cex is checked for the feasibility in the concrete software. • Refinement: Based on refutation, some constraints are restored so that the spurious Cex can be removed from the refined abstract model. • Current research efforts: • Fast Finite Transition Model construction of software • Effective encoding size computation to model the program under verification • Efficient formulation of property checking as a state space traversal & exploration problem • New, powerful model checking paradigm to handle large state spaces Faculty: Hsiao

25. Security Issues in Cognitive Radio Networks • Security is one of the major technical hurdles that must be overcome to fully realize the benefits of cognitive radio (CR) • Distinguishing attributes of CR systems/networks raise new security implications that have not been studied previously • Cooperative spectrum sensing • On-demand channel contention • Incumbent and self-coexistence mechanisms • Spectrum etiquette mechanisms • Exploits of CR systems by adversaries can have serious consequences • Interference to critical incumbent radio services • IEEE 802.22 Security Sublayer • Inspired by the Security Sublayer defined in 802.16 • Provides subscribers with confidentiality, authentication, and data integrity by applying cryptographic transforms to MPDUs (MAC protocol data units) Faculty: Park

26. Security Issues in Cognitive Radio Networks: Examples • Primary user emulation: By modifying a CR’s emission characteristics (frequency, power, modulation), an adversary may “emulate” an incumbent’s signal → Causes decrease in network capacity and degradation of network performance • Cooperative sensing in hostile environments: If a • subset of the CR terminals report false measurements • due to malicious or malfunctioning software, how would • this affect the final sensing decision? • Attacks that exploit self-coexistence mechanisms: • Self-coexistence mechanisms are needed to minimize • interference in overlapping CR net coverage areas → • Adversaries can launch attacks that exploit the • self-coexistence mechanisms • Unauthorized modification of radio software →Tamper resistance mechanisms need to be integrated w/radio software Faculty: Park

27. The performance of a CR network highly depends on how much information about the radio environment is available at each node. Radio Environment Map (REM) is proposed as An abstraction of real-world radio scenarios An integrated database that consists of multi-domain information of the radio environment, such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. Radio Environment Maps Faculty: C. da Silva, Reed

28. CR Framework for Energy Optimization • Idea: • Reconfigure radio to reduce power or energy consumption based on application QoS, radio environment, and radio capability

29. Major Sources of Power/Energy Consumption • RF power consumption: • Transmitter: power amplifier (PA). • Receiver: Low noise amplifier (LNA). • Processing power consumption: • Transmitter: DAC, baseband signal processing • Receiver: ADC, baseband signal processing. • Multiple sources need to be considered in radio power or energy model and optimization. • Focus on PA in the first step

30. Delay Insensitive Application • QoS requirement: • BER • Performance metric: • Energy saving • Selection criteria: • Baseline: conventional adaptive modulation (AM): Select transmission scheme minimizing radiated energy and calculate radio energy consumption • Level 1 cognitive transmission: Select transmission scheme minimizing radio energy consumption including implementation losses (PA) (Level 1 cognitive transmission) • Level 2 cognitive transmission: Joint optimize PA characteristics and transmission scheme to minimize energy consumption

31. Delay Insensitive Application – Performance Metric • Energy saving: metric showing improvement over conventional AM • , the energy consumption determined by adaptive modulation • , the energy consumption determined by cognitive transmission

32. Energy Saving – Level 1 Cognitive Transmission • Significant energy saving over classic AM is achieved (up to 75%) • The energy saving is reduced at further distances • Radiated power increases, so does PA efficiency • The maximum PA output power level limits the choice of cognitive transmission schemes • Converge to AM at d = 950m. • Energy saving result:

33. Transmission Scheme – Level 1 Cognitive Transmission • Blue: cognitive transmission • Red: adaptive modulation • Adaptive modulation: • Lowest possible modulation order (QPSK) and highest possible coding gain • Cognitive transmission: • Higher order modulation and coding rate and coding gain to make PA operate at favorable condition (higher efficiency). • Converge at d = 950 m (same as in energy saving).

34. Effective Bandwidth Efficiency – Level 1 Cognitive Transmission • Effective bandwidth efficiency improvement • Simultaneous energy saving and throughput improvement stem from the fact that minimizing energy consumption using adaptive modulation fails to consider PA efficiency characteristics and hence does not actually minimize system energy consumption. • Converge at d = 950 m (same as in energy saving).

35. PA Characteristics – Level 2 Cognitive Transmission • The PA efficiency is usually higher close to saturation region. • “Gear shifting PA”: • Adjust PA biasing according to the output signal envelope such that the PA operates closer to the saturation region • Dynamic biasing can be implemented at circuit level • Dynamic biasing through external control from cognitive engine (our assumption) • Two biasing levels are used in joint optimization.

36. Energy Saving – Level 2 Cognitive Transmission • Significant extra energy saving is achieved • The maximum output power is lower for the lower biasing scheme than for the higher biasing scheme. • The radiated power at further distances becomes larger and both schemes actually use the same PA (the PA with higher maximum output power) • Performance of level one and level two converge at a distance of 700 m

37. Multiple antennas have long been used at transmitter and receiver to Mitigate fading (diversity) Provide SNR gain (beam-steering) Increase spectral efficiency (spatial multiplexing) Mitigate interference (beam-forming) What is the best configuration for a given objective? It depends: channel knowledge, temporal correlation, spatial correlation, SNR,… Cognitive MIMO • Cognitive MIMO • Observe channel matrix, resulting performance of scheme • Use known relationships between channel and MIMO performance • Adapt modulation, coding, antenna configuration simultaneously • Learn optimal mapping between measurable metrics and MIMO/modulation/coding architecture • Network perspective: MIMO scheme impacts interference seen at other nodes • Proper use of MIMO can also help save power Faculty: Buehrer

38. In a network of autonomous, adaptive radios, do individual optimizations result in network-wide optimal performance? How much information about network conditions must independent radios have to make effective adaptations? Learning and reasoning are needed to manage complex cross-layer optimizations Cognitive Networks A cognitive network has a cognitive process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals! Faculty: L. DaSilva and MacKenzie

39. Objective: Understanding fundamental performance limits for future multi-hop cognitive radio networks Technical challenge: difference in available frequency bands at each node, uneven size in different frequency bands, coordination among the nodes in a distributed and heterogeneous environment Research scope: Develop new performance metrics for multi-hop CRs New models for interference, power control, scheduling, and routing Network performance limits and theoretical bounds Performance Limits for Cognitive Radio Networks Faculty: Hou

40. Cognitive radios have the freedom to choose how to divide the spectrum into channels Spectrum can be divided into orthogonal channels (OCs) or partially overlapping channels (POCs) Project objective: Answer the following questions: Which one is better? OCs or POCs? How large each slice should be? The correct answers depend on network types Example: POC is always better than OC in large networks (Fig 1) In small scale networks, orthogonal division of spectrum is a better choice (Fig 2) Ch One Ch Six Ch Eleven Network-aware Spectrum Division Figure 1 : capacity improvement ratio in large scale networks Figure 2 : capacity improvement ratio in small scale networks Faculty: Yang

41. Communication environment changes over time and may deviates from the original expectation Different communication environments have different requirements on routing Fundamental Rules for Cognitive Routing • Solution: Cognitive Routing • Dynamically switch the designs of routing components based on current environments • Challenge • The routing may not function properly due to incompatibility among the new designs of routing components. • Current research efforts: Fundamental theoretical guidelines for compatible routing designs that ensure • Consistent routing • If A decides to reach D through path <A,B,C,D>, then B should decide to reach D through <B,C,D> and C should decide to reach D through <C,D> • Optimal routing • Loop-free routing Faculty: Yang

42. The VT Public Safety Cognitive Radio • Recognize and configure for any P25 Phase 1 waveforms • Identify known networks • Interoperate with legacy networks • Provide a gateway between incompatible networks Waveforms: Capable of recognizing and configuring itself to operate with analog and digital FM plus BPSK, QPSK, and GMSK waveforms with data rates from 20 kbps up to at least 350 kbps. OFDM is in its over-the-air testing phase. First Generation Prototype Demonstrated April 24, 2007. The U.S. Navy is now building the Second-Generation Prototype Faculty: Bostian, MacKenzie, Hsiao

43. Virginia Tech Cognitive Radio Network Test Bed • - What kinds, or features, of CR are difficult enough that require a test bed? • - Defining test, measurement, and certification methodologies • - Refining policies and etiquettes • - Accumulating expertise • Based on USRP II platform: Reconfigurable protocol stack with GnuRadio and possibly other platforms • Uses experimental Motorola transceiver chip 100 MHz to 4GHz. • 48 nodes spread throughout ICTAS I building under construction: Some support for mobility, with subset of these radios mounted on tracks • Experiment set-up and configuration through a control room • Node architecture: Faculty: Bose, Buehrer, L. DaSilva, MacKenzie, Marathe, and Reed

44. Experimental Framework • Testbed facility available to any researcher on campus. • Open source code, protocols, and testing procedures. • Eventually, available to researchers around the world. Researcher Testbed Controller Deployed Nodes SubmitsExperiment Distributes Codeto Nodes

45. Interdisciplinary research teams needed to be effective. Advantages in keeping nodes simple. Key implementation challenges are in the MAC layer.  Distinguishing attributes of CR systems/networks raise new security implications that cannot be addressed using conventional approaches. Most of the security threats to CR networks arise from problems related to incumbent- and self-coexistence issues. Most of the security threats to CR terminals arise from problems related to the programmability of the devices Lessons Learned 1/2

46. Highly agile hardware is needed for meaningful adaptation.  DSPs lack the computational power, and FPGAs have yet to deliver on the capability of autonomous adaptability.  Much work remains on appropriate software architectures for supporting CR Non-coordination in adaptation has its advantages if done right (easy to do wrong). Much work still remains in defining appropriate metrics Distributed signal sensing provides better reliability – spatial sampling provides more info. than simply temporal sampling Lessons Learned 2/3

47. Lessons Learned 3/3 • Case-based reasoning along with optimization techniques work well, but other possibilities exists such as HMMs. • Observation, Orientation, Decision all have interrelationship and jointly considering them can simplify the implementation • Lots of lessons through extensive simulation helps to make the cognitive engine more effective when deployed. • Reliable, scalable and secure rendezvous techniques are hard • Detection benefits from detection-- bootstrap • Promise exists for power savings