Dyspan Sdr Cr Tutorial 10 25 Rev02

Understanding the Issues in Software Defined Cognitive Radio Jeffrey H. Reed Charles W. Bostian Virginia Tech Bradley Dept. of Electrical and Computer Engineering

Comment Slide – Delete Before Submitting Following section presented by Reed

What You Will Learn Basic Concepts of Software Defined Radio (SDR) Basic Concepts of Cognitive Radio (CR) and its relationship to SDR. How Cognitive Radios are Implemented Analyzing Cognitive Radio Behavior and Performance Regulatory Issues in Cognitive Radio Deployment Cognitive Radio Applications in Interoperability and Spectrum Access Current Research Issues

Acknowledgements Albrecht Johannes Fehske Thomas Rondeau Bin Le James Neel David Scaperoth Kyouwoong Kim David Maldonado Lizdabel Morales Youping Zhao Joseph Gaeddert Students that contributed to this presentation:

Software Defined Radio – Basic Concepts and Relationship to Cognitive Radio

Software Defined Radio (SDR) Termed coined by Mitola in 1992 Radio’s physical layer behavior is primarily defined in software Accepts fully programmable traffic & control information Supports broad range of frequencies, air interfaces, and application software Changes its initial configuration to satisfy user requirements

Software Defined Radio Levels (1/2) Highest Level of Reconfigurablity Completely flexible modulation format, protocols and user functions Flexible bandwidths and center frequency, i.e., RF front end is also configurable Adapts to different network and air interfaces Open architecture for expansion and modifications

Software Defined Radio Levels (2/2) Lowest Level of Reconfigurability Radio not easily changed Preset signal bandwidth and center frequency Few and preset modulation formats, protocols, and user functions

Advantages of SDR Reduced content of expensive custom silicon Reduce parts inventory Ride declining prices in computing components DSP can compensate for imperfections in RF components, allowing cheaper components to be used Open architecture allows multiple vendors Maintainability enhanced

Drawbacks of SDR Power consumption (at least for now) Security Cost Software reliability Keeping up with higher data rates Fear of the unknown Both subscriber and base units should be SDR for maximum benefit

Applications for SDR Military Full Connectivity Sensor Networks Better Performance Commercial Lower Cost – subscriber units Lower Cost – base unit Lower Cost – network Better performance Regulatory Stretch expensive spectrum Build in innovation mechanisms

How is a Software Radio Different from Other Radios? - Application Software Radio Dynamically support multiple variable systems, protocols and interfaces Interface with diverse systems Provide a wide range of services with variable QoS Conventional Radio Supports a fixed number of systems Reconfigurability decided at the time of design May support multiple services, but chosen at the time of design Cognitive Radio Can create new waveforms on its own Can negotiate new interfaces Adjusts operations to meet the QoS required by the application for the signal environment

How is a Software Radio Different from Other Radios?- Design Software Radio Conventional Radio + Software Architecture Reconfigurability Provisions for easy upgrades Conventional Radio Traditional RF Design Traditional Baseband Design Cognitive Radio SDR + Intelligence Awareness Learning Observations

How is a Software Radio Different from Other Radios? - Upgrade Cycle Software Radio Ideally software radios could be “future proof” Many different external upgrade mechanisms Over-the-Air (OTA) Conventional Radio Cannot be made “future proof” Typically radios are not upgradeable Cognitive Radio SDR upgrade mechanisms Internal upgrades Collaborative upgrades

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Cognitive Radio Term coined by Mitola in 1999 Mitola’s definition: Software radio that is aware of its environment and its capabilities Alters its physical layer behavior Capable of following complex adaptation strategies “ A radio or system that senses, and is aware of, its operational environment and can dynamically and autonomously adjust its radio operating parameters accordingly” Learns from previous experiences Deals with situations not planned at the initial time of design

What is a Cognitive Radio? Adaptive radios can adjust themselves to accommodate anticipated events Fixed radios are set by their operators Cognitive radios can sense their environment and learn how to adapt Beyond adaptive radios, cognitive radios can handle unanticipated channels and events. Cognitive radios require: Sensing Adaptation Learning Cognitive radios intelligently optimize their own performance in response to user requests and in conformity with FCC rules.

Cognitive radios are machines that sense their environment (the radio spectrum) and respond intelligently to it. Like animals and people they seek their own kind (other radios with which they want to communicate) avoid or outwit enemies (interfering radios) find a place to live (usable spectrum) conform to the etiquette of their society (the Federal Communications Commission) make a living (deliver the services that their user wants) deal with entirely new situations and learn from experience

1) Access to spectrum (finding an open frequency and using it) Cognitive radios are a powerful tool for solving two major problems:

2) Interoperability (talking to legacy radios using a variety of incompatible waveforms) Cognitive radios are a powerful tool for solving two major problems:

Levels of Radio Functionality Adapted From Table 4-1Mitola, “ Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio, ” PhD Dissertation Royal Institute of Technology, Sweden, May 2000. Level Capability Comments 0 Pre-programmed A software radio 1 Goal Driven Chooses Waveform According to Goal. Requires Environment Awareness. 2 Context Awareness Knowledge of What the User is Trying to Do 3 Radio Aware Knowledge of Radio and Network Components, Environment Models 4 Capable of Planning Analyze Situation (Level 2& 3) to Determine Goals (QoS, power), Follows Prescribed Plans 5 Conducts Negotiations Settle on a Plan with Another Radio 6 Learns Environment Autonomously Determines Structure of Environment 7 Adapts Plans Generates New Goals 8 Adapts Protocols Proposes and Negotiates New Protocols

What is a cognitive radio? An enhancement on the traditional software radio concept wherein the radio is aware of its environment and its capabilities , is able to independently alter its physical layer behavior , and is capable of following complex adaptation strategies. Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10. Urgent Allocate Resources Initiate Processes Negotiate Protocols Orient Infer from Context Select Alternate Goals Plan Normal Immediate Learn New States Observe Outside World Decide Act User Driven (Buttons) Autonomous Infer from Radio Model States Generate “Best” Waveform Establish Priority Parse Stimuli Pre-process Cognitive radio Cognition Cycle

Level 0 SDR 1 Goal Driven 2 Context Aware 3 Radio Aware 4 Planning 5 Negotiating 6 Learns Environment 7 Adapts Plans 8 Adapts Protocols Relationship between the Cognition Cycle and the Levels of Functionality Normal Urgent Select Alternate Goals Establish Priority Negotiate Immediate Negotiate Protocols Generate Alternate Goals Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10. Determine “Best” Known Waveform Generate “Best” Waveform Allocate Resources Initiate Processes Orient Infer from Context Parse Stimuli Pre-process Plan Normal Learn New States Observe Outside World Decide Act User Driven (Buttons) Autonomous Determine “Best” Plan Infer from Radio Model States

FCC Motivation for Cognitive Radio Currently the FCC is refarming licensed bands such as the TV Bands Long-term vision Eliminate rigid, coarse spectrum allocations Switch to demand-based approach Improve relative spectral efficiency Need new protocols for Supporting long-term vision of the FCC Inter-network interference avoidance Maximizing utilization of available bandwidth

Cognitive Radio Advantages All the software radio benefits Improved link performance Adapt away from bad channels Increase data rate on good channels Improved spectrum utilization Fill in unused spectrum Move away from over occupied spectrum New business propositions High speed internet in rural areas High data rate application networks (e.g., Video-conferencing) Significant interest from FCC, DoD Possible use in TV band refarming

Cognitive Radio Drawbacks All the software radio drawbacks Significant research to realize Information collection and modeling Decision processes Learning processes Hardware support Regulatory concerns Loss of control Fear of undesirable adaptations Need some way to ensure adaptations yield desirable networks

Cognitive Radio & SDR SDR’s impact on the wireless world is difficult to predict “ But what…is it good for?” Engineer at the Advanced Computing Systems Division of IBM, 1968, commenting on the microchip Some believe SDR is not necessary for cognitive radio Cognition is a function of higher-layer application Cognitive radio without SDR is limited Underlying radio should be highly adaptive Wide QoS range Better suited to deal with new standards Resistance to obsolescence Better suited for cross-layer optimization

Types of Software Defined Cognitive Radios Policy-Based Radio Reconfigurable Radio Cognitive Radio

Policy-based Radio A radio that is governed by a predetermined set of rules for choosing between different predefined waveforms The definition and implementation of these rules can be: during the manufacturing process during configuration of a device by the user; during over-the-air provisioning; and/or by over-the-air control Analogous to rules of what to order from a menu “ I’ll have GSM today”

Reconfigurable Radio A radio whose hardware functionality can be changed under software control Reconfiguration control of such radios may involve any element of the communication network Analogous to rules of what to order from a menu and permit substitutions to the order “ I’ll have GSM today with the 802.11 FEC”

Comment Slide – Delete Before Submitting Following section presented by Reed Needs more work on example SDR architectures

Radio Architecture Rx Tx RF Signal Amplify Mixer Filter Amplify Mixer Filter IF Signal Baseband Signal Superhetrodyne RF Signal Amplify Mixer Filter Analog To Digital Converter IF Signal Digital Signal Processing Software Defined Radio

Behind the Converters: The Software Architecture Nature of Architecture Depends on Applications: Commercial vs. Military Benefits of a Good Architecture Clear way to implement system Reuse --- modularity Quality control and testing Portability – one radio to another Upgradability Outsourcing/managing development Language independence More potential for Over-the-Air Programming Standardized interfaces Middleware-based architectures are commonly used

Example SDR: GNU Radio What is GNU Radio? GNU Radio is a set of S/W signal processing building blocks that allow users to create their own S/W radio Why GNU Radio? Attempts to solve the complexity issues of both H/W and S/W of SDR Modular (use with most any GPP) S/W used on Windows, Linux, Mac

Implementing a SDR with the GNU Radio USRP - Universal Software Radio Peripheral Courtesy of http://www.gnu.org/software/gnuradio/doc/exploring-gnuradio.html GNU Radio S/W available at www.gnuradio.org GNU Radio software - core s/w - user made s/w

USRP 4 ADC’s: 12bits per second, 64MSps, Analog Input BW over 200Mhz 4 DAC’s 14bits per second, 128MSps Receive Channel RF Interface Transmit Channel RF Interface

Challenges in SDR Design Hardware Significant effort in computing HW Advance DSP Designs Flexible RF and antennas Flexible ADCs Tradeoff of performance and flexibility Software Integration of components into single design flow Tradeoff of performance and flexibility Testing and validation FCC hardware/software certification Smoothing of design cycle Reduce overall time-to-market

Technology Challenges of SDR Technology in SDR partitioned into three basic pieces Hardware Physical devices on which processing is performed or interface to the “real world” Software Glue holding together system Network Functionality and ultimate value to the end-user Advances needed in all three arenas

Hardware Significant effort to date in computing HW Non-traditional computing platforms Advanced DSP designs High data rate FEC remains problematic Emphasis on computing HW alone can be myopic Other critical areas that require significant further work Flexible (or software controlled) RF Flexible ADC Antennas

Flexible RF RF is one of the main limiting factors on system design Places fundamental limits on the signal characteristics BW, SNR, linearity Truly flexible SDR requires flexible RF Difficult task RF is fundamentally analog and requires different a different approach for the management of attributes One method for achieving this is through the use of MEMS

MEMS (Micro Electro Mechanical Systems) Designs for RF Front Ends Tunable antenna with narrow fixed bandwidth Patch antenna connected by RF switches E-tenna’s Reconfigurable Antenna Idealized MEMs RF Front-end for a Software Radio Use MEMS filter banks to create tunable RF filters J.H. Reed, Software Radio: A Modern Approach to Radio Design, Prentice-Hall 2002.

ADC Challenges ADC is the bound between analog and digital world SDR requires the tuning of ADC characteristics Number of bits Support adequate SNR and dynamic range Sampling rate Prevent over-sampling (waste power) ADC technology trends are not necessarily compatible with these needs

ADCs Getting Better Exponentially 1994 ~ 2004 a leap of Analog to Digital Converter (ADC) technology Regression curve fit shows exponential increasing trends Trends are quite different for different ADC structures B bits f s sample rate Bin Lee, Tom Rondeau, Jeff Reed, Charles Bostian, “Past, Present, and Future of ADCs,” submitted to IEEE Signal Processing Magazine, August 2004

ADC: Improving Even When Considering Power Power-to-sampling-speed ratio favors less number of comparators The choice in selecting an ADC is tied to application requirement P diss is power dissipation Bin Lee, Tom Rondeau, Jeff Reed, Charles Bostian, “Past, Present, and Future of ADCs,” submitted to IEEE Signal Processing Magazine, August 2004

Integration of Hardware DSP share traits with GPP Similar programming methods Similar computing concepts Even though implementation may be wildly different FPGA and CCM do not share these traits with GPP Completely different programming paradigm Portability is an extremely difficult problem

Software Operating Environment Standardized structure for the management of HW and SW components SCA Technology to date has been largely derived from existing PC paradigm GPP-centric structure SCA 3.0 Hardware Supplement is an attempt to rectify this problem Several challenges remain Power management Integration of HW into structure

Software Architectures “ The sheer ease with which we can produce a superficial image often leads to creative disaster.” Ansel Adams [1902-1984], American artist (photography) Poor architectural design is leads to significant inefficiencies Architectures provide multiple benefits Clear way to implement system Generally component-based Software or hardware components Standardized interfaces Standard technology interface Common technology like middleware Standard semantic -- API Architectures becoming more prominent Software Communications Architecture (SCA) $14B to $27B for SCA radio work by DoD Cluster 5 contract up to $1B for embedded & handheld prototypes Maintain awareness of activity: big money for SDR

So How Do You Make a Software Radio? You have some hardware And you want to run some waveforms GSM, IS-95, or some other technology that the hardware is powerful enough to support

What kind of software is needed? (1/4) Something to manage hardware Configure associated devices Set devices to known state i.e.: Make sure NCO is available and ready Initialize cores Make sure programmable devices are ready Set memory pointers in DSP Set FPGA to known state

What kind of software is needed? (2/4) Some standardized way of storing relevant information More than just short-term memory Store configuration files Store last state of the machine Store user-defined attributes Identity Permissions Store functional software Should be able to map any kind of storage device to this Dynamic RAM, hard drive, FLASH, other

What kind of software is needed? (3/4) Some way of structuring the waveforms Standardized way of structuring “applications” so that the radio can “run” them In a Windows machine, these are .exe files It has to be generic enough for it to fit well with machines other than GPPs Needs to be able to interface with functional software

What kind of software is needed? (4/4) Something to actually “run” waveforms Install functional software in appropriate core Generate a start event Something to keep track of what is available and what can and cannot be installed Ideally, this will bind the whole thing together

Fundamental Composition of the SCA Keep track of HW in the system Store working environment, bit images, properties, etc. Boot up and maintain HW Keep track of what’s there (installed) Manage collection of resources to create waveform Capabilities e.g., Start and stop, test, describe Connections between resources Device Manager FileSystem Manager Devices Domain Manager Application Factory Resoruces Manage waveform operation Application Port

Software Communications Architecture (SCA) Processor-centric structure Standardized interface for components Seamless handling of HW and SW Open-source implementations available OSSIE C++ by MPRG SCARI Java by Communications Research Centre Non-secure Secure

Is the SCA Suitable for Commercial Implementations? Maybe No Current version is GPP-centric, hence heavy Irrelevant capabilities decrease its effectiveness Focus on waveform portability has limited appeal Static nature not well suited for cognitive radio No provisions for power management Yes Basic architectural principles are sound SCA 3.0 is a first step in dealing with GPP-centric communications within the radio Significant momentum ($$$ and time) within defense industry Being adopted by several other nations’ defense establishments

Summary of Trends SDR need is driven by two principal factors New applications Cognitive radio, collaborative radio & advanced roaming Increased number of protocols to support Potential cost reductions ADC is no longer the key bottleneck Flexible RF products starting to come to market Software architecture critical Additional technology supporting architectural approach available Reconfigurable hardware needed General-purpose hardware approach is likely to be unable to keep up with wireless bandwidth growth Component-based reconfigurable hardware architectures present powerful solution Multi-core processors show promise

SDR Market Today Military JTRS program created multi-billion dollar SDR market DARPA neXt Generation (XG) Communications project International derivatives of JTRS/SCA (EU, Canada, etc) Commercial Digital RF processors (TI Bluetooth and GSM) Multi-standard basestation implementations (Vanu) SDR handsets probably within 3 years as low power processors become available Regulatory Recent FCC directive to ensure code and RF compatibility

Cognitive Radio Implementation

Knobs and Meters Sample tabulation of knobs and meters by layer (adapted from Prof. Huseyin Arslan) Layer Meters (observable parameters) Knobs (writable parameters) MAC Frame error rate Data rate Source coding Channel coding rate and type Frame size and type Interleaving details Channel/slot/code allocation Duplexing Multiple access Encryption PHY Bit error rate SINR Received signal power Noise power Interference power Power consumption Fading statistics Doppler spread Delay spread Angle of Arrival Transmitter power Spreading type and code Modulation type Modulation index Pulse shaping Symbol rate Carrier frequency Dynamic range Equalization Antenna directivity Other Computational power Battery Life CPU Frequency scaling

The VT Cognitive Engine Simple Concept Radio Parameters “ Knobs and Meters” Channel Statistics Cognitive Engine Radio RX Radio TX

The VT Cognitive Engine Simple Concept Radio TX Channel Statistics Cognitive Engine Radio RX “ Meters” “ Old Knobs Settings” “ Old Knobs Settings” Radio Parameters “ Knobs and Meters” “ Optimized Solution” “ New Settings” “ New Settings”

The VT Tiered Approach to Cognition Modeling System Take in surrounding radio environment and user/network requirements Remember models and apply Case-based Decision Theory to determine best course of action to take Use Genetic Algorithms to update and optimize the new radio parameters Monitor feedback from radio to understand system performance Penalize knowledge base for poor performance

The Cognitive Engine “ Intelligent agent” that manages cognition tasks in a Cognitive Radio Independent entity that oversees cognitive operations Ideal Characteristics: Intelligence (Accurate decisions) Reliability (Consistent decisions) Awareness (Informed decisions) Adaptability (Situation dependent decisions) Efficiency (Low overhead decisions) Excellent QoS (Good decisions) Tradeoffs exist between these characteristics

Software Architecture - Theory

Software Architecture – Limited Functionality

Software Architecture: Full Functionality

Some Approaches to Cognitive Engine Genetic Algorithms Markov Models Neural Nets Expert Systems and Natural Language Processing Fuzzy Logic Open issue on what are the appropriate cognitive engine techniques

GA’s and biological metaphor The WSGA uses a genetic algorithm, which operates on chromosomes. The genes of the chromosome represent the traits of the radio (frequency, modulation, bandwidth, coding, etc.). The WSGA creatively analyzes the information from the CSM to create a new radio chromosome.

Some Approaches to Signal Classification Cyclic Spectrum Analysis Statistical characterization of signal parameters Eigenstructure techniques Model-based approaches

Analyzing Performance in a Cognitive Radio

Analyzing the Performance of a Network of Cognitive Radios

Ways of Analyzing Performance For the Cognitive Radio QOS, Detection of Primary Users (PU), SW Platform, QOS of PU, Position Location For the network of Cognitive Radios Quantifying the impact of the use of CR in a network Game Theoretic Approach See www.mprg.org/people/gametheory/index.shtml

Cognitive Radio Performance Evaluation: QoS Parameters Data throughput Latency Voice quality Video quality These depend on link performance measures: PHY Layer, e.g.: Bit error rate (BER) Signal to noise ratio (SIR) Signal to interference and noise ratio (SINR) Received signal strength MAC, network-layer, e.g.: Frame error rate (FER) Packet error rate Routing table change rate

Cognitive Radio Performance Evaluation: Detection of Primary Users Probability of detection (PoD) as a function of: number of observed symbols SNR Number of signals present (primary and secondary) Level of cooperation, e.g., number of devices (CRs) needed to achieve a given PoD (see next slide) Probability of false alarm same parameters as PoD

Cognitive Radio Performance Evaluation: Underlying Software Radio Platform Number of supported waveforms Processing power (mips, flops, #gates) Waveform-code reusability and portability Reusable: the same code can be used in principle in a different SDR platform Portable: instantaneous plug and play Delay for loading unloading waveforms RF front-end: Frequency range, Dynamic range, Sampling frequency, Sensitivity, Selectivity, Stability, Spurious response Power consumption Size, Weight, Cost

Cognitive Radio Performance Evaluation: Position Location Main perfromance measures for position location service: Precision and Availability Different technologies provide different quality of position location services: Assisted GPS (AGPS) performance degrades significantly when no clear view of sky (indoors, urban canyons) works best in rural areas (no shadowing) Network based services accuracy in general lower than AGPS works best with many base stations present (populated areas) performance doesn't degrade indoors Hybrid services Combines advantages of both approaches AGPS whenever possible, if not available switch to network based service

Cognitive Radio Performance Evaluation: Primary users' QoS Time needed to vacate channel after primary user (re-) appears Negative impacts: Increased SINR, BER, FER, … results in: Decreased: Data throughput Latency Voice quality Video quality Increased Call drop rate (cell phone networks) Handover failure (cell phone networks)

Dynamic cognitive radios in a network Dynamic benefits Improved spectrum utilization Improve QoS Many decisions may have to be localized Distributed behavior Adaptations of one radio can impact adaptations of others Interactive decisions Locally optimal decisions may be globally undesirable

Locally optimal decisions that lead to globally undesirable networks Scenario: Distributed SINR maximizing power control in a single cluster For each link, it is desirable to increase transmit power in response to increased interference Steady state of network is all nodes transmitting at maximum power Power SINR Need way to analyze networks with interactive decisions. Game theory can help.

What is a game? A game is a model (mathematical representation) of an interactive decision process. Its purpose is to create a formal framework that captures the process’s relevant information in such a way that is suitable for analysis. Different situations indicate the use of different game models. Identification of the type of game played by the cognitive radios provides insights into performance

Steady state characterization Steady state optimality Convergence Stability Scalability Key Issues in Analysis Steady State Characterization Is it possible to predict behavior in the system? How many different outcomes are possible? Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters? Convergence How do initial conditions impact the system steady state? What processes will lead to steady state conditions? How long does it take to reach the steady state? Stability How does system variations impact the system? Do the steady states change? Is convergence affected? Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal? a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 3

Cognitive Radio, Spectrum Policy, and Regulation

An Analogy between a Cognitive Radio and a Car Driver Cognitive Radio’s capabilities: Senses, and is aware of, its operational environment and its capabilities Can dynamically and autonomously adjust its radio operating parameters accordingly Learns from previous experiences Deals with situations not planned at the initial time of design Car Driver’s capabilities: Senses, and is aware of, its operational environment and its capabilities Can dynamically and autonomously adjust the driving operation accordingly Learns from previous experiences Deals with situations not planned at the initial time of learning to drive They behave almost exactly the same!!!

“ Rules of the Road” ➟ “Rules of the Cognitive Radio” POLICY AWARE Primary User has higher priority over Secondary users Radio emission may be prohibited at certain location or for certain type of radio LOCATION AWARE Precautions for certain areas, such as hospital, airplane, gas station, etc, where RF emission is highly restricted Parking Zone * Source of some pictures in this section: “California Drivers Handbook 2005”; “Illinois Rules of the Road 2004”

“ Rules of the Road”-inspired CR Philosophy and Etiquette Insights from “Traffic Model Analogy” TRAFFIC Scheduling Various traffic schedule methods and duplex methods for efficient and fair sharing of congested unlicensed spectrum TDD vs. FDD ➟ Dynamic Uplink/Downlink transmission in TDD mode Spectrum pooling is encouraged Traffic Law ➟ Spectrum Regulations Management by both Punishment and Encouragement FDD mode operation with paired spectrum $ fine

A traffic model analogy – Common Issues It is critical that everyone drives sensibly or defensively ➟ Every CR should be aware of Hidden Node problems Hidden Node Problem A and C are unaware of their interference at B, due to A, C cannot hear each other.

Vehicle Following Distances for Car Drivers ➟ Time needed to vacate channel after primary user (re-) appears for Cognitive Radios Vehicle Following Distances: TWO-SECOND RULE: Use the two-second rule to determine a safe following distance. A traffic model analogy (cont.)

A traffic model analogy (cont.) SPEED LIMIT for car driver ➟ Interference Level Limit (e.g. Max. Allowed Interference Temperature) for Cognitive Radio

City Map for Car Drivers ➟ Radio Environment Map (REM) for Cognitive Radios Learning from “Traffic model analogy” for the development of Cognitive Radio… REM Time (or duration) Location (x, y, z), Type of radio environment Local Policy Profile of primary users Profile of interference Max. allowed Interference Level

Learning from “Traffic model analogy” for the development of Cognitive Radio…(cont.) Regular conformance check against regulations Language and Etiquette for CR for Signaling and Negotiation

Spectrum Policy Challenges The spectrum is already allocated True spectrum scarcity on urban areas (ISM band) We need to deal with existing standards The standards are embedded in the hardware!

Spectrum Utilization Spectrum utilization is quite low in many bands Concept: Have radios (or networks) identify spectrum opportunities at run-time Transparently (to legacy systems) fill in the gaps (time, frequency, space) Considered Bands ISM Public Safety TV (UHF) Lichtenau (Germany), September 2001 dB  V/m From F. Jondral, “ SPECTRUM POOLING - An Efficient Strategy for Radio Resource Sharing, ” Blacksburg (VA), June 8, 200 4.

Spectrum Occupancy Study Spectrum occupancy in each band averaged over six locations (Riverbend Park, Great Falls, VA, Tysons Corner, VA, NSF Roof, Arlington, VA, New York City, NRAO, Greenbank, WV, SSC Roof, Vienna, VA) [ Source: FCC NPRM 03-0322. http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-03-322A1.pdf Results from Shared Spectrum Co. and Univ. of Kansas

Regulatory Trends In an effort to improve radio spectrum management and promote a more efficient use of it, the regulatory bodies are trying to adopt a new spectrum access model. This represents a paradigm shift from hardware-embedded policy implementation to dynamic software-based adaptation Harder to keep tight control!

Regulatory Trends Proceedings that are the Key Drivers: Receiver Standards ET Docket No. 03-65 NOI Interference Temperature ET Docket 03-237 NPRM/NOI Cognitive Radio ET Docket No. 03-108 NPRM License-exempt Operation in the TV Broadcast Bands ET Docket No. 04-186 Additional Spectrum for License-exempt devices below 900 MHz and in the 3 GHz Band ET Docket No. 02-380

Policy Engine Approach PE needs to provide limiting operational parameters Interpret policy automatically Act dynamically in response to the operating environment PE needs to authenticate the policy It will require an extremely efficient policy format It must handle the complexity of current policy without presenting a significant load to the CE The goal is to limit the search space before looking for a solution Rely on CE to do the reasoning about spectrum sharing

DARPA XG Program XG is trying to Develop the Technology and System Concepts to Dynamically Access Available Spectrum Source: DARPA XG Program

Spectrum Policy Language Design Actors and Roles Source: BBN Technologies Solutions LLC Area that needs improvements! Spectrum Policy Policy Administrator (e.g. FCC, NTIA) XG System Spectrum Opportunities Awareness via XG Protocols and Sensing query Language Design Knowledge Core Language Model and Representation Policy Language Designer (e.g. BBN/XG Program) Policy Editing and Verification Tools design Machine Readable Policy Instances Policy Repository encode publish Policy Repository

The BIG Question: FCC Certification At all costs, the FCC must avoid “an epidemic situation in the unlicensed area.” FCC likes to operate from “established engineering practices.” The SDR and CR communities must defined these. Open source radios are a particular problem because their operating parameters are not necessarily bounded.

People seeking certification must explain how their software will respect parameter limits specified in FCC rules. Submitted software must be accompanied by flow charts, code, and an explanation of how it works. Software certification should not be more difficult to achieve than hardware certification.

Proposed Approach Policy Engine Cognitive Engine Applications Bios/OS

Example of a Possible Cognitive Radio Application

How can CR improve Spectrum Utilization? Allocate the frequency usage in a network. Assist secondary markets with frequency use, implemented by mutual agreements. Negotiate frequency use between users. Provide automated frequency coordination. Enable unlicensed users when spectrum not in use. Overcome incompatibilities among existing communication services.

How can CR improve Network Management Efficiency? Present Practice characterizes service demand in a network statistically By using cognitive radio, time-space characterization of demand is possible Cognitive Radio Learns plans of the user to move and use wireless resources Expresses its plans to the network reducing uncertainty about future demand The network can use its resources more efficiently

How can a CR Enhance Service Delivery? Wireless Communications in general and cognitive radio in particular have great potential to generate personal user information For example: actual position, native language, habits, travel, etc. Enhanced services can be provided using this information CR interacts with the network on user’s behalf

Note Daily Drive Home at 5:30 (GPS Aided) Recall Brief Coverage Hole 1. Observe and Analyze Situation 2. Evaluate Alternatives Do Nothing Increase Coding Gain Increase Transmit Power Vertical Handoff Decrease Call Drop Threshold 4. Adapt Network 3. Signal Base Station Request Decrease In Call Drop Threshold CR in a Cellular System

Example of Cognitive Radio in Cellular Environment Cognitive radio is aware of areas with a bad signal Can learn the location of the bad signal Has “insight” Radio takes action to compensate for loss of signal Actions available: Power, bandwidth, coding, channel Radio learns best course of action from situation

Supplements Cellular System Cellular systems are plagued with coverage gaps Cognitive radio can enhance coverage around these gaps by: Learning the areas of coverage gaps Learning the best PHY layer parameters Taking action prior to getting to the area Sharing this knowledge with other cell phones Coverage gaps are found very rapidly Alert cellular system of gap, so provider can remedy situation

Current Research Efforts in Cognitive Radio

Universities Participating at Dyspan Bar-Ilang Univ. Georgia Tech Mich. State Univ. Michigan Tech MIT Northwestern Univ. Ohio Univ. Rutgers Univ. RWTH Aachen Univ. Stanford Univ. Univ. of Calif. Berkeley Univ. of Cambridge Univ. of Col. Univ. of MD Univ. of Pittsburg Univ. of Toronto Univ. of Warwick Universitaet Karlsruhe University of Piraeus Virginia Tech

DARPA neXt Generation Program - Motivation Problems: Spectrum Scarcity Spectral resources are not fully exploited Opportunities exist in space, time, frequency Current static spectrum allocation prevents efficient spectrum utilization Deployment difficulty Different policy regimes in different countries Deployment of communication networks tedious Of particular interest in military applications Proposed solution: Complement static spectrum allocation with "Opportunistic spectrum access" Primary users Licensed Priority to use allocated spectrum Guaranteed QoS Secondary users Non-licensed Can allocate unused spectrum among themselves Have to vacate bands if required by primaries Unless otherwise stated, all the information in this description of the DARPA XG program is based on the XG Vision rfc, available online: http://www.darpa.mil/ato/programs/xg/

DARPA neXt Generation Program: Research Goals Development of technologies that enable spectrum agility Sensing and characterization of the (RF-) environment Identification of unused spectrum ("opportunities") Allocation and exploitation of opportunities Development of standards for a software based policy regime to enable policy agility explained in more detail on the next slides

DARPA neXt Generation Program: Concepts of Policy Agility (1) Decoupling of policies from implementation Define abstract behaviors, e.g., "Channel can be vacated within t sec." Policies implement (dictate) behaviors Protocols instantiate behaviors Traceability All behaviors must be traceable to policies: Each operational mode a device is capable of is tied to a specific policy which allows it Software based Spectrum use policies have to be machine understandable Policy constraints can be implemented "on-the-fly" via software downloads

DARPA neXt Generation Program: Concepts of Policy Agility (2) Figure drawn from XG Vision RFC Decoupling policies, behaviors, and protocols: Separating what needs to be done from how it is implemented The framework's four key components

DARPA neXt Generation Program: Concepts of Policy Agility (3) Machine understandable policies will enable software downloads "on-the-fly" Figure drawn from XG Vision RFC

DARPA neXt Generation Program: Promises Flexible radio operation due to spectrum agility Simplified user control of XG systems System operation can be controlled in terms of behavior No need for technological details Facilitated policy design Constraints can be tailored to national or institutional needs in terms of behaviors No need for technological details Eased wireless device accreditation Traceability provides a means for an easy testing procedure of behaviors against policies Broad and future proof standard Will be designed to be applicable to a broad range of radios Future proof design will enable extension of the standard Framework character: different technological solutions (protocols) can be accomodated to perform a particular task (sensing, identification, allocation)

E 2 R Research in Europe E 2 R = End-to-End Reconfigurability Efficient, advanced & flexible end-user service provision Tailoring of application and service provision to user preferences and profile Efficient spectrum, radio and equipment resources utilization Enabling technologies for flexible spectrum resources Multi-standard platforms A single hardware platform shared dynamically amongst multiple applications

E2R Participants 1/2 Academic Partners Eurecom: Institut Eurecom I2R KCL:Centre for Telecommunications Research (CTR) - King's College London UoA: University of Athens TUD: Dresden University UoKarlsruhe: University of Karlsruhe, Communications Engineering Lab UPRC: University of Piraeus Research Center UNIS: University of Surrey Operator R&D Partners DoCoMo: DoCoMo Communications Laboratories Europe GmbH FT: France Telecom R&D TILAB: Telecom Italia S.p.A. TID: Telefonica I+D Source http://e2r.motlabs.com/

E2R Participants 2/2 Manufacturer Partners MOTO: Motorola Labs ACP: Advanced Circuit Pursuit AG ASEL: Alcatel SEL DICE: Danube Integrated Circuit Engineering Nokia: Nokia GmbH PMDL: Panasonic UK PEL: Panasonic European Laboratories GmbH SM: Siemens Germany SMC: Siemens Mobile Communications SpA THC: Thales Communications TRL: Toshiba Research Europe Limited MIL: Motorola Israel Ltd Regulator partners DiGITIP UPC: UPC RegTP

Berkeley Wireless Research Center

Berkeley Wireless Research Center Designing a cognitive radio to improve spectrum utilization Radio searches for feasible region and optimal waveform for transmission (environment sensing) Avoiding of Interference with primary spectrum users by: Measuring spectrum usage in time, frequency, and space Having statistical traffic models of primary spetrum users A cognitive radio test bed is currently being built From R.W. Brodersen, A. Wolisz, D. Cabric, S. M. Mishra, D. Willkomm "Corvus: A Cognitive Radio Aproach For Usage of Virtual Unlicensed Spectrum", July 29th 2004 The six system functions are split between physical and data link layer Two control channels: UCC for group management (group announcement) GCC used only by members of a certain group

WINLAB Rutgers University Design of info-stations for emergency and disaster relief applications Use of customized commercially available hardware, e.g. 802.11 wireless From: http://www.winlab.rutgers.edu/pub/docs/focus/Infostations.html Benefits Increases the total information available for rescue workers tailors the information with regard to specific needs and available bandwidth coordinates communication of different rescue groups at one site

National Science Foundation Grant CNS-0519959 “An Enabling Technology for Wireless Networks – the VT Cognitive Engine” National Institute of Justice Grant 2005-IJ-CX-K017 “A Prototype Public Safety Cognitive Radio for Universal Interoperability.” Develop and test a prototype system for using cognitive techniques to allow WiFi-like unlicensed operation in unoccupied TV channels. Investigate the behavior of networks containing both legacy radios and cognitive radios that can interoperate with them. Build a prototype cognitive radio that can recognize and interoperate with three commonly used and mutually incompatible public safety waveform standards http://support.mprg.org/dokuwiki/doku.php?id=cognitive_radio:start

Some SDR and Cognitive Radio Research at VT SCA core framework Open source effort Role of DSPs Power Management Integration of testing into the framework Rapid prototyping tools Smart antennas Smart antenna API Networking performance Experimental MIMO systems Cooperative radios Distributed MIMO Distributed Applications Cognitive radio networks Game theory analysis of cognitive networks Learning Techniques Test Beds UWB SDR Low Power SCA Distributed PCs Public Safety Radio Demo Keep up to date at http://support.mprg.org/dokuwiki/doku.php?id=cognitive_radio:start And http://www.mprg.org

CR Test-bed under development Neighbor WLANs Ethernet Actions Cordless Phone Bluetooth MWOL Tektronix TDS694C: Digital Real-time Oscilloscope Tektronix RSA3408A: Real-Time Spectrum Analyzer Distributed Measurement Collaborative Processing Observations Analysis and decision REM online updating TV station

Public Safety - Interoperability Focus on multi-agency interoperability since 9/11/2001 Cognitive radio technology can improve interoperability by enabling devices to bridge communications between jurisdictions using different frequencies and modulation formats. Such interoperability is crucial to enabling public safety agencies to do their jobs. Example: National Public Safety Telecommunications Council (NPSTC) supported by U.S. DOJ’s AGILE Program

IEEE 802.22 WRAN system based on 802.22 will make use of unused TV broadcast channels Interoperable air interface for use in spectrum allocated to TV Broadcast Service Allows Point to Multi-point Wireless Regional Area Networks (WRANS) Supports a wide range of services Data, voice and video Residential, Small and Medium Enterprises Small Office/Home Office (SOHO) locations

IEEE Project 1900 (P1900) The IEEE P1900 Standards Group was established in 1Q 2005 jointly by the IEEE Communications Society (ComSoc) and the IEEE Electromagnetic Compatibility (EMC) Society . The objective of this effort is to develop supporting standards related to new technologies and techniques being developed for next generation radio and advanced spectrum management.

IEEE P1900.1 Working Group : Objective document: “Standard Terms, Definitions and Concepts for Spectrum Management, Policy Defined Radio, Adaptive Radio and Software Defined Radio.” Purpose: This document will facilitate the development of these technologies by clarifying the terminology and how these technologies relate to each other.

IEEE P1900.2 Working Group : Objective document: “Recommended Practice for the Analysis of In-Band and Adjacent Band Interference and Coexistence Between Radio Systems.” Purpose: T his standard will provide guidance for the analysis of coexistence and interference between various radio services.

IEEE P1900.3 Working Group : Objective document : “Recommended Practice for Conformance Evaluation of Software Defined Radio (SDR) Software Modules.” Purpose : This recommended practice will provide guidance for validity analysis of proposed SDR terminal software prior to physical programming and activation of SDR terminal components.

IEEE 802.11h 802.11h helps WLANs share spectrum How? 801.11h implements two methods to help spectrum sharing: Dynamic Frequency Selection (DFS) Transmission Power Control (TPC) DFS is used to select the appropriate spectrum for WLAN TPC is used to manage WLAN networks and stations for Reduction of interference , Range control (setting borders for WLAN) , and Reduction of power consumption (beneficial in laptop use e.g.)

IEEE 802.15.3a Multiband OFDM for Personal Area Network Wireless USB2.0 (480Mbps) at 5 meters distances Cognitive Radio - Plausible Application to UWB Regulation Very fast spectrum sculpting via OFDM technology with wide bandwidth 528MHz QoS Support QoS can be supported by controlling the number of sub-carriers

Hurdles in CR FCC Development Policies The process and rules governing how frequencies and waveforms are selected and approved for use by cognitive equipment must be addressed. Software Flexibility Interface with policy updates Real-life functionality CR devices are smart enough to understand user request and surrounding environments Network availability for CR Network needs to announce their availability to CR Flexible or Reconfigurable Hardware Requires a language and protocols for initial interfacing with software and validation for existing devices as policies change across time and space Software Architectures More dynamic than SCA

Predictions for Future Evolution Time SDR with high ASIC content Re-programmable for fixed number of systems Factory reprogrammable Increased use of reconfigurable hardware Limited reconfiguration by user Early cognition Mid-level cognition Cognitive radios 2005 2007 2010 Adaptive spectrum allocation

Just Remember This... “ The best way to predict the future is to invent it.” Alan Kay, Author

Jeffrey H. Reed Willis G. Worcester Professor of ECE and Deputy Director, Mobile and Portable Radio Research Group (MPRG) Authored book, Software Radio: A Modern Approach to Radio Engineering IEEE Fellow for Software Radio, Communications Signal Processing and Education Industry Achievement Award from the SDR Forum Highly published. Co-authored – 2 books, edited – 7 books. Previous and Ongoing SDR projects from DARPA, Texas Instruments, ONR, Mercury, Samsung, NSF, General Dynamics and Tektronix

Jeffrey H. Reed Contact Information: [email_address] Electrical and Computer Engineering MPRG 432 Durham Hall Blacksburg, VA 24061 (540) 231-2972

Charles W. Bostian Alumni Distinguished Professor of ECE and Director, Center for Wireless Telecommunications Co-author of John Wiley texts Solid State Radio Engineering and Satellite Communications. IEEE Fellow for contributions to and leadership in the understanding of satellite path radio wave propagation. Award winning teacher Previous and Ongoing CR projects from National Science Foundation, National Institute of Justice

Charles W. Bostian Contact Information: [email_address] Electrical and Computer Engineering Virginia Tech, Mail Code 0111 Blacksburg, VA 24061-0111 (540) 231-5096

Hardware Blocks Software Modules

Example: Simple AM Transmitter (1/2) Building Blocks All Blocks are each defined as objects “ Amp” - Gain Stage “ m” - Message Signal “ mix” - Multiplication Stage “ LO” - Local Oscillator “ FIR” - Filter Stage X ~ Amp m FIR

Example: Simple AM Transmitter (2/2) Connecting Building Blocks + 1 Amp µ X ~ FIR m H/W Interface The arrow is an object that connects the flow graph

Dyspan Sdr Cr Tutorial 10 25 Rev02

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