# Presentation: 406695

## Rapidly Deployable Broadband Communications for Disaster Response

- Charles W. Bostian, Scott F. Midkiff, Timothy M. Gallagher, Christian J. Rieser, and Thomas W. Rondeau

- Center for Wireless Telecommunications
- Virginia Tech
- Blacksburg, VA 24061-0111

- March 4, 2003

## Testbed for High-Speed ‘End-to-End’ Communications in Support of Comprehensive Emergency Management

- A Project of
**Virginia Tech’s Center for Wireless Telecommunications (CWT) **
**Science Applications International Corporation**
- Sponsored by the National Science Foundation(Digital Government Program Award 9983463)
- With additional support from the National Response Center (NRC)

- C.W. Bostian, PI, S.F. Midkiff, and W. M. Kurgan Co-PIs

## Disaster Communications Project Research Objectives

- Overall Objective: Develop a rapidly deployable high capacity backbone radio system that seamlessly links on-site communications systems and networks to each other and to the outside world.
- Technical Challenges: find short-lived radio paths of opportunity and compensate for the shortcomings of these paths at both the radio and the network levels to deliver optimum performance.

## The Situation Our Project Addresses – A disaster area several miles on a side in which almost all communications have been wiped out.

- Cell phones provide voice service but are quickly saturated.
- Landline connections exist at the perimeter of disaster area that could be accessed via high-speed wireless links.

**Responders need rapidly deployable broadband communications. On September 11, getting needed connectivity took about a week.**

## Our Project: System Concept

- Fiber or Wireless NetworkConnection

- 2-3 km range(to be extended)

**Hub**

- Hub “illuminates” disaster
- area with 120 Mbps
- connectivity using broadband wireless (originally 28 GHz commercial LMDS equipment).

- 120 Mbps

**Remote****Unit**

- 120 Mbps

**Disaster Area**

**Remote****Unit**

## System Overview

- VoIP (non-cellular) Wireless Telephones

- VoIP Wireless Telephones

## Why did we use the 28 GHz LMDS band?

*Remember wireless data technology in 1998!*

-  IEEE 802.11b was an 11 Mbps indoor LAN for the office market.
- Only one or two companies offered radio modems that worked above 100 Mbps. These were intended for satellite links and priced accordingly.
- Successful 155 Mbps transmission over a wireless link was an expensive laboratory demonstration.
- The Dot.Com Bubble was on and equipment manufacturers were selling everything they could possibly manufacture.  They were not very interested in the disaster relief market.

## With the IEEE 802.11x and 802.16 technology now available, we are in the process of changing our operating frequency to the 5 GHz UNII band and using Proxim Tsunami Radios. But we learned a lot about 28 GHz.

## 28 GHz works best with LOS but NLOS paths exist and will work. These “paths of opportunity”can come from any direction and differ significantly from each other in radio channel characteristics.

- Researchers disagree about their characteristics

## To study the broadband characteristics of 28 GHz bounce paths, we developed an impulse channel sounder using UWB technology.

## 28 GHz paths of opportunity involve rough surface scattering and produce a continuous distribution of multipath components.

- Power delay profiles for scattering by limestone and brick walls.The first waveform is the LOS signal and the second is the scattered signal. From C. L. Dillard, T. M. Gallagher, C. W. Bostian, D. G. Sweeney. “28 GHz scattering by brick and limestone walls,” *2003 IEEE Antennas and Propagation Society International Symposium*, Vol. 3, pp. 1024-1027

## Because of rough surface scattering, paths of opportunity can have significantly different channel characteristics.

- Calculated BER for Burruss path

- Calculated BER for Cowgill path

- Measurement Layout

- Calculated plots of BER versus Eb/N0 for paths whose power delay profiles we measured.

## We need a radio that is capable of finding these paths of opportunity and configuring itself to make optimal use of them.

- In addition, there is a human factors problem.

## Fire fighters and other first responders emphatically do not want a radio that requires hands-on operation or frequent adjustment by an expert.  They need a radio that is smart enough to find the best path of opportunity, configure itself for it, and close a link, all without human intervention.

- In other words, they need a cognitive radio.

## What is a Cognitive Radio?

- can adjust themselves to accommodate 	anticipated channels and environment

***Fixed radios***
- are set by their operators

***Cognitive radios***
- can sense their environment and learn how to adapt

**A cognitive radio is not necessarily a software radio!**

- Beyond adaptive radios, cognitive radios can handle unanticipated channels and environments
- Cognitive radios require:
-  Sensing
-  Adaptation
-  Learning
- Cognitive radios can maintain a quality of service (smart transmitter) and reduce interference to neighboring radios (smart receiver)

## How can we model a cognitive radio?

- Synthesize biological cognition model and genetic algorithms (GA)
**Fundamental principles evolve to optimize response through genetic **crossover
**Random discoveries and spontaneous inspiration occur through genetic **mutation

- Biological model of cognition
**Left brain processing – **“Logical”
**Right brain processing – **“Creative”

***resources***
**Environment**

***resources***
**Radio**

**Adaptation and Processing**
**    ****- Learning**

**Monitor and Control**
** ****- Exploit trends**

## Our Approach to Realizing a Cognitive Radio

** “****Controlled Chaos” **
**Allow evolution through unique solutions **
**Force compliance with  regulations**

- Use biologically-inspired genetic algorithms to control and adapt the radios

- System is designed in terms of chromosomes
- Represents radio parameters
- Channel model also encapsulated in a chromosome

- Like biological organisms, the radio will play and learn from current and past behavior

- DNA Strand, Gilbert – Apple, 2003
- Performing Feats of Bioinformagic
- http://www.apple.com/pro/science/gilbert/

## Cognitive Radio System Diagram

## WCGA and HMMs for Wireless Channels

- The WCGA uses CWT’s broadband sounder technology to observe the channel.

**Wireless Channel Genetic Algorithm (WCGA)**

**CWT’s Broadband Channel Sounder**

- The channel is then modeled by a Hidden Markov Model (HMM) that is compact and computationally efficient.

## WCGA and HMMs for Wireless Channels

**Hidden Markov Model**

**Radio Adaptation – Improved performance over time**

**Channel Impulse Response**

**Burst Error Length**

**Frequency of Burst Errors**

**Amplitude (V)**

**Time (ns)**

## CSM and WSGA for Intelligent Radio Adaptation

**Wireless System Genetic Algorithm**
**(WSGA)**

**Cognitive System Monitor (CSM) **
**(****Learning Classifier,** **Meta-GA, Memory, Control****)**

- The WSGA takes the channel model and information from the CSM to adapt the radio using a Genetic Algorithm.

- The CSM synthesizes channel model and adaptation process and better directs radio evolution from observed and learned behavior.

## Current Work – Proxim Tsunami Radios

- Adapting with limited range of adaptable parameters:
-  Modulation: QPSK, QAM8, QAM16
-  Power: 6 dBm – 17 dBm
-  Frequency: See figure on left
-  Uplink/Downlink ratio

- Even with this limited-flexibility legacy radio, we can use our cognitive processes to adapt the radio, including the avoidance of an interferer.

**Frequency Channels available to Proxim Tsunamis**

**Interference Test setup**

**Network Base Station Unit**

**Network Subscriber Unit**

**Interfering Unit**

## Cognitive Radio Testbed Demo

## Cognitive Radio Testbed Link

- Cognitive Radio Testbed Demo

- Interferer Degrades Broadband Wireless Link

- WSGA evolves radio operation

- Link quality of service (QOS) is restored even in presence of interferer

- Interferer

- Broadband Wireless Link

- Interferer
- Tx=11dBm
- QAM 16
- Fibs 12
- 3/4 FEC
- Freq=b-5

- Link
- Tx=6dBm
- QAM 16
- Fibs 22
- 3/4 FEC
- Freq=a-5

- Link
- Tx=16dBm
- QPSK 4
- Fibs 4
- 1/2 FEC
- Freq=a-5

## Future Work: Building A More Complete Cognitive Radio

- Build an adaptive radio
- 88 MHz – 6 GHz
**100 Mbps data link**
- Programmable power and diversity** techniques**
**Arbitrary signal constellations for increased modulation capability**
- Programmable MAC layer** for timing, payload sizes, and connection maintenance**

**Extend cognitive radio algorithms**
- Include **MAC layer** adaptation using **economic** market analysis models
**Improve sensing, modeling, and learning**

## Future Work: Cognitive Radio Network

-  Extend cognitive radio techniques to build network test bed
**Analyze cognitive radio network coordination via **economic theory
-  Investigate adaptation mechanisms at MAC layer
**Apply **genetic algorithm** approach to MAC/Data Link Layer**
**Provide a requested **quality of service
- Enable dynamic spectrum access

- Gene Splicing Presentation
- Biotechnology Online
- http://www.biotechnology.gov.au/biotechnologyOnline/interactives/gene_splicing_interactive.htm

## References

-  C.W. Bostian, S. F. Midkiff, W. M. Kurgan, L. W. Carstensen, D. G. Sweeney, and T. Gallagher, “Broadband Communications for Disaster Response”, *Space Communications*, Vol. 18 No. 3-4 (double issue), pp. 167-177, 2002
-  C.L. Dillard, T.M. Gallagher, C.W. Bostian, and D.G. Sweeney, “Rough Surface Scattering From Exterior Walls at 28 GHz,” *IEEE Trans. Antennas and Propagation*, in press.
-  T.W. Rondeau, C.J. Rieser, T.M. Gallagher, and C.W. Bostian. “Online Modeling of Wireless Channels with Hidden Markov Models and Channel Impulse Responses for Cognitive Radios,” 2004 International Microwave Symposium

## This work was supported by the National Science Foundation under awards 9983463 and DGE-9987586.

- Charles W. Bostian 	 	bostian@vt.edu
- Scott F. Midkiff			midkiff@vt.edu
- Timothy M. Gallagher 		tgallagh@vt.edu
- Christian J. Rieser 		cjrieser@vt.edu
- Thomas W. Rondeau 		trondeau@vt.edu

- http://www.cwt.vt.edu
- 540-231-5096

- This work was supported by the National Science Foundation under awards 9983463 and DGE-9987586.