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All Rights Reserved © Alcatel-Lucent 2006, ##### Design Issues for Wireless Networks Across Diverse and Fragmented Spectrum Collaborators: Bell Labs India: Supratim Deb, Kanthi Nagaraj Bell Labs USA: Piyush Gupta
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Mobile Data Explosion Will Result in Diverse & Fragmented Spectrum Examples: AT&T Spectrum in New York – 700MHz band, 800MHz, 1.7GHz and 2.1GHz Unlicensed Spectrum in US – DTV Whitespaces ( MHz), 2.4GHz and 5.1GHz
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Research Goal PHY + Sensing MAC + Spectrum Selection Routing + Flow Control Design a network stack that operates across 1) Fragmented and spatially varying spectrum with diverse propagation 2) Devices with different tunable center freq. and b/w range New Design Perspective Lot of research
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Designing MAC and above: Unique Challenges Devices can tune frequency and bandwidth Non-channelized system complex MAC No fixed interference graph Frequency dependent propagation Complicates spectrum allocation and MAC design No fixed communication graph Next-hop (hence, routing) depends on frequency Leakage power at a distance depends on frequency Adjacent channel interference (hence, guard band) varies with frequency Cross layer optimization is highly complex log f c Received Power at fixed distance, power (dB) slope = 20 Power Spectral Density All traditional network stack design issues require a fresh look.
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Interference Management Standard Approach: Measurement based interference map But, if the spectrum is diverse … Two devices may interfere only in certain frequency bands Design Principle: Generate different interference maps for different bands Ideally a single/small number of control channels Can use measurements over a single control channel P r (f 1 ) P r (f 2 ) 20 log (f 2 /f 1 ) Interference maps in a higher freq. band can be deduced from interference map in a lower band (and knowledge of ambient interference)
All Rights Reserved © Alcatel-Lucent 2006, ##### Agile and Efficient MAC for Unlicensed Access in TV Whitespaces and ISM Bands
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Gist of FCC Mandate Usable Free Spectrum: Unused TV channels between MHz for unlicensed portable access Varies from city to city Limit Interference to DTV receiver: Tx power: 16 dBm in adjacent band, 20 dBm elsewhere Out of Band Emission: Has to fall by 55 dB in the adjacent 6 MHz band Spectrum Mask
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Single Link Capacity With ACI Constraints Fundamental Question: Whats the optimal transmit power anyway? High power-> lower bandwidth Guard band required to prevent interference to the TV channel Low power -> reduced system capacity Observations: Optimal capacity is independent on center frequency For FCCs spectrum mask, the power cap is not too sub-optimal Design Implications: Choose fixed power (marginal loss in capacity) Ensure a minimum bandwidth (leakage depends on PSD) 20 dBm DTV Channel Freq 16 dBm DTV Channel Freq
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | MAC Design Considerations Fragmented spectrum + limitations on maximum tunable bandwidth of a radio implies Multi radio AP, single radio client (for cost considerations) Need to account for frequency dependent propagation Resilient to disruptions E.g. wireless microphones Evolution over WiFi Easy adoption path, quick time to market, can interoperate with ISM bands IEEE af standard already in progress
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Joint Spectrum Selection and Client Allocation (1,2,3,4,5,6,7)(1,2,3,4,5,6,7) (1,2,3,4,5,6,7)(1,2,3,4,5,6,7) (1,2,3,4,5,6,7)(1,2,3,4,5,6,7) (1,2,3,4,5,6,7)(1,2,3,4,5,6,7) Joint Spectrum Selection and Client Assignment is a non-trivial problem
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Capturing the Utility of a Band Important Observation: For most technologies, data rate/Hz depends on SNR as Data Rate / Hz = a × (SNR in dB) - b Client-1 Client-2 ASE = Data rate averaged over all clients / Hz = a × (SNR averaged over all clients in dB) – b SNR(f 1 ) SNR(f 2 ) 20 log (f 2 /f 1 ) ASE in f 1 can be generated for ASE in f 2 Design Implication: Two step ASE generation: Each AP generates ASE in control channel band. Use frequency dependence and ambient interference measurements to compute ASE in all bands.
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Proportional Fairness is Key for Whitespaces 6Mbps 2.4GHz 6Mbps 600MHz More likely that far away client will bring down performance of system Very simple design with minimal information overhead on top of ensures proportional fairness
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Simulation Results Comparison scheme White space selection algorithm is optimal Number of clients assigned per band is proportional to number of clients 60-90% improvement in total throughput Proportionally fair Frequency agnostic schemes do not work well!
All Rights Reserved © Alcatel-Lucent | Connecting the Next Billion 2010 | Making it Work in Practice Designed frequency translator with < 2 microsecond switching delay Dynamic range of MHz Integrates with a WiFi card on Sokeris box Extensive indoor trials done Waiting for experimental license for outdoor trials
DYNAMIC SPECTRUM ACCESS IN DTV WHITESPACES: DESIGN RULES, ARCHITECTURE AND ALGORITHMS Supratim Deb, Vikram Srinivasan, (Bell Labs India) Ritesh Maheshwari.
Wireless Networking in the TV Bands Ranveer Chandra Collaborators: Thomas Moscibroda, Srihari Narlanka, Victor Bahl, Yunnan Wu, Yuan Yuan.
Low-cost, Long-Range Connectivity over the TV White Spaces Ranveer Chandra Collaborators: Thomas Moscibroda, Victor Bahl, Ivan Tashev Rohan Murty (Harvard),
Korea Advanced Institute of Science and Technology Network Systems Lab. Wireless Communications: The Future Professor Song Chong Network Systems Laboratory.
Networking Devices over White Spaces Ranveer Chandra Collaborators: Thomas Moscibroda, Rohan Murty, Victor Bahl, Srihari Narlanka.
Cognitive Wireless Networking in the TV Bands Ranveer Chandra, Thomas Moscibroda, Victor Bahl Srihari Narlanka, Yunnan Wu.
Networked Systems Practicum Lecture 9 – Next Gen Radio Technologies 1.
Next Generation Wi-Fi: Networking over White Spaces Ranveer Chandra Collaborators: Victor Bahl, Thomas Moscibroda, Srihari Narlanka, Yunnan Wu, Yuan Yuan.
$ Allocating Dynamic Time-Spectrum Blocks in Cognitive Radio Networks Victor Bahl Ranveer Chandra Thomas Moscibroda Yunnan Wu Yuan.
October 2013 doc.: IEEE /1276r0 Xinzhou Wu, Qualcomm Inc.Slide 1Submission Authors: Date: Proposal for DSRC band coexistence.
Wireless Networking in the TV Bands Ranveer Chandra Collaborators: Victor Bahl, Thomas Moscibroda, Srihari Narlanka, Yunnan Wu, Yuan Yuan.
Multi-Channel Wireless Networks: Capacity and Protocols Pradeep Kyasanur and Nitin H. Vaidya University of Illinois at Urbana-Champaign.
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 5 Spectrum.
ACM VANET 2006 Cooperative Collision Warning Using Dedicated Short Range Wireless Communications Tamer ElBatt **, Siddhartha Goel Gavin Holland HRL Laboratories,
1 Spectrum Sharing in OFDM-Based Cognitive Radio Networks C. Rosenberg This work was done in collaboration with Dr. L. Le, Profs. P. Mitran & A. Girard.
New Opportunities in Wireless Communications New Opportunities in Wireless Communications Ali M Niknejad Robert W Brodersen Understanding and Increasing.
Berkeley Wireless Research Center Why Theorists and Implementers Should Work Together Bob Brodersen Dept. of EECS Univ. of Calif. Berkeley
1 Multi-Channel Wireless Networks: Capacity and Protocols Nitin H. Vaidya University of Illinois at Urbana-Champaign Joint work with Pradeep Kyasanur Chandrakanth.
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 7 Reconfiguration,
Improving Wireless Access Technologies Adam Wolisz Professor of EE&CS, Technische Universität Berlin, TKN Adjunct Professor, EE&CS Dept, UC Berkeley, BWRC.
15-744: Computer Networking L-21 Wireless in the Real World.
Newlans Use of Millimeter Waves LAN (mmwLAN) for Enterprise Applications IEEE 802 Tutorial November 11, Rosio Alvarez, Director OIT, U.Mass. End.
WINLAB Backhauling in TV White Space Narayan B. Mandayam ( joint work with Cyrus Gerami, Larry Greenstein, Ivan Seskar ) WINLAB, Rutgers University IEEE.
1 11 A Case for Adapting Channel Width in Wireless Networks Ranveer Chandra, Ratul Mahajan, Thomas Moscibroda, Ramya Raghavendra†, Paramvir Bahl Microsoft.
Providing Unlimited Wireless Capacity Bob Brodersen Berkeley Wireless Research Center Adaptrum, Inc and SiBEAM, Inc. Univ. of California, Berkeley.
HY539: Spring 2005 Wireless networks and mobile computing Lecture2: Radio Channel Issues Prof. Maria Papadopouli Assistant Professor Department of Computer.
Slide 1Thursday, June 30, /05/03 EMERGING TECHNOLOGIES IN WIRELESS Jack H. Winters Chief Scientist, Motia
Cross Layer Design -Suparna. Agenda Introduction Signaling Methods Architecture Applications Issues Proposed solutions Conclusion & Future.
WIRELESS MESH NETWORKS Ian F. AKYILDIZ* and Xudong WANG** * Georgia Institute of Technology BWN (Broadband Wireless Networking) Lab ** TeraNovi Technologies.
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