On Cooperative Wireless Techniques On Cooperative Wireless - - PowerPoint PPT Presentation

On Cooperative Wireless Techniques On Cooperative Wireless Techniques

A WINLAB Research Sampling A WINLAB Research Sampling

Predrag Predrag Spasojevi Spasojević

ć

Industrial Advisory Board Meeting, December 4, 2007 WINLAB, Rutgers University WINLAB, Rutgers University

• Communication Reliability

Communication Reliability

• Message Confidentiality

Message Confidentiality

• Efficient Sensing

Efficient Sensing

• Efficient Data Collection

Efficient Data Collection

• Efficient Spectrum Usage

Efficient Spectrum Usage

• Spectrum Usage Analysis

Spectrum Usage Analysis

• Interference Mitigation, and

Interference Mitigation, and … …

Collaborate to Enable Collaborate to Enable

Cooperative Techniques for Ensuring Cooperative Techniques for Ensuring Communication Reliability and Message Confidentiality Communication Reliability and Message Confidentiality

• Trusted

users can cooperate to defend adversarial eavesdropping.

Transmitter Cooperation for Confidentiality Transmitter Cooperation for Confidentiality

1

W

2

W

Reliability Confidentiality

Xiaojun Tang, Predrag Spasojevic, Xiaojun Tang, Predrag Spasojevic, Ruoheng Ruoheng Liu, and H. Vincent Poor Liu, and H. Vincent Poor

Receiver ARQ Feedback Improves Receiver ARQ Feedback Improves Reliability and Message Confidentiality Reliability and Message Confidentiality

Secrecy Reliability

User 1 User 2 Transmitter W ^

W

W

• Simple retransmission may hurt secrecy.

Can retransmission benefit secrecy? Yes! If done properly.

Xiaojun Tang, Predrag Spasojevic, Xiaojun Tang, Predrag Spasojevic, Ruoheng Ruoheng Liu, and H. Vincent Poor Liu, and H. Vincent Poor

Eavesdropper average SNR: 5dB Target secrecy outage prob.: = 0.001 Maximum # of transmissions: M=8

s

ζ

Secrecy Throughput v.s. Main SNR Secrecy Throughput v.s. Main SNR

RTD may outperform INR, when there is a reliability outage constraint

Target reliability outage prob.: =0.001 e

ζ

10 11 12 13 14 15 16 17 18 19 20 0.5 1 1.5 2 2.5

INR RTD

Main SNR Throughput Xiaojun Tang, Predrag Spasojevic, Xiaojun Tang, Predrag Spasojevic, Ruoheng Ruoheng Liu, and H. Vincent Poor Liu, and H. Vincent Poor

Receiver ARQ Feedback Improves Receiver ARQ Feedback Improves Reliability and Message Confidentiality Reliability and Message Confidentiality

RTD – Repetition Time Diversity INR – Incremental Redundancy

s

ζ

Collaborative Sensing and Data Collection Collaborative Sensing and Data Collection

BT node WLAN nodes

Goran Ivkovic, Predrag Spasojevic, Predrag Spasojevic, and Ivan Seskar

Collaborative Radio Scene Analysis

sensors

• ne Bluetooth and two 802.11b Nodes

Radio Scene Analysis: Spectrogram Reconstruction

BT packets WLAN packets

before recovered

Goran Ivkovic, Predrag Spasojevic, Predrag Spasojevic, and Ivan Seskar

Radio Scene Analysis: Recovery of Transmitter Activity Patterns and PSDs WLAN BT

Goran Ivkovic, Predrag Spasojevic, Predrag Spasojevic, and Ivan Seskar

R

2

R

1

Data Collection in Location-Unaware Networks

collaborative distributed dissemination infrastructure building a circular route infrastructure model Silvija Kokalj-Filipovic, Roy Yates, Predrag Spasojevic, Predrag Spasojevic,

data dissemination and storage:

1 n 3 2

random access push-pull data collection

Silvija Kokalj-Filipovic, Predrag Spasojevic, Predrag Spasojevic, Roy Yates

Coding for Data Storage and Random Access Collection

relaying, overhearing, and random coding strategies for efficient decoding

push: immediate neighborhood access pull: search for desired coded packets

How many coded packets do I need to pull to decode all data?

500 1000 1500 2000 2500 3000 3500 4000 10 20 30 40 50 60 70 80 90 100

n: number of symbols to decode percentage od doping symbols LEGEND D: Fountain with Degree-2 Doping U: uniform doping D min DP D mean DP D max DP U min DP U mean DP U max DP

Coding for Data Storage and Random Access Collection

Silvija Kokalj-Filipovic, Predrag Spasojevic, Predrag Spasojevic, Roy Yates Random access push-pull data collection and decoding random packet pull “smart” packet pull

Collaborative Signal Processing in Energy Replenishable Sensor Networks

Jing Lei, Roy Yates and Larry Greenstein

E B D A C

Sources Available for Sensor Replenishment

• mechanical energy
• thermal energy
• radiant energy
• electromagnetic energy

α: replacement

1 2 4 55

β

β

β

+ α β

5,1

λ

5,2

λ

5,3

λ

5,4

λ

5,5

λ 3

recharging β :

Energy Level high low

Energy-Aware Transmission Policy Modeled by Markov Chain

• Collaborative MMSE Estimation of Gaussian Markov

Processes by Replenishable Sensors

Jing Lei, Roy Yates and Larry Greenstein

• Collaborative Detection Based on Kullback

Leibler Distance by Replenishable Sensors

Jing Lei, Hang Liu (Thomson CR), Roy Yates and Larry Greenstein

– Each sensor calculates histogram of observations – K-L distance between observation histogram and underlying pdf is calculated – Soft-decision based on quantized K-L distance

Will Radios Collaborate? And Will Radios Collaborate? And How to Encourage Collaboration? How to Encourage Collaboration?

Coalitions in Cooperative Networks

• Users may prefer to form coalitions.
• A cooperative protocol is stable

if no subset of users defects to form a coalition

• Do stable forms of cooperation always exist?

Why should rational self-interested users cooperate?

– Do all users gain (greater rate) from cooperation? – Can users gain more by cooperating only selectively? – What if there are costs to cooperation? (e.g. decode other users’ signals) Suhas Mathur, Lalitha Sankara Narayanan and Narayan B. Mandayam

Coalitions in Cooperative Networks

• Receiver cooperation

– The grand coalition (coalition of all user) always forms – Bargaining theory can be used to guarantee fair allocations of rate to users.

• Transmitter cooperation with ideal inter-user links

– User are deterred fro defection by the threat of jamming interference from other users – Grand coalition is the only possible stable structure but doesn’t always form

• User cooperation using partial-decode-and-forward

– Even in situations where cooperation is most expected (clustered users in a MAC) the coop. of all users cannot be guaranteed – Depends on strength of inter-user links and the powers of individual users Suhas Mathur, Lalitha Sankara Narayanan and Narayan B. Mandayam

A node delegates a portion of its bandwidth to relay in exchange for cooperation. Nash bargaining solution to efficiently and fairly explore the advantages of bandwidth exchange.

Bandwidth Exchange as an Incentive for Relaying

Dan Zhang and Narayan B. Mandayam

To Collaborate or Not? To Collaborate or Not? for Spectrum Access and Interference Mitigation for Spectrum Access and Interference Mitigation

Dynamic Spectrum Allocation for Uplink Users with Heterogeneous Utilities

• Utility based spectrum allocation
• Multiple users and SPs
• SPs allocate spectrum to users
• User applications: utility functions of

spectrum

• Users transmit to SPs (uplink)
• SPs have different efficiencies
• User sum utility maximization
• Distributed implementation
• SPs charge users spectrum price
• Users maximize utility minus spectrum

costs

Spectrum Regulator (Govt. org. like FCC) Service Providers (SP) (Base Stations) End Users Level I SPs share spectrum amongst themselves Level II SPs provide spectrum to end users

Xi

xij pij rij

Joydeep Acharya, Roy D. Yates

Optimal Spectrum Allocation

At optimal price all spectrum is used A user obtains spectrum from only one SP Each user is allocated spectrum User with higher marginal utility gets more User with better link gain/power gets more More users imply higher spectrum price Joydeep Acharya, Roy D. Yates

Dynamic Spectrum Allocation for Uplink Users with Heterogeneous Utilities

• Spectrum server devises a centralized time schedule for the links to:

– maximize sum rate of the network – implement fair scheduling – implement energy efficient scheduling – meet a given set of rate requirements on the links

• Centralized scheduling provide upper bounds to system performance

1 4 3 2 1 3

Coordinated vs Distributed Access Scheduling for Variable Rate Links

Chandrasekharan Raman, R. Yates, N. Mandayam

• Each link transmits with a chosen probability (independent of other links)
• Average link rates depends on the interference from the other link
• Provide a decentralized algorithm to achieve a desired rate vector

Rates for low interference case = same as rates in centralized scheme r1 r2 r1 r2 High interference case = strictly smaller than rate in centralized scheme

Chandrasekharan Raman, Jasvinder Singh, R. Yates, N. Mandayam

Coordinated vs Distributed Access Scheduling for Variable Rate Links

Coordinate the base antenna transmissions so as to minimize the inter-cell interference, and hence to increase the downlink system capacity.

Cooperative Base Station Transmissions with Multiple Antennas

• M. Kemal Karakayali, J. Foschini, R. Valenzuela, Roy Yates

Cooperate to Capture Spatial Diversity

• Ruoheng

Liu, P. Spasojevic, E. Soljanin

• Lalitha

Sankara Narayanan, G. Kramer, N. Mandayam

Cooperate to Capture Wireless Broadcast Energy

• Ivana

Maric, R. Yates

• Ruoheng

Liu, P. Spasojevic, E. Soljanin

Bandwidth Exchange as an Incentive for Relaying Dan Zhang and Narayan B. Mandayam A node delegates a portion of its bandwidth to relay in exchange for cooperation. Nash bargaining solution to efficiently and fairly explore the advantages of bandwidth exchange. Cooperative Base Station Transmissions with Multiple Antennas

• M. Kemal Karakayali, Roy Yates

Coordinate the base antenna transmissions so as to minimize the inter-cell interference, and hence to increase the downlink system capacity. Coordinated Scheduling vs Random Access for Variable Rate Links Chandrasekharan Raman, Jasvinder Singh, R. Yates, N. Mandayam Rate regions of centralized scheduling vs probabilistic random access for end-to- end flows in a network of interfering links. Convergence of distributed scheduling algorithms.

A Framework for Dynamic Spectrum Sharing between Cognitive Radios Joydeep Acharya, Roy D. Yates Suggests spectrum price as a regulatory mechanism that brings about coordination amongst the service providers with minimal control messaging. Profit Maximizing Pricing Strategies for Dynamic Spectrum Allocation Joydeep Acharya, Roy D. Yates Users decide how much spectrum to buy from a Service provider based on the price and the utility which the purchased spectrum would yield.