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Illinois Center for Wireless Systems Wireless Networks: Algorithms and Optimization R. Srikant...
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![Page 1: Illinois Center for Wireless Systems Wireless Networks: Algorithms and Optimization R. Srikant ECE/CSL.](https://reader035.fdocuments.net/reader035/viewer/2022081603/5697bfab1a28abf838c9af0a/html5/thumbnails/1.jpg)
Illinois Center forWireless Systems
Wireless Networks: Algorithms and Optimization
R. SrikantECE/CSL
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Researchers & Selected TopicsTopics
Tamer BasarChris Hadjicostis
Bruce HajekJennifer HouP. R. KumarSean Meyn
R. S. SreenivasR. Srikant
Mathematical Tools
Cross-layer design Optimization
Power Control Distributed Algorithms
Distributed MAC Information Theory
Multi-channel, multi-antenna protocols
Game Theory
Performance Analysis
Systems & Control
Security Stochastic Processes
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Technical Approach
Establish fundamental limits of performance of single-hop
and multi-hop wireless networks
Translate algorithms into practical protocols for
wireless networks accounting for overhead, complexity
Design distributed algorithms to achieve or approximate the
above performance limits
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Example Project: Clean-Slate Design
Sponsor: NSF Organizations: UIUC, Princeton, Texas-Austin,
Purdue, Ohio State Goal: Clean-state design of wireless networks
Is there an optimal network architecture? Should it be layer separated (PHY, MAC, network, transport, etc.) or cross-layered?
Are there near-optimal architectures that tradeoff between efficiency, robustness, signaling overhead, complexity, etc.?
Develop methodologies to evaluate alternativesKey difficulty: No notion of a reliable bit-pipe
between a pair of nodes
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Example Project: From Theory to Practice
Sponsor: DARPA Companies: Lockheed-Martin, Alcatel-Lucent Universities: UIUC, Stanford, Princeton, UCSB Pose the problem of fair, efficient resource
allocation as a convex optimization problemObtain a solution using dual decomposition theoryFind approximate solutions to optimal routing, power
control and MAC algorithmsDevise low-overhead signalling protocol to enable
implementation of approximately optimal algorithms Implement in a 30-50 node MANET
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Example Project: Security & Trustworthiness
Sponsor: Motorola Organizations: UIUC Design algorithms for identifying and isolating
misbehaving users in multi-hop wireless networks and devise incentive mechanisms to encourage cooperative behaviorAlgorithms have to be distributedNo single user should have an incentive to deviate
from socially responsible behaviorRobust to coalitionsLow overhead
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Recent Results: Optimal Architecture
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What does it mean to “optimally” allocate resources?
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Unlike TCP, source does not react to end-to-end congestion; instead hop-by-hop congestion control
Congestion Control for Flow f : Decrease queue length if ingress queue length is small
Ingress
Queue length
20 60 80 20Weights=Backpressures
-40 -20 60
Cross-layer solution is optimal: power, time slots, routing are all allocated at the same time scale
On the other hand, layering is beneficial for proliferation: can minimal coupling of functionality among layers reap most of the performance benefits?
Optimal Solution
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Recent Results: Scheduling & QoS
Problem: Given QoS constraints on the packet delay at the router, what is the optimal scheduling policy?
Scheduling policy can use queue length, delay of HoL packet, channel conditions in making a decision
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Key Result
Using queue-length or head-of-line packet delay information can dramatically improve performance
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QLB: Queue-length based scheduling policy
Greedy: Schedule the user with the best channel
Number of users
Netw
ork
Th
rou
ghp
ut
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Recent Results: Enforcing Cooperation
Problem: Detect misbehaving nodes and provide incentives for all nodes to act in a socially responsible manner
Detecting misbehavior is difficult in wireless networks
Example:C asks D to send a packet to EWhen D transmits to E, B transmits to AD E transmission is successful, but C does not
know
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A B C D E
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Game-Theoretic Solution
DARWIN: Distributed, Adaptive Reputation mechanism for WIreless Networks Collect each node’s reputation
based on its forwarding behavior; errors will occur occasionally
Punishment for misbehavior: Tit-for-tat strategy
Accounting for errors: Be contrite (accept punishments assuming that they are due to error)
Above behavior is optimal from a game-theoretic point-of-view: deviation from cooperation is not fruitful
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Can be incorporated into 802.11 or other protocols
Overhead is fixed, independent of network load
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Summary
Design of optimal architectures and algorithms for wireless networks
Develop new theory and translate it into practice
Theory-driven protocol stack design can lead dramatic gains in network performance
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