Post on 17-Jan-2016
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
Recent Results: Optimal Architecture
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What does it mean to “optimally” allocate resources?
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
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
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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
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
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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