Cost sharing with network coding in two-way relay networks

Ciftcioglu, Ertugrul Necdet; Sagduyu, Yalin E.; Berry, Randall A.; Yener, Aylin

doi:10.1109/allerton.2009.5394485

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…This work is in line with a variety of other recent papers that use game-theoretic methods to analyze network coding problems-treating either individual unicasts or individual nodes in the network as selfish decision makers [6], [10], [16], [23], [26], [28]. Most of these results are only applicable in restricted settings.…”

supporting

confidence: 78%

The price of selfishness in network coding

Marden

Effros

2009

2009 Workshop on Network Coding, Theory, and Applications

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Abstract-A game-theoretic framework is introduced for studying selfish user behavior in shared wireless networks. The investigation treats an -unicast problem in a wireless network that employs a restricted form of network coding called reverse carpooling. Unicast sessions independently choose routes through the network. The cost of a set of unicast routes is the number of wireless transmissions required to establish those connections using those routes. Game theory is employed as a tool for analyzing the impact of cost sharing mechanisms on the global system performance when each unicast independently and selfishly chooses its route to minimize its individual cost. The investigation focuses on the performance of stable solutions, where a stable solution is one in which no single unicast can improve its individual cost by changing its route. The results include bounds on the best-and worst-case stable solutions compared to the best performance that could be found and implemented using a centralized controller. The optimal cost sharing protocol is derived and the worst-case solution is bounded. That worst-case stable performance cannot be improved using cost-sharing protocols that are independent of the network structure.

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supporting

confidence: 78%

The price of selfishness in network coding

Marden

Effros

2009

2009 Workshop on Network Coding, Theory, and Applications

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“…Some of these consider games played between unicast flows (sessions) [22], [23], [24]; thus the model and concerns in these are quite from from our game model that assumes and utilizes the multicast nature of the traffic. Among prior work, [25], [26] are closest to ours, and also consider multicast traffic.…”

Section: Related Workmentioning

confidence: 99%

Capacity Allocation Games for Network-Coded Multicast Streaming

Anshelevich

Caskurlu

Kar

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract-In this paper we formulate and study a capacity allocation game between a set of receivers (players) that are interested in receiving multicast data (video/multimedia) being streamed from a server through a multihop network. We consider fractional multicast streaming, where the multicast stream from the source (origin-server) to any particular receiver (end-user) can be split over multiple paths. The receivers are selfish and non-cooperative, but must collaboratively purchase capacities of links in the network, as necessary for delivery of the multicast stream from the source to the individual receivers, assuming that the multicast stream is network coded. For this multicast capacity allocation (network formation) game, we show that the Nash equilibrium is guaranteed to exist in general. For a 2-tier network model where the receivers must obtain the multicast data from the source through a set of relay nodes, we show that the priceof-stability is at most 2, and provide a polynomial-time algorithm that computes a Nash equilibrium whose social cost is within a factor of 2 of the socially optimum solution. For more general network models, we show that there exists a 2-approximate Nash equilibrium, whose cost is at most 2 times the social optimum. We also give a polynomial time algorithm that computes a (2 + ϵ)-approximate Nash equilibrium for any ϵ > 0, whose cost is at most 2 times the social optimum. Simulation studies show that our algorithms generate efficient Nash equilibrium allocation solutions for a vast majority of randomly generated network topologies.

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“…In [7], it is observed that only one bit of queue information of the other source leads to significant improvement compared with no information for energy minimization in a bidirectional relay network where the relay applies network coding. This approach can be classified as reducing the bit-complexity [18] of message passing.…”

Section: Related Workmentioning

confidence: 99%

Distributed backpressure protocols with limited state feedback

Rager

Ciftcioglu

Yener

et al. 2011

2011 - MILCOM 2011 Military Communications Conference

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Abstract-The freedom and flexibility of wireless Mobile Adhoc Networks (MANETs) that make them extremely desirable for many military, emergency, and sensor network applications also present challenges for multiple layers in the network stack. Max-weight scheduling, also known as backpressure routing, is a cross-layer control algorithm that is well-known to be throughput optimal since it provides queue stability within the network for all traffic injection rates within the network's capacity. Despite its desirable properties like operating on instantaneous queue and channel states without requiring their statistics, max-weight scheduling relies on global knowledge of full system state information of the network. This is an overly optimistic assumption for most real-world implementations. In this work, we address this issue and develop a distributed max-weight scheduling algorithm in which nodes disseminate the necessary network state information for each to make the same optimal cross-layer control decision individually each time slot. We explore the idea of disseminating only a limited amount of state feedback for this algorithm and evaluate the subsequent impact on performance. We compare the distributed protocol to a centralized version that assumes nodes have global knowledge of this network state information, showing differences in performance from the increased overhead. We also introduce and evaluate an improvement to the algorithm that achieves better performance by dynamically adjusting values used to estimate queue backlogs in the limited state feedback scheduling algorithm.

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Cost sharing with network coding in two-way relay networks

Cited by 8 publications

References 22 publications

The price of selfishness in network coding

The price of selfishness in network coding

Capacity Allocation Games for Network-Coded Multicast Streaming

Distributed backpressure protocols with limited state feedback

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