Reverse-Engineering MAC: A Non-Cooperative Game Model

Lee, Jang-Won; Tang, Ao; Huang, Jianwei; Chiang, Mung; Robert, Alain

doi:10.1109/jsac.2007.070808

Cited by 79 publications

(38 citation statements)

References 16 publications

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“…Four decades have elapsed since the publication of Abramson's Aloha system [10]. In the interim we can cathegorize the proposed MAC algorithms into two types [11]: Random Access (RA) algorithms and Scheduling-Based (SB) algorithms. While centralised SB algorithms are contention-free (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this class of problems, the optimal solution is then relaxed into an implementable algorithm. This is the case for [11] and [14], where the relaxation process decreases the final optimization efficiency, as several approximations are used to translate the optimal solution into a practical algorithm. Moreover, these algorithms require extensive message passing among the nodes, and consequently wasting a significant amount of throughput.…”

Section: Related Workmentioning

confidence: 99%

“…A -It is possible to design a MAC scheme where a node n y uses its own medium access probability s i ¼ s y in (12) to determine the optimal access probability (s H given by (11)) and simultaneously guarantee that its medium access probability converges to the optimal value.…”

Section: Estimation Of the Number Of Nodesmentioning

confidence: 99%

“…When the contention period k [ 0 finishes, a node uses the expression (12) with the measurep a and s i ¼ s init to obtain the estimate for the number of competing nodes (nðkÞ). ThennðkÞ is used as an argument in (11) to obtain a reference for the medium access probabilitysðkÞ, which is later imposed by the controller CðsðkÞÞ. The controller output sðk þ 1Þ is then used to define the contention window…”

Section: Contention Window Regulationmentioning

confidence: 99%

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MyopicMAC: A Throughput-Optimal Random Access Scheme for Distributed Wireless Networks

Oliveira

Luís

Bernardo

et al. 2015

Wireless Pers Commun

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This work presents a novel Medium Access Control (MAC) algorithm for single-hop distributed wireless networks, MyopicMAC, which is designed to maximize the network throughput performance. In our scheme, all nodes adopt a common optimal contention window avoiding message passing, which is obtained through the use of the common view of the channel. MyopicMAC is also designed to improve the medium access fairness. This goal is achieved by adopting a contention window similar for all nodes. Departing from the optimal throughput for a saturated network, we devise a scheme to estimate the number of nodes, which is a prime parameter to regulate the medium access control. It is shown that each node is able to estimate the number of competing nodes by using both its own medium access probability and the idle slot probability observed in the channel. Several simulation results evaluate the throughput, fairness and delay performance obtained with our proposal. The possibility of transmission errors is also taken into account. A comparison is made with several state-of-the-art MAC algorithms, different number of competing nodes and different behaviors expressed by different transmission error probabilities. The obtained results indicate that MyopicMAC performs closer to the optimal throughput and simultaneously exhibit high medium access fairness. Moreover, because MyopicMAC does not take the success of previous transmissions in the contention regulation, the performance of the MAC algorithm is not affected by transmission errors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Estimation Of the Number Of Nodesmentioning

confidence: 99%

Section: Contention Window Regulationmentioning

confidence: 99%

See 2 more Smart Citations

MyopicMAC: A Throughput-Optimal Random Access Scheme for Distributed Wireless Networks

Oliveira

Luís

Bernardo

et al. 2015

Wireless Pers Commun

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“…Here, we only mention a few that are most closely related to the work. Lee et al reverse‐engineered backoff‐based random‐access MAC protocols in ad hoc networks, and demonstrated that the contention resolution algorithm in such protocols is implicitly participating in a noncooperative game. Cui et al extended the random access game model to a network with multiple contention measurement signals, studied the design of random access games, and analyzed different distributed algorithms to achieve equilibria.…”

Section: Introductionmentioning

confidence: 99%

A vehicle's weight‐based prioritized reciprocity MAC

Lang

Wang

Mei

et al. 2019

Trans Emerging Tel Tech

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In vehicular ad hoc networks, vehicles compete for channel access through the medium access control protocol to transmit messages. Most traditional medium access methods assign the same probability to all vehicles accessing the channel and ignore the intrinsic attributes of different vehicles. Attributes regarding the position and role of a vehicle are important factors to be considered when a vehicle is competing for the channel. In this paper, we introduce a new notion of a weight factor in a fair medium access game and propose a game model to adjust each node's channel access probability with complete information in the vehicular environment based on the weight factors of vehicles. The weight of a vehicle represents a specific property, such as the distance between vehicles, the vehicle's role, and so on, which can be used to distinguish priorities of different vehicles during channel competition. We also provide instances of relevant applications by using the distance between vehicles and a vehicle's role. Then, we analyze the incomplete information game in which each node cannot acquire the previous channel access probabilities of other nodes. Moreover, we construct a distributed best-response learning algorithm based on the medium access game model and demonstrate that the game can converge to a unique and stable equilibrium under certain conditions. Furthermore, we investigate some performance indicators such as channel access delay and normalized throughput and perform extensive numerical analyses compared with the IEEE 802.11p protocol and an existing game-based medium access control algorithm without consideration of weights. The performance comparison of high-priority nodes and all nodes in a network is also studied. Finally, we analyze the effects of the proportion of high-priority nodes on the performance of the channel. The performance improvement after introducing the weight factor is verified by these numerical results.Trans Emerging Tel Tech. 2019;30:e3654.wileyonlinelibrary.com/journal/ett

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Network Optimization Theory

Glisic

Lorenzo

2009

Advanced Wireless Networks

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In the past, network protocols in layered architectures have been obtained on an ad hoc basis, and many of the recent crosslayer designs are also conducted through piecemeal approaches. It is only recently that network protocol stacks have instead been analyzed and designed as distributed solutions to some global optimization problems in the form of generalized network utility maximization (NUM), providing insight on what they optimize and on the structures of the network protocol stacks. This chapter will present material required for understanding of layering as optimization decomposition where each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems.Decomposition theory provides the analytical tool for the design of modularized and distributed control of networks. This chapter will present results of horizontal decomposition into distributed computation and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control and channel coding. Key results from many recent works are summarized and open issues discussed. Through case studies, it is illustrated how layering as optimization decomposition provides a common framework for modularization, a way to deal with complex, networked interactions. The material presents a top-down approach to design protocol stacks and a mathematical theory of network architectures.Convex optimization has become a computational tool of central importance in engineering, thanks to its ability to solve very large, practical engineering problems reliably and efficiently. Many communication problems can either be cast as or be converted into convex optimization problems, which greatly facilitate their analytic and numerical solutions. Furthermore, powerful numerical algorithms exist to solve the optimal solution of convex problems efficiently.For the basics in the area of convex optimization, the basics of convexity, Lagrange duality, distributed subgradient methods and other solution methods for convex optimization, the reader is referred to References [1] to [8].Advanced Wireless Networks: Cognitive, Cooperative and Opportunistic 4G Technology Second Edition Savo Glisic and Beatriz Lorenzo

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Reverse-Engineering MAC: A Non-Cooperative Game Model

Cited by 79 publications

References 16 publications

MyopicMAC: A Throughput-Optimal Random Access Scheme for Distributed Wireless Networks

MyopicMAC: A Throughput-Optimal Random Access Scheme for Distributed Wireless Networks

A vehicle's weight‐based prioritized reciprocity MAC

Network Optimization Theory

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