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Cross layer design for wireless multi-hop Networks
Ao Weng Chon R97942044
Abstract—We focus on considering the joint optimization of rate and reliability. It forms a cross layer design between transport layer (congestion control) and physical layer (adaptive coding).There is an intrinsic trade-off between rate and reliability. Through scalarization between these two objectives, we can obtain a Pareto optimal trade-off curve. We have different recourses allocation constraints among the two classes of users. The cross layer design problem can be solved distributively.The performance and complexity of the algorithmwill also be justified.
Index Terms—Cross layer optimization, rate allocation, physical –layer channel coding.
I.INTRODUCTION
Facing the problem of scarcity of spectrum recourse in unlicensed band, several experiments have conducted and show that underutilization of licensed band. Cognitiveradio is a device that can sense the spectrum, opportunistically access the spectrum hole and initiate transmission aware of interference with the licensed users (primary users).Cognitive radio networks consist of cognitive radio users (secondary users).In such environment, the main challengesare the avoidance of interfering primary users and the coexistence of secondary users. We want to limit the resources delivering to secondary users and perform network utility optimization among these two classes of users.
II.Problem formulation
In this report we use Network Utility Maximization (NUM) Framework to study the joint assignment of rate and reliability among sources in multi-hop wireless network. Formulating the above problem in the NUM framework can lead to natural functional decomposition into layers and distributed algorithms. The optimality is preserved through messages passing between layers and local messages exchanging among nodes. The framework reasons (joint) cross layer design. Heuristic layering turns out to be solving a NUM optimization problem. This is so called “Layering as optimization decomposition”. Especially, our problem leads tocongestion control at transport layer and the adaptive coding at the physical layer. The messages passing between layers are congestion prices (Lagrange multipliers). Updating is performed distributively using local messages.
III.Time table
Paper survey: between 4/24~5/8.
Problem formulation: between 5/15~5/22
Problem solving and simulation: between 5/29~6/12
IV.Time table
Paper survey: between 4/24~5/8.
Problem formulation: between 5/15~5/22
Problem solving and simulation: between 5/29~6/12
V.Time table
Paper survey: between 4/24~5/8.
Problem formulation: between 5/15~5/22
Problem solving and simulation: between 5/29~6/12
VI.Time table
Paper survey: between 4/24~5/8.
Problem formulation: between 5/15~5/22
Problem solving and simulation: between 5/29~6/12
References
[1]Yi Shi; Hou, Y.T., "A Distributed Optimization Algorithm for Multi-Hop Cognitive Radio Networks," INFOCOM 2008. The 27th Conference on Computer Communications. IEEE , vol., no., pp.1292-1300, 13-18 April 2008
[2]Long Le; Hossain, E., "QoS-Aware Spectrum Sharing in Cognitive Wireless Networks," Global Telecommunications Conference, 2007. GLOBECOM '07. IEEE , vol., no., pp.3563-3567, 26-30 Nov. 2007
[3]Mung Chiang; Low, S.H.; Calderbank, A.R.; Doyle, J.C., "Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures," Proceedings of the IEEE , vol.95, no.1, pp.255-312, Jan. 2007
[4]Palomar, D.P.; Mung Chiang, "A tutorial on decomposition methods for network utility maximization," Selected Areas in Communications, IEEE Journal on , vol.24, no.8, pp.1439-1451, Aug. 2006