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Cross-Layer Throughput Optimization in Cognitive Radio Networks with SINR Constraints
Author(s) -
Miao Ma,
Danny H. K. Tsang
Publication year - 2010
Publication title -
international journal of digital multimedia broadcasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.164
H-Index - 17
eISSN - 1687-7586
pISSN - 1687-7578
DOI - 10.1155/2010/985458
Subject(s) - computer science , throughput , cognitive radio , heuristic , rounding , interference (communication) , mathematical optimization , relaxation (psychology) , optimization problem , integer programming , nonlinear programming , set (abstract data type) , channel (broadcasting) , computer network , algorithm , nonlinear system , telecommunications , wireless , mathematics , psychology , social psychology , physics , quantum mechanics , artificial intelligence , operating system , programming language
Recently, there have been some research works in the design of cross-layer protocols for cognitive radio (CR) networks, where the Protocol Model is used to model the radio interference. In this paper we consider a multihop multi-channel CR network. We use a more realistic Signal-to-Interference-plus- Noise Ratio (SINR) model for radio interference and study the following cross-layer throughput optimization problem: (1) Given a set of secondary users with random but fixed location, and a set of traffic flows, what is the max-min achievable throughput? (2) To achieve the optimum, how to choose the set of active links, how to assign the channels to each active link, and how to route the flows? To the end, we present a formal mathematical formulation with the objective of maximizing the minimum end-to-end flow throughput. Since the formulation is in the forms of mixed integer nonlinear programming (MINLP), which is generally a hard problem, we develop a heuristic method by solving a relaxation of the original problem, followed by rounding and simple local optimization. Simulation results show that the heuristic approach performs very well, that is, the solutions obtained by the heuristic are very close to the global optimum obtained via LINGO. © 2010 M. Ma and D. H. K. Tsang

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