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SINR Based Topology Control for Multihop Wireless Networks with Fault Tolerance

机译:基于SINR基于具有容错的多跳无线网络的拓扑控制

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In this paper, an optimal centralized approach to topology control (TC) is adopted where the network topology is established considering interference, k-connectivity and routing constraints. This optimization problem however involves link scheduling and power assignment under SINR constraint, which is an NP hard problem even for a small number of nodes. An exact solution beyond six nodes has not been found so far. Opting for heuristics rather than exact approach, the proposed algorithms in the literature, either cannot guarantee the quality of the solution, or approximate the interference (protocol interference model) rather than using realistic SINR models. Here, at first we present a novel formulation for the optimal solution and analyse its limits. We then propose a novel approximation algorithm using column generation (CG) together with knapsack transformation on the SINR constraint. Particle Swarm Optimization (PSO) is integrated into the CG, to provide robust initial feasible patterns. The results show that, CG-PSO with knapsack transformation increase the solvable instances three fold in terms of number of nodes, in comparison to the state-of-art approaches. The links are scheduled with less power and shorter scheduling lengths,while the proposed algorithm also reduces the computation time at lower penalty cost.
机译:在本文中,采用了考虑干扰,k连接和路由约束的网络拓扑结构的最佳集中式控制(TC)。然而,这种优化问题涉及在SINR约束下的链路调度和功率分配,即使对于少量节点,也是NP难题。到目前为止,还没有找到超过六个节点的精确解决方案。选择启发式,而不是精确的方法,所提出的文献中的算法,要么不能保证解决方案的质量,或近似干扰(协议干扰模型),而不是使用逼真的SINR模型。在这里,首先我们提出了一种用于最佳解决方案的新型制剂并分析其限制。然后,我们将使用列生成(CG)的新颖近似算法与SINR约束上的背包变换一起。粒子群优化(PSO)集成到CG中,以提供强大的初始可行模式。结果表明,与现有技术的方法相比,CG-PSO与背包变换增加了节点数量三倍的可溶性实例。该链路调度较少的功率和更短的调度长度,而所提出的算法也以较低的惩罚成本降低计算时间。

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