一种最大化网络吞吐量的认知无线Ad Hoc网络跨层优化算法

A Cross-Layer Throughput-Maximization Algorithm for Cognitive Wireless Ad hoc Networks

认知无线Ad hoc网络(cognitive wireless ad hoc networks)是由一组具有认知决策能力的节点以多跳无线方式组成的智能网络.网络容量的求解与网络吞吐量的优化是该类网络研究的难点.作者首先推导了混叠模式下认知无线Ad hoc网络容量上界的闭合表达式,并指出该上界只与用户空间分布特性相关;然后提出了一种新的基于遗传算法的跨层优化算法,通过联合优化邻居选择与功率分配实现网络吞吐量的最大化;最后仿真验证了该算法的有效性,结果表明网络吞吐量能较好地逼近网络容量上界.

Cognitive wireless ad hoc networks are smart networks formed by multiple cognitive nodes in a distributed and multi-hop fashion.The analysis of network capacity and optimization of network throughput are key problems in the research field of such networks.In this paper,we first derive the close-form expression of the upper bound of network capacity for cognitive wireless ad hoc networks in the underlay spectrum access mode,and show that this upper bound is only determined by the spatial distribution of the nodes.Then we present a novel cross-layer optimization algorithm for maximizing network throughput,which adopts genetic algorithm（GA） to achieve the optimal neighbor selection and power allocation.Lastly,numerical simulation is conducted to verify the proposed scheme,and it is shown that the obtained network throughput achieves a performance closely approximate to the upper bound of network capacity.