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Throughput Optimization in Cognitive Random Wireless Ad hoc Networks

机译:认知随机无线自组织网络中的吞吐量优化

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We consider coexistence of overlapping but distinct primary and secondary random ad hoc networks. A secondary node has node-level and network-level obligations to avoid interference on primary receiving nodes. At the node level, it is allowed to transmit only if it does not detect data reception from its primary neighbors. At the network level, it needs to make sure that the aggregate secondary interference on an arbitrary primary receiving node satisfies an interference constraint. In this paper, we show that both of these obligations are non-trivially related to transmission attempt probabilities in primary and secondary networks and our aim is the optimization of throughput in these networks. We show that the interference constraint limits the feasible space for transmission probabilities of both systems. Looking at the secondary network, as a network plugged into the primary, we propose a progressive transmission probability optimization algorithm and show through simulation that it converges to the optimum throughput in a few time slots. Results show that at the optimum transmission probabilities, while the primary network deviates slightly from its optimal throughput (obtained when secondary network is not present), a considerable throughput can be gained by the secondary network. Results also show that the sum throughput also increases as a result of this optimization.
机译:我们考虑重叠但不同的主要和次要随机自组织网络的共存。次要节点具有节点级和网络级的义务,以避免对主要接收节点的干扰。在节点级别,仅当它未检测到来自其主要邻居的数据接收时,才允许发送。在网络级别上,需要确保任意主接收节点上的合计辅助干扰满足干扰约束。在本文中,我们表明这两个义务与主要网络和次要网络中的传输尝试概率均无关紧要,并且我们的目标是优化这些网络中的吞吐量。我们表明干扰约束限制了两个系统的传输概率的可行空间。看看辅助网络,作为插入主网络的网络,我们提出了一种渐进的传输概率优化算法,并通过仿真表明它在几个时隙内收敛到了最佳吞吐量。结果表明,在最佳传输概率下,虽然主网络与最佳吞吐量略有偏离(当不存在辅助网络时获得),但辅助网络可以获得相当大的吞吐量。结果还表明,由于优化,总吞吐量也增加了。

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