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首页> 外文期刊>Signal Processing, IET >Femtocell base station clustering and logistic smooth transition autoregressive-based predicted signal-to-interference-plus-noise ratio for performance improvement of two-tier macro/femtocell networks
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Femtocell base station clustering and logistic smooth transition autoregressive-based predicted signal-to-interference-plus-noise ratio for performance improvement of two-tier macro/femtocell networks

机译：飞蜂窝基站集群和基于逻辑平滑过渡的基于自回归的预测信噪比加噪比，用于改善两层宏/飞蜂窝网络的性能

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The aim of this study is to improve the performance of two-tier macro/femtocell networks using a power control approach. In wireless networks, power control plays an important role in improving a number of performance parameters such as co-channel interference and outage probability reduction, throughput increasing, and power saving. This study explores the evolution of centralised power control algorithm based on femtocell base station (FBS) clustering and predicted signal-to-interference-plus-noise ratio (SINR) of users. To reduce the computational complexity of centralised algorithm, dense deployed femtocells are considered in different clusters. In this case, femtocells inside one cluster make considerable interference to each other, while the interferences from femtocells of other clusters are negligible. Moreover, because of the non-linearity of SINR samples, non-linear logistic smooth transition autoregressive (LSTAR) model is used to model the SINR data, and then the next SINR samples are predicted from the previous samples. According to the clustered FBSs and predicted SINR, the proposed power control scheme is applied to femtocell network in the downlink. The results demonstrate that the introduced method improves the outage probability and throughput and outperforms previous methods significantly.
机译：这项研究的目的是使用功率控制方法来改善两层宏/毫微微小区网络的性能。在无线网络中，功率控制在改善许多性能参数（如同信道干扰和断电概率降低，吞吐量增加和节能）中起着重要作用。这项研究探索了基于毫微微小区基站（FBS）聚类和预测的用户信干噪比（SINR）的集中式功率控制算法的发展。为了降低集中式算法的计算复杂度，在不同集群中考虑部署密集的毫微微小区。在这种情况下，一个群集中的毫​​微微小区彼此之间会产生相当大的干扰，而其他群集中的毫​​微微小区的干扰则可以忽略不计。此外，由于SINR样本的非线性，使用非线性逻辑平稳过渡自回归（LSTAR）模型对SINR数据进行建模，然后从先前的样本中预测下一个SINR样本。根据集群的FBS和预测的SINR，将所提出的功率控制方案应用于下行链路的毫微微小区网络。结果表明，引入的方法提高了中断概率和吞吐量，并且明显优于以前的方法。

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