Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks
Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks
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DOI：
10.1007/s11432-016-9143-6
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发表时间：
2017-08
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影响因子：
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通讯作者：
Wenxiang Zhu;P. Xu;Thioanh Bui;Guilu Wu;Yan Yang
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文献类型：
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作者：
Wenxiang Zhu;P. Xu;Thioanh Bui;Guilu Wu;Yan Yang
近年来，能源效率已成为一个重要话题，特别是在超密集网络（UDN）领域。在这一领域，提出了小区关联偏置调整和小小区开/关来提高UDN的能源效率性能。这是通过改变小区关联关系并关闭没有用户的额外小小区来完成的。然而，小区关联关系的多样性以及小小区的开启/关闭可能会降低某些用户的数据速率，导致不符合用户的数据速率要求。考虑到能效优化问题的离散性和非凸性以及优化过程中小区关联与调度的耦合关系，很难获得最优的小区关联偏差。在本研究中，我们通过调整小小区的小区关联偏差来优化网络能源效率，同时满足用户的数据速率要求。我们提出了一种基于节能集中吉布斯采样的细胞关联偏差调整（CGSCA）算法。在CGSCA中，需要收集信道状态信息、小区关联信息、网络负载信息等全局信息。然后，考虑到交换消息的开销和 CGSCA 获取 UDN 中全局信息的实现复杂度，我们提出了一种具有较低消息交换开销和实现复杂度的基于节能的分布式吉布斯采样的小区关联偏差调整（DGSCA）算法。使用 DGSCA，我们推导出计算小区中用户数量和用户 SINR 的更新公式。我们分析了所提出的两种算法和其他现有算法的实现复杂性（例如计算复杂性和通信复杂性）。我们进行了仿真，结果表明，与其他现有算法相比，CGSCA和DGSCA具有更快的收敛速度，以及更高的能量效率和吞吐量的性能增益。此外，我们分析了用户数据速率约束在优化能效方面的重要性，并比较了不同算法与不同数量的小基站的能效性能。然后，随着小小区数量的增加，我们呈现睡眠小小区的数量。
In recent years, energy efficiency has become an important topic, especially in the field of ultra-dense networks (UDNs). In this area, cell-association bias adjustment and small cell on/off are proposed to enhance the performance of energy efficiency in UDNs. This is done by changing the cell association relationship and turning off the extra small cells that have no users. However, the variety of cell association relationships and the switching on/off of the small cells may deteriorate some users’ data rates, leading to nonconformance to the users’ data rate requirement. Considering the discreteness and non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process, it is difficult to achieve an optimal cell-association bias. In this study, we optimize the network energy efficiency by adjusting the cell-association bias of small cells while satisfying the users’ data rate requirement. We propose an energy-efficient centralized Gibbs sampling based cell-association bias adjustment (CGSCA) algorithm. In CGSCA, global information such as channel state information, cell association information, and network load information need to be collected. Then, considering the overhead of the messages that are exchanged and the implementation complexity of CGSCA to obtain the global information in UDNs, we propose an energy-efficient distributed Gibbs sampling based cell-association bias adjustment (DGSCA) algorithm with a lower message-exchange overhead and implementation complexity. Using DGSCA, we derive the updated formulas for calculating the number of users in a cell and the users’ SINR. We analyze the implementation complexities (e.g., computation complexity and communication com- plexity) of the proposed two algorithms and other existing algorithms. We perform simulations, and the results show that CGSCA and DGSCA have faster convergence speed, as well as a higher performance gain of the energy efficiency and throughput compared to other existing algorithms. In addition, we analyze the importance of the users’ data rate constraint in optimizing the energy efficiency, and we compare the energy efficiency performance of different algorithms with different number of small cells. Then, we present the number of sleeping small cells as the number of small cells increases.