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| 1 | Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks显示文摘In recent years, energy efficiency has become an important topic, especially in the field of ultradense 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 complexity) 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. | Wenxiang ZHU Pingping XU Thi Oanh BUI Guilu WU Yan YANG | 2018 | Science China(Information Sciences)2018，61，2： | 1 |
| 2 | 树上自旋系统的快速采样算法显示文摘自旋系统是统计物理学中用来描述微观粒子相互作用的重要框架,其可以描述伊辛模型,硬核模型,玻茨模型等统计物理学中的重要模型;通过求解自旋系统的配分函数可以得出物质的能量、磁矩等物理性质.作为一种重要的图模型,自旋系统在理论计算机、人工智能、概率论等领域中被称作马尔可夫随机场而广泛应用,其可以描述着色问题、图同态问题等图论中的重要问题.对图中的点和边赋予非负权重,自旋系统可以诱导出著名的吉布斯分布;配分函数的近似计算可以归约到对应的吉布斯采样问题,通过吉布斯采样可以求解系统的相关物理性质和统计规律.作为模型的简化,树上的自旋系统受到广泛研究;本文研究树上自旋系统的采样算法,并将其推广到树宽较小的图上.我们的主要工作可以列举如下:对于无外场的伊辛模型,基于节点的两种状态的对称性,可以直接计算出任意节点对应的边缘分布,然后通过简单变量的组合来模拟吉布斯分布.类似地,着色问题和玻茨模型也可以基于状态的对称性用简单变量来模拟吉布斯分布.对于一般的自旋系统,无法保证状态的对称性,我们先递归地计算出所有节点的边缘分布,然后基于这些边缘分布进行采样,并通过简单变量的组合来模拟吉布斯分布.对于普通图,我们引入树宽的概念来度量图与树的相似性,并且基于节点间的独立性将算法推广到树宽为2的伪森林和仙人掌图中.我们的算法仅需要线性时间来得到吉布斯分布中的一个样本,在时间复杂度上优于基于马尔可夫链蒙特卡洛模拟的采样算法. | 白宗磊 王捍贫 曹永知 王璐璐 | 2022 | 计算机学报2022，45，10： | 0 |
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