Distributed Nonconvex Power Control using Gibbs Sampling
Distributed Nonconvex Power Control using Gibbs Sampling
复制标题
DOI：
10.1109/tcomm.2012.082812.120017
复制
发表时间：
2012-09
影响因子：
8.3
通讯作者：
L. Qian;Y. Zhang;M. Chiang
中科院分区：
文献类型：
--
作者：
L. Qian;Y. Zhang;M. Chiang
长期以来，无线网络中的发射功率控制一直被认为是减轻同频干扰的有效机制。由于高度的非凸性，如果要最大化系统效用，就很难实现最优功率控制。在前面的文章中，我们提出了一种集中式最优功率控制算法，该算法对于凹形和非凹形系统效用函数都能获得全局最优解。一个悬而未决的问题是，这种全局最优解能否以分布式方式实现。针对这一问题，本文提出了一种基于吉布斯采样的异步分布式功率控制算法(简称GRAD)。无论效用函数的凹性、可微性和单调性如何，该算法都能快速收敛到全局最优解。为了进一步提高算法的实用性，本文提出了两种改进的GREAD算法，即I-GREAD和NI-GREAD，以减少通信复杂度在时间和空间两个维度上的消息传递。特别是，前缀“I”代表不频繁的消息传递，减少了消息传递的“时间开销”。无论消息传输率如何降低，都可以证明I-GREAD算法是收敛的。同时，NI-GREAD算法将消息传递相关的计算开销限制在较小的邻域空间内，其中前缀N代表邻域消息传递。结果表明，NI-GREAD算法得到的解的最优性取决于邻域大小的选择。
Transmit power control in wireless networks has long been recognized as an effective mechanism to mitigate co-channel interference. Due to the highly non-convex nature, optimal power control is known to be difficult to achieve if a system utility is to be maximized. In our earlier paper , we have proposed a centralized optimal power control algorithm that obtains the global optimal solution for both concave and non-concave system utility functions. A question remained unanswered is whether such global optimal solution can be achieved in a distributed manner. This paper addresses the question by developing a Gibbs Sampling based Asynchronous distributed power control algorithm (referred to as GLAD). The proposed algorithm quickly converges to the global optimal solution regardless of the concavity, differentiability and monotonicity of the utility function. To further enhance the practicality of the algorithm, this paper proposes two variants of the GLAD algorithm, namely I-GLAD and NI-GLAD, to reduce message passing in two dimensions of communication complexity, i.e., time and space. In particular, I-GLAD, where the prefix "I" stands for Infrequent message passing, reduces the "time overhead" of message passing. The convergence of I-GLAD can be proved regardless of the reduction in the message passing rate. Meanwhile, NI-GLAD, where the prefix "N" stands for Neighborhood message passing, restricts the computation overhead related to message passing to a small neighborhood space. Our results show that the optimality of the solution obtained by NI-GLAD depends on the selection of the neighborhood size.