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Partition function game for rate optimization in random interference networks

机译:分区函数游戏,用于随机干扰网络中的速率优化

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摘要

Interference is one of the main limiting factors in wireless communication networks for system performance nowadays. In this paper, we study a coalitional game approach for rate optimization in a random interference networks where multiple transmission pairs transmit concurrently. We model the rate optimization problem as a partition function game. All transmission pairs form separated coalitions, and the pairs cooperate to transmit within each coalition. An iterative coalition structure generation algorithm is proposed to obtain partitions. This algorithm allows each transmission pair to make individual decision whether to stay in or depart from its current coalition. We prove that the proposed iterative algorithm can achieve a Nash-stable partition. In each coalition, we adopt the duality features of broadcast channel (BC) and multi-access channel (MAC) to maximize the sum-rate. Simulation results show that the proposed algorithm can improve the network performance significantly compared with location-based static partition forms.
机译:干扰是当今无线通信网络中对系统性能的主要限制因素之一。在本文中,我们研究了一种联盟博弈方法,用于在多个传输对同时传输的随机干扰网络中优化速率。我们将速率优化问题建模为分区函数游戏。所有传输对形成分离的联盟,并且这些对协作以在每个联盟内传输。提出了一种迭代联盟结构生成算法来获取分区。该算法允许每个传输对单独做出决定,是停留在当前联盟中还是离开当前联盟。我们证明了所提出的迭代算法可以实现Nash稳定分区。在每个联盟中,我们都采用广播信道(BC)和多路访问信道(MAC)的双重性功能,以使总速率最大化。仿真结果表明,与基于位置的静态分区形式相比,该算法可以显着提高网络性能。

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