基于注水算法的认知网络功率分配技术研究

Power Allocation Technology based on Water Flooding Algorithm in Cognitive Network

功率分配技术能有效提高认知网络传输链路的信道容量,而注水算法利用凸优化的思想能实现功率分配最优化。针对认知网络中使用注水算法分配信道功率时未考虑邻近认知信道干扰的情况,对认知网络中邻近认知信道干扰对授权信道的影响进行研究,提出了基于注水算法的两种降低邻近认知信道干扰的功率分配的次优化方案。性能分析和仿真结果表明,认知网络中认知信道干扰对授权信道的影响不容忽略,合理调整注水算法的参数能够降低邻近认知信道干扰,达到传输容量的最大化。

Power allocation technology can effectively improve channel capacity, and water flooding algorithm solve power allocation optimization by using convex optimization theory. Due to the ignoring of algorithm adjacent secondary channel interference in cognitive by water flooding algorithm in cognitive network, the interference of the adjacent secondary channel on the authorized channel is discussed in detail in the paper, and two optimization proposals on decreasing of adjacent secondary channel interference are described. Theoretical analysis and computer simulation indicate that interference gain of secondary user with primary channel couldn＇t be ignored. Reasonably regulating the parameters of water flooding algorithm can reduce the adjacent secondary channel interference and maximize the channel capacity.