基于认知网络不对称模型的交会跳频算法

摘要

Proposed in this paper is a novel channel-hopping algorithm for rendezvous on the basis of asymmetric model in cognitive networks (named AMCH).According to the available channel set sensed by each secondary user, the lengths of channel hopping sequences for transmitters and receivers are selected in two disjoint sets of prime numbers, and two channel hopping sequences are respectively constructed.Without any other auxiliary infor-mation ( such as the synchronization information and the global channel label information, etc.) , any two secondary users within the communication scope can rendezvous in a short time and establish a link in their common channels. In comparison with the existing algorithms, AMCH algorithm diminishes the redundancies of channel-hopping se-quences and improves the efficiency of rendezvous.Simulated results show that AMCH algorithm outperforms the existing algorithms in terms of time-to-rendezvous upper boundary and rendezvous performance.%基于认知网络的不对称模型提出了一种新的交会跳频算法（ AMCH ）。该算法根据每个次要用户感知到的可用信道集，在两个不相交的素数集中选择发射机和接收机的跳频序列长度并分别构建跳频序列。在不需要其他辅助信息（如全网同步信息、信道标号信息）的情况下，通信范围内的任意两个次要用户通过跳频于短时间内就能交会在它们的共同信道上并建立通信链路。与现有算法相比，AMCH算法减小了跳频序列冗余，提高了交会效率。仿真结果表明，AMCH算法的交会时间上限最小，交会性能优于现有算法。