An integrated incremental self-organizing map and hierarchical neural network approach for cognitive radio learning

TLDR: An incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks and the adaptability of ISOM can improve the real-time learning performance.

Abstract: In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.