Research On Radio Resource Management Based On IEEE 1900.4

With the high speed development of radio communication, the management for radio resource being more and more necessary in today's wireless communications. Especially within cognitive radio networks, it is causing the demand for radio access selection of the multimode reconfigurable terminals being greatly increasingly. This has the particular advantage of high data rates in unlicensed spectrum. Consequently, the radio access strategy of reconfiguration for a multimode reconfigurable terminal plays an important role in ensuring quality of service (QoS). An effective strategy for terminal reconfiguration radio access under the framework of IEEE P1900.4 is presented in this paper. The proposed strategy combines the eigenvector method and modified technique for order preference by similarity to ideal solution (modified TOPSIS), which can rank the available reconfiguration mode alternatives with fast and simple implementation. At the same time, it can provide the user with the best available QoS at any implement period. Simulations are conducted under a cognitive radio network with UMTS, WiMAX and Wireless LANs. The results show that the proposed strategy can effectively decide the optimum mode of reconfiguration by making trade-off among network condition, user preference, and service application.In the next generation radio network , especially in downlink ,multicast communication will be the main mode. Consequently, the optimization of channel assignment in heterogeneous cognitive multicast communication system is significant. In this paper we adopt reinforcement learning under the framework of IEEE1900.4 in channel assignment in heterogeneous cognitive multicast communication system to improve assignment efficiency. The results of simulations show that the method is available.