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下一代无线网络是多种无线接入技术并存的异构网络,要充分利用各种无线技术网络的资源,为用户提供更好的服务,需要实现异构网络的融合。本文引入认知无线网络概念以解决未来网络的复杂性、异构性问题,从而实现异构网络的融合。认知无线网络能够使网络从静态工作模式发展到动态自适应工作模式,从单一封闭式网络发展到异构融合网络,从而能够面向用户需求提供更加可靠和高速率的用户服务。无线接入网络的选择问题是未来无线异构网络的关键技术之一,也是多网融合的基础。本文面向用户终端提供一种基于Q-学习的认知无线网络接入方案,该方法充分利用了认知无线电技术以及认知无线网络相关技术,使得移动终端能够根据自身的偏好或者Qo S需求,选择最合适的接入网络,同时实现系统整体能耗开销最优化。特别地,本文以家庭基站和宏基站共存复杂架构下的网络接入问题进行探讨并提供解决办法。仿真结果表明,本文提供的网络选择算法能够显著提升系统的阻塞情况并降低系统的能耗开销。
Next generation wireless networks are heterogeneous networks in which a variety of radio access technologies coexist. To fully utilize the resources of various wireless technology networks and provide users with better services, it is necessary to realize the integration of heterogeneous networks. In this paper, the concept of cognitive wireless network is introduced to solve the complexity and heterogeneity of the future network, so as to realize the integration of heterogeneous networks. Cognitive wireless networks enable the network to evolve from a static working mode to a dynamic adaptive working mode from a single closed network to a heterogeneous converged network, so as to be able to provide more reliable and high-speed user services for user needs. The choice of wireless access networks is one of the key technologies of wireless heterogeneous networks in the future, and also the basis of multi-network convergence. In this paper, a user terminal is provided with a cognitive radio network access scheme based on Q-learning, which fully utilizes the cognitive radio technology and the related technology of the cognitive radio network so that the mobile terminal can, according to its own preference or Qo S requirement, Choose the most suitable access network, and at the same time optimize the overall energy consumption of the system. In particular, this paper discusses and provides solutions to the network access problems under the complex architecture of home base station and macro base station coexistence. The simulation results show that the network selection algorithm provided in this paper can significantly improve the system congestion and reduce the system energy consumption overhead.