提出了一种适用于时间频率选择性衰落信道的MIMO-OFDM系统的组合信道估计方法。采用AR过程对信道进行建模,利用基于导频的低维Kalman滤波算法进行信道估计,并采用LS算法估计时变的信道衰减因子。Kalman滤波跟踪了信道的时域相关性,为了同时跟踪信道的频域相关性,采用了一种基于MMSE(minimum mean square error)的合并器对Kalman滤波算法进行修正。仿真表明,提出的这种组合算法降低了传统的Kalman滤波结构的复杂度,能够跟踪信道的时频变化,改进了基于LS准则的信道估计算法,并且与复杂的高维Kalman滤波算法的信道估计性能相当。
本文将跨层优化和蚂蚁优化方法结合起来解决自组网中的负载均衡问题,提出了一种基于跨层负载感知和双向逐跳更新信息素的蚂蚁优化路由协议(CLABHPU)。协议将整个路径中各节点 MAC 层的总平均估计时延和节点队列缓存的占用情况结合起来,共同作为路由选择和路由调整的重要依据,进行按需路由发现和维护;通过拥塞节点丢弃蚂蚁分组的方法减少了控制开销,增加了算法的可扩展性,较好地解决了自组网中现有基于蚂蚁算法的路由协议中普遍存在的拥塞问题和路由开销问题。同时,协议在路由发现阶段通过中间节点对信息素表进行双向和逐跳更新,提高了算法的收敛速度和对异常情况的反应速度。通过概率选路提供到目的节点的大量冗余路由,提高了算法的可靠性和顽存性。仿真结果表明,CLABHPU 在分组成功递交率、路由开销以及端到端平均时延等方面具有优良性能,能很好地实现网络业务流负载均衡。
The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.
