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针对用户异构的多对多微型用户认知网络,研究如何有效分配次用户感知信道组,以实现能量消耗、感知性能及频谱接入机会的相对平衡.首先,考虑微型用户的特性,结合用户拓扑信息,在系统检测性能的约束条件下,以最大化能量效用为准则构建了优化问题.然后,对问题进行简化处理,基于KKT(Karush-Kuhn-Tucker)条件推导出闭合式以决定用户感知的优先级,进而设计了具有位置感知的能量有效性分配算法以逼近优化问题的最优解.最后,针对不同的用户分布场景进行仿真,结果表明提出的算法能够在快速分配最优感知用户组的同时,获得比其他算法更高的能量效用,且其能量效用性能很接近最优算法(穷举搜索)的性能,但复杂度却大为降低.
Aiming at the heterogeneous many to many user cognitive networks, this paper studies how to effectively allocate sub-user aware channel groups to achieve the relative balance between energy consumption, perceived performance and spectrum access opportunities.Firstly, considering the characteristics of micro-users, Topology information, the optimization problem is constructed with the maximization of energy utility under the constraints of the system detection performance.Then, the problem is simplified and the closed-form is derived based on KKT (Karush-Kuhn-Tucker) condition to determine the user perception And then designs a location-aware energy-efficient allocation algorithm to approximate the optimal solution of the optimization problem.Finally, the simulation of different user-distributed scenarios shows that the proposed algorithm can quickly allocate optimally aware user groups At the same time, it obtains higher energy utility than other algorithms, and its energy performance is very close to the performance of the optimal algorithm (exhaustive search), but the complexity is greatly reduced.