认知无线网络中的跨层资源优化

跨层资源优化是设计认知无线网络重要的一环，是典型的多目标优化问题。为此，提出一种自适应克隆与邻域选择优化算法解决认知无线网络中的资源优化分配问题。以使用带宽、消耗功率、数据传输速率等指标作为认知网络优化目标，并将其在算法中进行优化。通过2种典型测试函数的仿真比较，结果表明该算法能够有效解决认知无线网络中的频谱资源分配、功率控制及速率提升等多目标优化问题，且与SPEA-2算法和NNIA算法相比，具有明显的优越性。

Cross-layer Resources Optimization in Cognitive Wireless Networks

Resources optimization of cross-layer is a very important part in cognitive wireless networks design, and it is a typical multi-objective optimization problem. This paper proposes an adaptive clone and neighbor selection algorithm to resolve the problem of optimization resources allocation in cognitive wireless networks, and uses the algorithm to optimize three goals of used bandwidth, power consumption and rate of data transmission in cognitive networks. Simulations and comparisons in two typical test functions show that the proposed algorithm can effectively solve the problem of multi-objective optimization, such as spectrum resources allocation, power control and rate improvement, it is more superior than SPEA-2 algorithm and NNIA algorithm.