【摘要】 为提高认知网络的频谱利用效率和信道数据速率,提出一种基于最大速率和干扰约束的认知网络最优功率分配算法。提出认知中继系统模型,分析用户的发送/接收信号功率及接收信号,即干扰加噪声比;将成功传递位的最大化速率问题转化为凸优化问题,提出最大干扰电平约束与中继功率的线性约束组合方程式,通过使用Karush-Kuhn-Tucker(KKT)条件求得最优功率分配方案。实验结果表明,该算法的信道利用率分别为对比算法的121.3%和118.6%。更多还原

【Abstract】 To improve spectral efficiency and channel data rate of cognitive networks,a cognitive network optimal power allocation algorithm based on maximum rate and interference constraint was proposed.Cognitive relay system model was proposed to analyze the transmission/reception signal power and the received signal,namely interference plus noise ratio.The successful delivery bit rate maximization problem was transferred into a convex optimization.A linear constraint combination equation of maximum interference level constraint and relay power was proposed.Karush-Kuhn-Tucker(KKT)conditions were used to obtain optimal power allocation scheme.Experimental data show that the channel utilization of the algorithm is 121.3% and 118.6% of that of the comparison algorithm respectively.更多还原