融合信息通信和空中计算的认知无线网络鲁棒资源分配优化

Robust resource allocation optimization in cognitive wireless network integrating information communication and over-the-air computation

针对空中计算网络中无线传感器的功率资源限制及其与现有无线通信网络的频谱竞争,研究一个包含信息通信和空中计算功能的认知无线网络,其中,主网络实现无线信息通信功能,次网络实现空中计算功能,且次网络中的传感器利用主网络基站发送的信号收集无线能量。考虑空中计算的均方误差(MSE)约束和网络中各节点的功率约束,基于随机信道不确定性构建目标函数为无线通信用户的和速率最大化的鲁棒资源分配优化问题,提出一个交替优化(AO)-改进的约束随机连续凸逼近(ICSSCA)算法,即AO-ICSSCA算法,将原鲁棒优化问题转换为确定性优化的子问题,并以交替的方式优化主网络基站的下行波束成形向量、次网络中传感器的功率因子和融合中心的融合波束成形向量。仿真实验结果表明,相较于改进前的约束随机连续凸逼近(CSSCA)算法,AO-ICSSCA算法能以更短的计算用时实现更好的优化性能。

To address the power resource limitations of wireless sensors in over-the-air computation networks and the spectrum competition with existing wireless information communication networks,a cognitive wireless network integrating information communication and over-the-air computation was studied,in which the primary network focused on wireless information communication,and the secondary network aimed to support over-the-air computation where the sensors utilized signals sent by the base station of the primary network for energy harvesting.Considering the constraints of the Mean Square Error(MSE)of over-the-air computation and the transmit power of each node in the network,base on the random channel uncertainty,a robust resource optimization problem was formulated,with the objective function of maximizing the sum rate of wireless information communication users.To solve the robust optimization problem effectively,an Alternating Optimization(AO)-Improved Constrained Stochastic Successive Convex Approximation(ICSSCA)algorithm called AO-ICSSCA,was proposed,by which the original robust optimization problem was transformed into deterministic optimization sub-problems,and the downlink beamforming vector of the base station in the primary network,the power factors of the sensors,and the fusion beamforming vector of the fusion center in the secondary network were alternately optimized.Simulation experimental results demonstrate that AO-ICSSCA algorithm achieves superior performance with less computing time compared to the Constrained Stochastic Successive Convex Approximation(CSSCA)algorithm before improvement.