RIS辅助认知反向散射通信网络吞吐量最大化算法

Throughput maximization algorithm for RIS-aided cognitive backscatterr communication networks

为了提高频谱利用率与解决反向散射通信存在障碍物阻挡导致通信质量急剧下降的问题,基于实际非线性能量收集模型,本文研究了智能超表面(reconfigurable intelligent surface,RIS)辅助的认知反向散射通信网络吞吐量最大化问题.考虑最大干扰功率、最小能量收集与RIS相移约束,建立了一个联合优化传输时间、发射功率、反射系数与RIS相移的多变量耦合资源分配模型。利用变量替换、二次变换和半正定松弛方法,将原问题转换为凸优化问题求解,并提出一种基于迭代的吞吐量最大化资源分配算法.仿真结果表明,与传统线性能量收集算法相比,所提算法平均吞吐量提升了15.0%;与传统无RIS辅助算法相比,所提算法平均吞吐量提升了22.7%.

To improve the spectrum utilization and solve the problem of communication performance degradation due to obstacle blocking in backscatter communication,based on the practical non-linear energy harvesting(EH)model,a throughput maximization problem is investigated for reconfigurable intelligent surface(RIS)-aided cognitive backscatter communication networks.Considering the maximum interference power constraint,the minimum EH constraint,and the phase shift of RIS constraint,a multivariate coupled resource allocation model is formulated by jointly optimizing transmission time,transmit power,reflection coefficient,and phase shift of RIS.Then,the original problem is transformed into a convex optimization problem using the variable substitution approach,quadratic transform method,and semi-definite relaxation method,and then an iteration-based resource allocation algorithm for throughput maximization is proposed.Simulation results verify that the average throughput of the proposed algorithm is increased by 15.0%compared with the traditional algorithm with the linear EH,and 22.7%compared with the traditional algorithm without RIS.