This paper considers simultaneous wireless information and power transfer enabled full-duplex multi-user multiple-input multiple-output cognitive networks. By taking into account imperfect channel state information (CSI) of the links toward primary users (PUs), this paper aims to simultaneously optimize two design objectives, namely the achievable sum-rate and sum harvested energy in the secondary network. Hence, the design problem is modelled as a multi-objective optimization problem (MOOP) subject to the transmit power constraint at the base station and robust harmful interference constraints at the PUs. Then, to find the Pareto set, the MOOP is rewritten to a single-objective problem (SOOP) by using the modified weighted Tchebycheff method. However, it is mathematically difficult to solve the transformed SOOP due to the non-concavity of the sum-rate function and the semi-infinite nature of the robust interference constraints. To overcome this challenge, we use the difference of two convex functions (DC) technique and S-procedure theory to recast the design optimization problem as the sequential convex programming. Various numerical simulations are conducted to illustrate the Pareto optimal solution sets, the robustness of interference constraints against CSI uncertainties, and trade-offs between the achievable sum-rate and harvested energy.

本文考虑同时启用无线信息和功率传输的全双工多用户多输入多输出认知网络。通过考虑到主要用户（PU）的链路的信道状态信息（CSI）的不完善，本文旨在同时优化两个设计目标，即次级网络中可实现的总速率和总能量收集。因此，设计问题被建模为一个多目标优化问题（MOOP），该问题受到基站的发射功率约束和PU的鲁棒有害干扰约束的影响。然后，为了找到帕累托集，使用改进的加权Tchebycheff方法将MOOP重写为单目标问题（SOOP）。然而，由于求和率函数的非凹性以及鲁棒干扰约束的半无限性质，在数学上很难求解转换后的SOOP。为了克服这个挑战，我们使用两个凸函数（DC）技术和S-过程理论的差异，将设计优化问题重铸为顺序凸编程。进行了各种数值模拟，以说明Pareto最优解集，针对CSI不确定性的干扰约束的鲁棒性以及可实现的总费率和收获的能量之间的权衡。我们使用两个凸函数（DC）技术和S-过程理论的差异，将设计优化问题重铸为顺序凸编程。进行了各种数值模拟，以说明Pareto最优解集，针对CSI不确定性的干扰约束的鲁棒性以及可实现的总费率和收获的能量之间的权衡。我们使用两个凸函数（DC）技术和S-过程理论的差异，将设计优化问题重铸为顺序凸编程。进行了各种数值模拟，以说明Pareto最优解集，针对CSI不确定性的干扰约束的鲁棒性以及可实现的总费率和收获的能量之间的权衡。