论文标题
在MU-MISO网络中进行保密率优化的稳健波束形成设计
Robust Beamforming Design for Sum Secrecy Rate Optimization in MU-MISO Networks
论文作者
论文摘要
本文假设基站已知的不完美的通道状态信息,研究了多用户下行网络的波束形成设计问题。在这种情况下,基站配备了多个天线,每个用户都被特定的窃听器窃听,每个用户或窃听者都配备了一个天线。据认为,基站使用给定要求传输功率的特定要求采用发射光束形成。目的是最大化网络的保密率。由于通道的不确定性,很难计算系统的确切总和保密率。因此,考虑了保密率的最大值。总和保密率的优化仍然使被考虑的波束形成设计问题难以解决。为了解决这个问题,提出了一个波束形成的设计方案,通过采用基于泰勒膨胀的半决赛弛豫和一阶近似技术,将原始问题转化为凸近似问题。此外,凭借低复杂性的优势,在基站能够将窃听器的速率无效的情况下提出了一种基于零福利的光束形成方法。当基站没有能力时,将使用用户选择算法。数值结果表明,前一种策略的性能要比后者更好,这主要是由于优化波束形成方向的能力,并且两者都优于基于信噪比的基于信噪比的算法。
This paper studies the beamforming design problem of a multi-user downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is wiretapped by a specific eavesdropper where each user or eavesdropper is equipped with one antenna. It is supposed that the base station employs transmit beamforming with a given requirement on sum transmitting power. The objective is to maximize the sum secrecy rate of the network. Due to the uncertainty of the channel, it is difficult to calculate the exact sum secrecy rate of the system. Thus, the maximum of lower bound of sum secrecy rate is considered. The optimization of the lower bound of sum secrecy rate still makes the considered beamforming design problem difficult to handle. To solve this problem, a beamforming design scheme is proposed to transform the original problem into a convex approximation problem, by employing semidefinite relaxation and first-order approximation technique based on Taylor expansion. Besides, with the advantage of low complexity, a zero-forcing based beamforming method is presented in the case that base station is able to nullify the eavesdroppers' rate. When the base station doesn't have the ability, user selection algorithm would be in use. Numerical results show that the former strategy achieves better performance than the latter one, which is mainly due to the ability of optimizing beamforming direction, and both outperform the signal-to-leakage-and-noise ratio based algorithm.