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On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems

机译:在无细胞大型MIMO系统的上行MAX-MINR上

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A cell-free massive multiple-input multiple-output system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio problem is formulated for the optimization of receiver filter coefficients at a central processing unit and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized Eigenvalue problem, whereas the geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms the existing schemes in the literature.
机译:使用MAX-MIN方法考虑无细胞大规模多输入多输出系统,以通过每用户功率约束来最大化最小用户速率。首先,基于信道统计来导出近似的上行链路用户速率。然后,原始的MAX-MIN信号到干扰 - 加密率问题被配制用于在中央处理单元和用户电力分配处优化接收器滤波器系数。为了解决这个MAX-MIN的非凸面问题,我们将原始问题分成两个子问题,即接收器滤波器系数设计和功率分配。接收器滤波器系数设计被制定为广义特征值问题,而几何编程(GP)用于解决用户电力分配问题。基于这两个子问题,提出了一种迭代算法,其中两个问题都在交替解决,而其中一个设计变量是固定的。该迭代算法获得全局最佳解决方案,通过建立上行链路二元性来证明其最优性。此外,我们提出了一种新的次优基方案,其提供GP制剂以有效地和全球最大化最小上行链路用户速率。数值结果表明,所提出的方案显着优于文献中现有方案。

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