论文标题
一种低复杂的方法,可在无上行链路单元格中的最大值公平性
A Low-Complexity Approach for Max-Min Fairness in Uplink Cell-Free Massive MIMO
论文作者
论文摘要
我们考虑了无链路单元格的大规模多输入多输出的最大最大公平性问题,这是超越5G网络的潜在技术。更具体地说,我们旨在最大化所有受使用者功率约束的用户的最低光谱效率,假设在接入点接收组合技术。所考虑的问题可以进一步分为两个子问题:接收器滤波器系数设计和功率控制问题。虽然接收器系数设计被证明是一个广义的特征值问题,因此承认了封闭形式的解决方案,但功率控制问题在数值上是麻烦的。为了解决电力控制问题,现有方法依赖于不适合大规模系统的几何编程(GP)。为了克服GP方法的高复杂性问题,我们首先重新重新重新制定功率控制问题介绍凸面程序,然后将平滑技术与加速的投影梯度方法结合使用来解决它。仿真结果表明,所提出的解决方案可以实现几乎相同的目标,但比现有的基于GP的方法要少得多。
We consider the problem of max-min fairness for uplink cell-free massive multiple-input multiple-output which is a potential technology for beyond 5G networks. More specifically, we aim to maximize the minimum spectral efficiency of all users subject to the per-user power constraint, assuming linear receive combining technique at access points. The considered problem can be further divided into two subproblems: the receiver filter coefficient design and the power control problem. While the receiver coefficient design turns out to be a generalized eigenvalue problem, and thus, admits a closed-form solution, the power control problem is numerically troublesome. To solve the power control problem, existing approaches rely on geometric programming (GP) which is not suitable for large-scale systems. To overcome the high-complexity issue of the GP method, we first reformulate the power control problem intro a convex program, and then apply a smoothing technique in combination with an accelerated projected gradient method to solve it. The simulation results demonstrate that the proposed solution can achieve almost the same objective but in much lesser time than the existing GP-based method.