This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).
翻译:本文考虑了无细胞型大规模超动投入和多输出(MIMO)系统的基本电力分配问题,这些系统的目的是在每个接入点在总电压限制和每个用户在服务质量限制下最大限度地实现总能效(EEE),每个用户在总电压限制下实现总能效(QOS),优化问题的现有解决办法是解决第二阶锥形程序(SOCP)的顺序,其计算复杂性尺度与网络规模相比大为惊人。因此,对于实际的大型无细胞型大型MIMO系统来说,这些系统无法实施。为解决这一问题,我们提议基于加速预测梯度方法框架工作的迭代电力控制算法。具体地说,拟议方法的每一次迭代动力控制算法都是通过简单的封闭式表达方式进行的,在采用惩罚方法将限制以惩罚功能的形式引入目标时,最后,拟议算法的趋同基于SOCP的已知解决办法相比,具有分析性和数字上的趋同性。 模拟结果表明,我们拟议的电力控制算法可以实现与现有SOPS-CP-时间级方法相同的E,但更重要的是,比以更低一个SOCP-时间级方法。