Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve the users by coherent joint transmission. The spectral efficiency (SE) achieved by each user depends on the power allocation: which APs that transmit to which users and with what power. In this article, we revisit the max-min and sum-SE power allocation policies, which have previously been approached using high-complexity general-purpose solvers. We develop and compare several different high-performance low-complexity power allocation algorithms that are appropriate for use in large systems. We propose two new algorithms for sum-SE power optimization inspired by weighted minimum mean square error (WMMSE) minimization and fractional programming (FP). Further, one new FP-based algorithm is proposed for max-min fair power allocation. The alternating direction method of multipliers (ADMM) is used to solve specific convex subproblems in the proposed algorithms. Our ADMM reformulations lead to multiple small-sized subproblems with closed-form solutions. The proposed algorithms find global or local optimal power allocation solutions for large-scale systems but with reduced computational time compared to previous work.

中文翻译：

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无小区大规模MIMO系统的高效下行链路功率分配算法

无小区大规模MIMO系统由大量地理上分散的接入点（AP）组成，这些接入点通过相干联合传输为用户提供服务。每个用户获得的频谱效率（SE）取决于功率分配：哪些AP向哪个用户传输以及以什么功率传输。在本文中，我们将回顾以前使用高复杂度通用求解器实现的max-min和sum-SE功率分配策略。我们开发并比较了适用于大型系统的几种不同的高性能，低复杂度的功率分配算法。在加权最小均方误差（WMMSE）最小化和分数规划（FP）的启发下，我们提出了两种用于求和SE功率优化的新算法。此外，提出了一种新的基于FP的算法，用于最大-最小公平功率分配。所提出的算法使用乘数交替方向法（ADMM）来解决特定的凸子问题。我们对ADMM的重新制定导致使用封闭式解决方案的多个小型子问题。所提出的算法找到了针对大型系统的全局或局部最优功率分配解决方案，但与以前的工作相比，其计算时间减少了。