Abstract
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.
Highlights
T O SUPPORT the exceptional proliferation in smart devices and the enormous growth of mobile data traffic, dense communication networks are envisioned
Inspired by [19]–[21], we here develop distributed algorithms based on scaled alternating direction method of multipliers (ADMM) to solve the resulting quadratically-constrained quadratic program (QCQP), in which each updating step is derived in closed form and can be carried out distributively among multiple access points (APs)
We summarize the fractional programming (FP) approach for solving the max-min fairness problem in Algorithm 6 whose steps are in closedform
Summary
T O SUPPORT the exceptional proliferation in smart devices and the enormous growth of mobile data traffic, dense communication networks are envisioned. We develop low-complexity algorithms with closed-form update equations for the max-min fairness and sum-SE maximization problems in distributed downlink operation of Cell-free mMIMO. After some problem-specific steps, the resulting weighted minimum mean square error (WMMSE) algorithm converges to a local optimum Another approach to max-min and sum-SE power allocation problems is the concave-convex procedure (CCP) based fractional programming (FP) [19], [20], by utilizing that the signal-to-interference-and-noise ratio (SINR) is a fraction. Inspired by [19]–[21], we here develop distributed algorithms based on scaled ADMM to solve the resulting QCQPs, in which each updating step is derived in closed form and can be carried out distributively among multiple APs. We note that this work differs from the existing work on multi-cellular communications with cooperative base stations [29], which has a similar optimization problem structure for a different system model. The identity matrix with dimension N × N is denoted by IN
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