Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

• Published in: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5145 - 5149
• Main Authors: Chien, Trinh Van, Bjornson, Emil, Larsson, Erik G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2020
• Series: IEEE International Conference on Acoustics, Speech and Signal ProcessingInternational Conference on Acoustics, Speech and Signal Processing (ICASSP)
• Subjects: access points; antenna arrays; Cell-free Massive MIMO; coherent joint transmission; convex programming; Downlink; Energy efficiency; energy-efficient cell-free massive MIMO joint power allocation; integer programming; large-scale distributed antenna system; Massive MIMO; MIMO communication; mixed-integer second-order cone program; nonconvex optimization problem; Optimization; optimization framework; Power demand; resource allocation; Signal processing algorithms; sparse optimization; telecommunication power management; total power consumption; total power minimization; transmit powers; Transmitting antennas
• ISBN: 9781509066315; 1509066322; 1509066314; 9781509066322
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP40776.2020.9054083

A large-scale distributed antenna system that serves the users by coherent joint transmission is called Cell-free Massive MIMO (multiple input multiple output). For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the downlink, considering both the hardware and transmit powers, where APs can be turned off. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. We also propose a low-complexity algorithm that exploits group-sparsity in the problem formulation. Numerical results manifest that our optimization framework can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers.