Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing
2020 (English)In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, p. 5145-5149Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
IEEE, 2020. p. 5145-5149
Series
IEEE International Conference on Acoustics, Speech and Signal ProcessingInternational Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, E-ISSN 2379-190X
Keywords [en]
antenna arrays, convex programming, integer programming, MIMO communication, resource allocation, telecommunication power management, optimization framework, transmit powers, large-scale distributed antenna system, coherent joint transmission, access points, total power consumption, nonconvex optimization problem, energy-efficient cell-free massive MIMO joint power allocation, mixed-integer second-order cone program, Cell-free Massive MIMO, total power minimization, sparse optimization, energy efficiency
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-170118DOI: 10.1109/ICASSP40776.2020.9054083ISI: 000615970405081ISBN: 9781509066315 (electronic)ISBN: 9781509066322 (print)OAI: oai:DiVA.org:liu-170118DiVA, id: diva2:1471694
Conference
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual Barcelona, May 4-8, 2020
Note
This paper was supported by ELLIIT and CENIIT.
2020-09-292020-09-292021-03-09Bibliographically approved