On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

TY - GEN

T1 - On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

AU - Bashar, Manijeh

AU - Cumanan, Kanapathippillai

AU - Burr, Alister G.

AU - Ngo, Hien Quoc

AU - Larsson, Erik G.

AU - Xiao, Pei

N1 - Funding Information: The work of K. Cumanan and A. G. Burr was supported by H2020-MSCA-RISE-2015 under grant number 690750. This work was also supported in part by the U.K. Engineering and Physical Sciences Research Council under Grant EP/N020391/1. Publisher Copyright: © 2019 IEEE.

PY - 2019/5

Y1 - 2019/5

N2 - We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

AB - We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

UR - http://www.scopus.com/inward/record.url?scp=85070228610&partnerID=8YFLogxK

U2 - 10.1109/ICC.2019.8761134

DO - 10.1109/ICC.2019.8761134

M3 - Conference contribution

AN - SCOPUS:85070228610

T3 - IEEE International Conference on Communications

BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Communications, ICC 2019

Y2 - 20 May 2019 through 24 May 2019

ER -