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
PY - 2019/7/15
Y1 - 2019/7/15
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 - https://ieeexplore.ieee.org/document/8761134
U2 - 10.1109/ICC.2019.8761134
DO - 10.1109/ICC.2019.8761134
M3 - Conference contribution
T3 - IEEE International Conference on Communications (ICC): Proceedings
BT - IEEE International Conference on Communications (ICC)
PB - IEEE
ER -