TY - GEN
T1 - Task-Based Quantization for Massive MIMO Channel Estimation
AU - Shlezinger, Nir
AU - Eldar, Yonina C.
AU - Rodrigues, Miguel R.D.
N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant No. 646804-ERC-COG-BNYQ, from the Israel Science Foundation under grant No. 0100101, and from the Royal Society International Exchange scheme IE 160348. Publisher Copyright: © 2019 IEEE.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Massive multiple-input multiple-output (MIMO) systems are the focus of increasing research attention. In such setups, there is an urgent need to utilize simple low-resolution quantizers, due to power and memory constraints. In this work we study massive MIMO channel estimation with quantized measurements, when the quantization system is designed to minimize the channel estimation error, as opposed to the quantization distortion. We first consider vector quantization, and characterize the minimal error achievable. Next, we focus on practical systems utilizing scalar uniform quantizers, and design the analog and digital processing as well as the quantization dynamic range to optimize the channel estimation accuracy. Our results demonstrate that the resulting massive MIMO system which utilizes low-resolution scalar quantizers can approach the minimal estimation error dictated by rate-distortion theory, achievable using vector quantizers.
AB - Massive multiple-input multiple-output (MIMO) systems are the focus of increasing research attention. In such setups, there is an urgent need to utilize simple low-resolution quantizers, due to power and memory constraints. In this work we study massive MIMO channel estimation with quantized measurements, when the quantization system is designed to minimize the channel estimation error, as opposed to the quantization distortion. We first consider vector quantization, and characterize the minimal error achievable. Next, we focus on practical systems utilizing scalar uniform quantizers, and design the analog and digital processing as well as the quantization dynamic range to optimize the channel estimation accuracy. Our results demonstrate that the resulting massive MIMO system which utilizes low-resolution scalar quantizers can approach the minimal estimation error dictated by rate-distortion theory, achievable using vector quantizers.
KW - Massive MIMO
KW - quantization
UR - http://www.scopus.com/inward/record.url?scp=85068969786&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ICASSP.2019.8682735
DO - https://doi.org/10.1109/ICASSP.2019.8682735
M3 - Conference contribution
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4489
EP - 4493
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -