TY  - GEN
CY  - Brighton, UK
A1  - Shlezinger, N
A1  - Eldar, YC
A1  - Rodrigues, MRD
KW  - Massive MIMO
KW  -  quantization.
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 ratedistortion theory, achievable using vector quantizers.
ID  - discovery10085351
UR  - https://doi.org/10.1109/ICASSP.2019.8682735
PB  - IEEE
SN  - 1520-6149
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Task-Based Quantization for Massive MIMO Channel Estimation
AV  - public
SP  - 4489
Y1  - 2019/04/17/
EP  - 4493
ER  -