Task-Based Quantization for Massive MIMO Channel Estimation

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.