Deep Quantization for MIMO Channel Estimation

Quantizers play a critical role in digital signal processing systems. In practice, quantizers are typically implemented using scalar analog-to-digital converters (ADCs), commonly utilizing a fixed uniform quantization rule which is ignorant of the task of the system. Recent works have shown that the performance of quantization systems utilizing scalar ADCs can be significantly improved by properly processing the analog signal prior to quantization. However, the implementation of such systems requires complete knowledge of the underlying model, which may not be available in practice. In this work we design task-oriented quantization systems with scalar ADCs using deep learning, focusing on the task of multiple-input multiple-output (MIMO) channel estimation. By utilizing deep learning, we construct a task-based quantization system, overcoming the need to explicitly recover the system model and to find the proper quantization rule for it. Our results indicate that the proposed method results in practical MIMO systems with scalar ADCs which are capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model.