Low-resolution analog-to-digital converters (ADCs) in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems can significantly reduce the power, cost, and interconnect data rates of infrastructure basestations. Thus, recent research on the theory and algorithm sides has extensively focused on such architectures, but with idealistic quantization models. However, real-world ADCs do not behave like ideal quantizers, and are affected by fabrication mismatches. We analyze the impact of capacitor-array mismatches in successive approximation register (SAR) ADCs, which are widely used in wireless systems. We use Bussgang's decomposition to model the effects of such mismatches, and we analyze their impact on the performance of a single ADC. We then simulate a massive MU-MIMO system to demonstrate that capacitor mismatches should not be ignored, even in basestations that use low-resolution SAR ADCs.
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DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.
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- NSF-PAR ID:
- 10297934
- Date Published:
- Journal Name:
- 2021 - IEEE International Conference on Communications
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC42927.2021.9501054
