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• W3101379481 abstract "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." @default.
• W3101379481 created "2020-11-23" @default.
• W3101379481 creator A5027364315 @default.
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• W3101379481 date "2020-11-05" @default.
• W3101379481 modified "2023-09-27" @default.
• W3101379481 title "DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs" @default.
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• W3101379481 hasPublicationYear "2020" @default.
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