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- W3046015132 abstract "In this paper, we present a deep learning based trainable approximate message passing algorithm (TAMP) for signal detection in massive multiple-input multiple-output (MIMO) systems. The TAMP network consists of a preprocessing layer and a fixed number of detection layers, where the preprocessing layer is designed by using a standard fully connected layer, and the structure of each detection layer is derived by unfolding each iteration of the iterative GAMP algorithm. In addition, the proposed TAMP includes trainable parameters controlling prior mean and variance of minimum mean squared error (MMSE) denoiser. The parameters are trained by standard deep learning techniques. We evaluate the signal detection performance of the proposed TAMP under Rayleigh-fading and spatial correlated MIMO channels. Furthermore, we compare TAMP with existing state-of-the-art iterative message passing-based detection algorithms and deep learning based detection algorithms. Computer experiments show that TAMP is applicable under both Rayleigh-fading and spatial correlated channel in massive MIMO systems. Moreover, comparison results demonstrate that TAMP can achieve better detection accuracy and faster convergence." @default.
- W3046015132 created "2020-08-03" @default.
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- W3046015132 date "2020-06-01" @default.
- W3046015132 modified "2023-09-26" @default.
- W3046015132 title "Deep Learning Based Trainable Approximate Message Passing for Massive MIMO Detection" @default.
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- W3046015132 doi "https://doi.org/10.1109/icc40277.2020.9148845" @default.
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