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- W3012975215 abstract "In this paper, we present an algorithm to obtain the posterior beliefs of the transmitted bits in an uplink coded massive multiple-input-multiple-output (MIMO) wireless communication system with low resolution analog-to-digital-converters (ADC) at the base station (BS). The nonlinearities introduced by low resolution ADCs necessitates a new multiuser detection approach, for which propose a variational Bayes’ inference framework. Further, in coded communications, it is more important to obtain soft symbol estimates for the data bits, rather than hard decisions on the data symbols. We approximate the exact posterior distribution with a fully factorized distribution used in mean field theory in statistical physics, and find its parameters such that the evidence lower bound (ELBO) is maximized. These parameters are used to obtain the posterior beliefs of the transmitted bits in an iterative manner. The resulting algorithm, named the quantized variational Bayesian soft symbol decoding (QVBSSD) is computationally inexpensive and easy to implement. The output posterior beliefs are input to a soft-input-hard-output channel decoder, which gives the decoded bits. We empirically illustrate the bit error rate (BER) of the QVBSSD algorithm, and show its superiority compared to the unquantized MMSE based detection." @default.
- W3012975215 created "2020-04-03" @default.
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- W3012975215 date "2019-11-01" @default.
- W3012975215 modified "2023-09-25" @default.
- W3012975215 title "Variational Bayesian Inference based Soft-Symbol Decoding for Uplink Massive MIMO Systems with Low Resolution ADCs" @default.
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- W3012975215 doi "https://doi.org/10.1109/ieeeconf44664.2019.9048750" @default.
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