| Summary: | In massive multiple input and multiple output (MIMO) systems the challenge is the detection of the individual signals from the composite signal with a large system limit. The optimal detector becomes prohibitively complex. The approximate message passing (AMP) algorithm, designed for compressed sensing, has attracted researchers to counter this problem due to its reduced complexity with a large system limit. For this reason the AMP algorithm has been used for detection in massive MIMO systems. In this paper, we focus on implementing this algorithm in a fixed-point format. To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm. These estimation functions are obtained using the log-sum approximation, then taking the exponent of the result. The log-sum approximation is obtained by the Jacobian logarithm with a correction function. We also provide a modification of the correction function for the approximations that best suits our case. We then transform the algorithm with the approximated functions to fixed-point and provide a BER performance for the algorithm with the variables set to 16-bit word lengths using the hybrid “ScaledDouble” data types.
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