Abstract
We propose an information-optimum approximate message passing (AMP) for quantized massive multi-input multi-output (MIMO) signal detection. A well-known strategy for realizing low-complexity and high-accuracy massive multi-user detection (MUD) is AMP-based belief propagation (BP). However, when internal operations are conducted with double-precision arithmetic, large memory occupancy and severe processing delay are inevitable in the actual massive MIMO implementation. To address this issue, we replace all operations with a simple look-up table (LUT) search where all messages exchanged between each iteration process are unsigned integers. That is, the proposed signal detection is performed using only simple integer arithmetic. The LUT is designed offline using an information-bottleneck (IB) method, and the probability distribution of messages at each iteration step is required for determining the quantization threshold tracked by discrete density evolution (DDE). Computer simulations demonstrate the validity of the IB LUT-based AMP in terms of bit error rate (BER) performance and memory occupancy. The proposed method allows quantizing the AMP detector with fewer bits while maintaining similar performances, such as that of a typical AMP with double-precision.
Highlights
Compared to typical small-scale multi-antenna systems, massive multi-input multi-output (MIMO), where the base station (BS) is equipped with a massive number of antenna elements, promises significant improvements in spectral efficiency, detection reliability, and energy efficiency [1]–[3]
As low-complexity multi-user detection (MUD) solutions, linear spatial filters based on least square (LS) and linear minimum mean square error (MMSE) criteria are often utilized while sacrificing optimal detection capability [3]
We extend the typical binary class discrete density evolution (DDE) to multi-class, where the probability mass function (PMF) of quantized values is computed based on the probability density function (PDF) of corresponding continuous values, which is unlike the original binary DDE
Summary
Compared to typical small-scale multi-antenna systems, massive multi-input multi-output (MIMO), where the base station (BS) is equipped with a massive number of antenna elements, promises significant improvements in spectral efficiency, detection reliability, and energy efficiency [1]–[3]. In the AMP-based MUD, the original values referred in the function-nodes (FNs) change continuously and dynamically because of channel coefficients, which implies the number of possible combinations is infinite Under this condition, the joint probability distribution is not calculable, and the DDE loop is not closing. We extend the typical binary class DDE to multi-class, where the probability mass function (PMF) of quantized values is computed based on the probability density function (PDF) of corresponding continuous values, which is unlike the original binary DDE This is possible because the PDF of beliefs in AMP can be approximated to the Gaussian distribution with high-accuracy in large-system conditions. To the best of the authors’ knowledge, a LUT-based MUD mechanism based on the IB method and DDE scheme for massive MIMO systems, which is the key contribution of this paper, has not been proposed yet
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