A Study on Replica Generation Using LUT Based on Information Bottleneck for MF-GaBP in Massive MIMO Detection

This paper proposes a symbol replica generation method using look-up table (LUT) based on the information bottleneck (IB) theory in matched filter Gaussian belief propagation (MF-GaBP) for massive multi-input multi-output (MIMO) detection. MF-GaBP serves as an iterative signal detection scheme with low computational complexity by utilizing massive MIMO simplification owing to the law of large numbers. However, when the internal mathematical processes in MF-GaBP are conducted in double precision, a severe processing delay is inevitable. To avoid the impairment, we propose a quantized MF-GaBP detection scheme using predesigned LUTs. The quantization threshold is designed based on the sequential IB (sIB) method for minimizing the mutual information loss. Finally, computer simulations demonstrate that the proposed method significantly reduces the memory occupancy on the basis of table-based processing while suppressing error floor level of the bit error rate (BER) performance.

[1]  Pablo M. Olmos,et al.  Expectation Propagation Detection for High-Order High-Dimensional MIMO Systems , 2014, IEEE Transactions on Communications.

[2]  Shinsuke Ibi,et al.  Design of Adaptively Scaled Belief in Multi-Dimensional Signal Detection for Higher-Order Modulation , 2019, IEEE Transactions on Communications.

[3]  Peng Li,et al.  Multiple output selection-LAS algorithm in large MIMO systems , 2010, IEEE Communications Letters.

[4]  A. Chockalingam Low-complexity algorithms for large-MIMO detection , 2010, 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[5]  Noam Slonim,et al.  The Information Bottleneck : Theory and Applications , 2006 .

[6]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[7]  Gerhard Bauch,et al.  Trellis based node operations for LDPC decoders from the Information Bottleneck method , 2015, 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS).

[8]  Shinsuke Ibi,et al.  Design of Criterion for Adaptively Scaled Belief in Iterative Large MIMO Detection , 2019, IEICE Trans. Commun..

[9]  B. Sundar Rajan,et al.  A Novel Monte-Carlo-Sampling-Based Receiver for Large-Scale Uplink Multiuser MIMO Systems , 2013, IEEE Transactions on Vehicular Technology.

[10]  B. Sundar Rajan,et al.  Low-complexity near-MAP decoding of large non-orthogonal STBCs using PDA , 2009, 2009 IEEE International Symposium on Information Theory.

[11]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[12]  Toshihiko Nishimura,et al.  Low-Complexity Detection Based on Belief Propagation in a Massive MIMO System , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[13]  B. Sundar Rajan,et al.  Large MIMO Systems , 2014 .

[14]  Gerhard Bauch,et al.  Information-Optimum Discrete Signal Processing for Detection and Decoding - Invited Paper , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).