Layered Belief Propagation for Low-complexity Large MIMO Detection Based on Statistical Beams
For wireless communication systems, this work addresses the trade-off between detection performance and computational cost in massive MIMO base stations.
This paper proposes a layered belief propagation detector for large MU-MIMO systems that reduces computational complexity while improving bit error rate performance compared to conventional methods. The method achieves lower complexity with competitive BER.
This paper proposes a novel layered belief propagation (BP) detector with a concatenated structure of two different BP layers for low-complexity large multi-user multi-input multi-output (MU-MIMO) detection based on statistical beams. To reduce the computational burden and the circuit scale on the base station (BS) side, the two-stage signal processing consisting of slow varying outer beamformer (OBF) and group-specific MU detection (MUD) for fast channel variations is effective. However, the dimensionality reduction of the equivalent channel based on the OBF results in significant performance degradation in subsequent spatial filtering detection. To compensate for the drawback, the proposed layered BP detector, which is designed for improving the detection capability by suppressing the intra- and inter-group interference in stages, is introduced as the post-stage processing of the OBF. Finally, we demonstrate the validity of our proposed method in terms of the bit error rate (BER) performance and the computational complexity.