Layered Belief Propagation for Low-Complexity Large MIMO Detection Based on Statistical Beams

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

Ibi

et al. 2019

Self Cite

This paper proposes concatenated beam-and antenna-domain belief propagation (BP) based signal detector for realizing low-complexity large multiuser multi-input multioutput (MU-MIMO) detection. As a low-complexity strategy for large-scale MU detection (MUD), group-specific signal detection in the beam domain has been proposed, where user equipments (UEs) are grouped based on the long-term channel statistics at a base station (BS) to design outer beamformer (OBF). In this case, the BP-based algorithm is effective as the subsequent signal detector to compensate for the performance degradation due to the dimensionality reduction caused by the OBF. However, the convergence characteristics of beam-domain BP is degraded as compared to the antenna-domain BP due to increased correlation between the beliefs. To avoid the impairments, we propose a novel concatenated structure of beam-and antenna-domain BP layers, which allows us to benefit from the advantages of both domains while maintaining low signal processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate (BER) performance both in the coded and uncoded cases and the computational complexity.

“…In simulations, the NS method [16] is utilized for suppressing the harmful impacts of spatial fading correlation. In this paper, the number of sets is 4 as in the case of [5].…”

Section: B Antenna-domain Mf-based Bp Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Concatenated Beam- and Antenna-domain Layered Belief Propagation for Large MIMO Detection

Takahashi

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

Ibi

et al. 2019

Self Cite

“…Recent efforts at complexity reduction, for both uplink and downlink, include two-stage beamforming strategies [1]- [4]. In [1], a statistical outer beamformer based on grouping mobiles based on similar correlation matrices reduces the effective spatial dimension of the equivalent channels [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…In [1], a statistical outer beamformer based on grouping mobiles based on similar correlation matrices reduces the effective spatial dimension of the equivalent channels [1], [2]. This is followed by an inner beamformer that suppresses both intra-and inter-group interference, resulting in significant reduction in computation [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Beamspace Local LMMSE: An Efficient Digital Backend for mmWave Massive MIMO

Abdelghany

Madhow

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

2019

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We explore an all-digital architecture for a mmWave massive MIMO cellular uplink in which the number of users scales with the number of antenna elements at the base station. We consider the design of multiuser detection strategies after a spatial FFT, which concentrates the energy of each user onto a few FFT bins in "beamspace." In this paper, we propose and investigate a local LMMSE receiver that exploits this property, using a small window in beamspace to demodulate each user. The proposed architecture is computationally efficient: the required window size depends on load factor (the number of users divided by the number of antenna elements) and does not scale with the number of elements. We also show that adaptive implementations of such local LMMSE receivers naturally extend to provide implicit channel estimation.

Low-Complexity Beam-Domain Channel Estimation with Long-Term Statistics for Large MIMO Detection

Takahashi

2019 IEEE Global Communications Conference (GLOBECOM)

Ibi

et al. 2019

Self Cite

This paper proposes low-complexity beam-domain channel estimation using long-term channel statistics in belief propagation (BP) based large multi-input multi-output (MIMO) detection. When the channel correlation matrix between the base station (BS) and each user equipment (UE) is available and used as prior information, maximum a-posteriori probability (MAP) estimation provides the optimal estimation performance. However, it requires undesirably complex large-scale matrix operations at any time the channel statistics is changed. By appropriately selecting beam-domain angular bins for each UE, the proposed method allows us to significantly reduce the computational cost while maintaining the near-optimal performance in terms of the mean square error (MSE) of estimated channel. The selection threshold is adaptively determined based on the prior information such as the channel correlation matrix, statistical beam, and receive SNR. For the subsequent BP-based signal detection, an appropriate covariance matrix is designed while considering the detrimental impact of channel estimation errors. Numerical results show that the proposed method can reduce the computational cost to less than 4% as compared to the MAP estimation, while providing similar MSE performance.