Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems

Liu, Lei; Yuen, Chau; Guan, Yong Liang; Liu, Ying; Su, Yuping

doi:10.1109/twc.2016.2585481

Cited by 106 publications

(92 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AMP algorithms essentially implement the standard rules of BP message passing [43] and all messages are approximated with univariate Gaussian distributions. Among AMP methods for MIMO detection, we can mention the CHEMP algorithm in [28] and GMPID in [30]. An approximation to p(u|y) can be constructed from the AMP marginals using the Bethe reparameterization [43].…”

Section: A Posterior Approximation and Inferencementioning

confidence: 99%

“…We can mention the channel hardening-exploiting message passing (CHEMP) in [28] and the Gaussian Message Passing Iterative Detector (GMPID) in [29], [30]. Both methods have been shown to be effective (close to SD methods) for large MIMO systems with QPSK constellations.…”

mentioning

confidence: 99%

“…Both methods have been shown to be effective (close to SD methods) for large MIMO systems with QPSK constellations. However, asymptotic analysis of this type of algorithms shows that they do not perform well with high-order QAM constellations unless the number r of receiving antennas is much larger than the number m of transmitting antennas [30], [31]. An improved version of the GMPID algorithm called SA-GMPID is shown to asymptotically converge to the MMSE detection solution even for the case m/r > 1 [32].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference

Cespedes

Olmos

Sanchez-Fernandez

et al. 2018

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

In this paper we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the Expectation Consistency (EC) framework, which describes the approximate inference problem as an optimization over a non-convex function. EC generalizes algorithms such as Belief Propagation and Expectation Propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input-output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check (LDPC) channel code.

show abstract

Section: A Posterior Approximation and Inferencementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference

Cespedes

Olmos

Sanchez-Fernandez

et al. 2018

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…DNN-AIDED MP-BSBL ALGORITHM The factor graph in Fig. 2 is densely connected, which results in the correlation problem of the Gaussian messages [13]- [15]. To address this problem, we propose a DNN-MP-BSBL algorithm to imposes weights on the Gaussian message update and the MF message update.…”

Section: Is Briefed As Followsmentioning

confidence: 99%

DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation

Zhang

Liu

Huang

et al. 2019

2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

Self Cite

View full text Add to dashboard Cite

Faced with the massive connection, sporadic transmission, and small-sized data packets in future cellular communication, a grant-free non-orthogonal random access (NORA) system is considered in this paper, which could reduce the access delay and support more devices. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In this algorithm, the message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the weights could alleviate the convergence problem of the MP-BSBL algorithm. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations.Index Terms-deep neural network, sparse Bayesian learning, grant-free, user activity detection, channel estimation arXiv:1910.04154v1 [cs.IT]

show abstract

“…There are many off-the-shelf MUD algorithms which can be directly applied to the massive MIMO BS. For example, when the data symbols are assumed Gaussian distributed, the performance of the GMPID is proven to converge to that of the minimum mean square error (MMSE) estimator [34]. Furthermore, aided by a 10-bit superposition coded modulation scheme [43], [44], the GMPID can still guarantee excellent decoding accuracy for discrete data symbols.…”

Section: E Output and Decisionmentioning

confidence: 99%

Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications

Zhang

Liu

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

The massiveness of devices in crowded Machineto-Machine (M2M) communications brings new challenges to existing random-access (RA) schemes, such as heavy signaling overhead and severe access collisions. In order to reduce the signaling overhead, we propose a fixed-symbol aided RA scheme where active devices access the network in a grant-free method, i.e., data packets are directly transmitted in randomly chosen slots. To further address the access collision which impedes the activity detection, one fixed symbol is inserted into each transmitted data packet in the proposed scheme. An iterative message passing based activity detection (MP-AD) algorithm is performed upon the received signal of this fixed symbol to detect the device activity in each slot. In addition, the deep neural network-aided MP-AD (DNN-MP-AD) algorithm is further designed to alleviate the correlation problem of the iterative message passing process. In the DNN-MP-AD algorithm, the iterative message passing process is transferred from a factor graph to a DNN. Weights are imposed on the messages in the DNN and further trained to improve the accuracy of the device activity detection. Finally, numerical simulations are provided for the throughput of the proposed RA scheme, the accuracy of the proposed MP-AD algorithm, as well as the improvement brought by the DNN-MP-AD algorithm.Index Terms-M2M communications, random access, message passing detection, deep neural network.

show abstract

Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems

Cited by 106 publications

References 53 publications

Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference

Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference

DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation

Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications

Contact Info

Product

Resources

About