482 research outputs found
A Low Complexity MIMO Detection Based on Pair-Wise Markov Random Fields
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Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input
multiple-output (MIMO) introduces dramatic capacity improvements along with
very high beamforming and spatial multiplexing gains. When developing efficient
physical layer algorithms for massive MIMO systems, message passing is one
promising candidate owing to the superior performance. However, as their
computational complexity increases dramatically with the problem size, the
state-of-the-art message passing algorithms cannot be directly applied to
future 6G systems, where an exceedingly large number of antennas are expected
to be deployed. To address this issue, we propose a model-driven deep learning
(DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by
considering the low complexity of the AMP algorithm and adaptability of GNNs.
Specifically, the structure of the AMP-GNN network is customized by unfolding
the approximate message passing (AMP) algorithm and introducing a graph neural
network (GNN) module into it. The permutation equivariance property of AMP-GNN
is proved, which enables the AMP-GNN to learn more efficiently and to adapt to
different numbers of users. We also reveal the underlying reason why GNNs
improve the AMP algorithm from the perspective of expectation propagation,
which motivates us to amalgamate various GNNs with different message passing
algorithms. In the simulation, we take the massive MIMO detection to exemplify
that the proposed AMP-GNN significantly improves the performance of the AMP
detector, achieves comparable performance as the state-of-the-art DL-based MIMO
detectors, and presents strong robustness to various mismatches.Comment: 30 Pages, 7 Figures, and 4 Tables. This paper has been submitted to
the IEEE for possible publication. arXiv admin note: text overlap with
arXiv:2205.1062
Novel LDPC coding and decoding strategies: design, analysis, and algorithms
In this digital era, modern communication systems play an essential part in nearly every aspect of life, with examples ranging from mobile networks and satellite communications to Internet and data transfer. Unfortunately, all communication systems in a practical setting are noisy, which indicates that we can either improve the physical characteristics of the channel or find a possible systematical solution, i.e. error control coding. The history of error control coding dates back to 1948 when Claude Shannon published his celebrated work “A Mathematical Theory of Communication”, which built a framework for channel coding, source coding and information theory. For the first time, we saw evidence for the existence of channel codes, which enable reliable communication as long as the information rate of the code does not surpass the so-called channel capacity. Nevertheless, in the following 60 years none of the codes have been proven closely to approach the theoretical bound until the arrival of turbo codes and the renaissance of LDPC codes. As a strong contender of turbo codes, the advantages of LDPC codes include parallel implementation of decoding algorithms and, more crucially, graphical construction of codes. However, there are also some drawbacks to LDPC codes, e.g. significant performance degradation due to the presence of short cycles or very high decoding latency. In this thesis, we will focus on the practical realisation of finite-length LDPC codes and devise algorithms to tackle those issues.
Firstly, rate-compatible (RC) LDPC codes with short/moderate block lengths are investigated on the basis of optimising the graphical structure of the tanner graph (TG), in order to achieve a variety of code rates (0.1 < R < 0.9) by only using a single encoder-decoder pair. As is widely recognised in the literature, the presence of short cycles considerably reduces the overall performance of LDPC codes which significantly limits their application in communication systems. To reduce the impact of short cycles effectively for different code rates, algorithms for counting short cycles and a graph-related metric called Extrinsic Message Degree (EMD) are applied with the development of the proposed puncturing and extension techniques. A complete set of simulations are carried out to demonstrate that the proposed RC designs can largely minimise the performance loss caused by puncturing or extension.
Secondly, at the decoding end, we study novel decoding strategies which compensate for the negative effect of short cycles by reweighting part of the extrinsic messages exchanged between the nodes of a TG. The proposed reweighted belief propagation (BP) algorithms aim to implement efficient decoding, i.e. accurate signal reconstruction and low decoding latency, for LDPC codes via various design methods. A variable factor appearance probability belief propagation (VFAP-BP) algorithm is proposed along with an improved version called a locally-optimized reweighted (LOW)-BP algorithm, both of which can be employed to enhance decoding performance significantly for regular and irregular LDPC codes. More importantly, the optimisation of reweighting parameters only takes place in an offline stage so that no additional computational complexity is required during the real-time decoding process.
Lastly, two iterative detection and decoding (IDD) receivers are presented for multiple-input multiple-output (MIMO) systems operating in a spatial multiplexing configuration. QR decomposition (QRD)-type IDD receivers utilise the proposed multiple-feedback (MF)-QRD or variable-M (VM)-QRD detection algorithm with a standard BP decoding algorithm, while knowledge-aided (KA)-type receivers are equipped with a simple soft parallel interference cancellation (PIC) detector and the proposed reweighted BP decoders. In the uncoded scenario, the proposed MF-QRD and VM-QRD algorithms are shown to approach optimal performance, yet require a reduced computational complexity. In the LDPC-coded scenario, simulation results have illustrated that the proposed QRD-type IDD receivers can offer near-optimal performance after a small number of detection/decoding iterations and the proposed KA-type IDD receivers significantly outperform receivers using alternative decoding algorithms, while requiring similar decoding complexity
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers
Deep neural networks (NNs) are considered a powerful tool for balancing the
performance and complexity of multiple-input multiple-output (MIMO) receivers
due to their accurate feature extraction, high parallelism, and excellent
inference ability. Graph NNs (GNNs) have recently demonstrated outstanding
capability in learning enhanced message passing rules and have shown success in
overcoming the drawback of inaccurate Gaussian approximation of expectation
propagation (EP)-based MIMO detectors. However, the application of the
GNN-enhanced EP detector to MIMO turbo receivers is underexplored and
non-trivial due to the requirement of extrinsic information for iterative
processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo
receivers, which realizes the turbo principle of generating extrinsic
information from the MIMO detector through a specially designed training
procedure. Additionally, an edge pruning strategy is designed to eliminate
redundant connections in the original fully connected model of the GNN
utilizing the correlation information inherently from the EP algorithm. Edge
pruning reduces the computational cost dramatically and enables the network to
focus more attention on the weights that are vital for performance. Simulation
results and complexity analysis indicate that the proposed MIMO turbo receiver
outperforms the EP turbo approaches by over 1 dB at the bit error rate of
, exhibits performance equivalent to state-of-the-art receivers with
2.5 times shorter running time, and adapts to various scenarios.Comment: 15 pages, 12 figures, 2 tables. This paper has been accepted for
publication by the IEEE Transactions on Signal Processing. Copyright may be
transferred without notice, after which this version may no longer be
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