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

Robust Quantitative Susceptibility Mapping via Approximate Message Passing

Purpose: It is challenging to recover magnetic susceptibility in the presence of phase errors, which may be caused by noise or strong local-susceptibility shifts in cases of brain hemorrhage and calcification. We propose a Bayesian formulation for quantitative susceptibility mapping (QSM) where a customized Gaussian-mixture distribution is used to model the long-tailed noise distribution. Theory: Complex exponential functions of the phase are used as nonlinear measurements. Wavelet coefficients of the susceptibility map are modeled by the Laplace distribution. Measurement noise is modeled by a two-component Gaussian-mixture distribution, where the second component is reserved to model the noise outliers. The susceptibility map and distribution parameters are jointly recovered using approximate message passing (AMP). Methods: The proposed AMP with built-in parameter estimation (AMP-PE) is compared with the state-of-the-art nonlinear L1-QSM and MEDI approaches that adopt the L1-norm and L2-norm data-fidelity terms respectively. They are tested on the simulated and in vivo datasets. Results: On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE and SSIM, MEDI achieved the lowest HFEN. On the in vivo datasets, AMP-PE is more robust and better at preserving structural details and removing streaking artifacts in the hemorrhage cases than L1-QSM and MEDI. Conclusion: By leveraging a customized Gaussian-mixture noise prior, AMP-PE achieves better performance in challenging cases of brain hemorrhage and calcification. It is equipped with built-in parameter estimation, which avoids subjective bias from the usual visual-tuning step of in vivo reconstruction.Comment: Keywords: Approximate message passing, Compressive sensing, Parameter estimation, QS

Object-oriented Tools for Distributed Computing

Distributed computing systems are proliferating, owing to the availability of powerful, affordable microcomputers and inexpensive communication networks. A critical problem in developing such systems is getting application programs to interact with one another across a computer network. Remote interprogram connectivity is particularly challenging across heterogeneous environments, where applications run on different kinds of computers and operating systems. NetWorks! (trademark) is an innovative software product that provides an object-oriented messaging solution to these problems. This paper describes the design and functionality of NetWorks! and illustrates how it is being used to build complex distributed applications for NASA and in the commercial sector

A Flexible and Modular Framework for Implementing Infrastructures for Global Computing

We present a Java software framework for building infrastructures to support the development of applications for systems where mobility and network awareness are key issues. The framework is particularly useful to develop run-time support for languages oriented towards global computing. It enables platform designers to customize communication protocols and network architectures and guarantees transparency of name management and code mobility in distributed environments. The key features are illustrated by means of a couple of simple case studies

Run-time scheduling and execution of loops on message passing machines

Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent

Implementing a distributed mobile calculus using the IMC framework

In the last decade, many calculi for modelling distributed mobile code have been proposed. To assess their merits and encourage use, implementations of the calculi have often been proposed. These implementations usually consist of a limited part dealing with mechanisms that are specific of the proposed calculus and of a significantly larger part handling recurrent mechanisms that are common to many calculi. Nevertheless, also the "classic" parts are often re-implemented from scratch. In this paper we show how to implement a well established representative of the family of mobile calculi, the distributed [pi]-calculus, by using a Java middleware (called IMC - Implementing Mobile Calculi) where recurrent mechanisms of distributed and mobile systems are already implemented. By means of the case study, we illustrate a methodology to accelerate the development of prototype implementations while concentrating only on the features that are specific of the calculus under consideration and relying on the common framework for all the recurrent mechanisms like network connections, code mobility, name handling, etc

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

Implementing Session Centered Calculi

Recently, specific attention has been devoted to the development of service oriented process calculi. Besides the foundational aspects, it is also interesting to have prototype implementations for them in order to assess usability and to minimize the gap between theory and practice. Typically, these implementations are done in Java taking advantage of its mechanisms supporting network applications. However, most of the recurrent features of service oriented applications are re-implemented from scratch. In this paper we show how to implement a service oriented calculus, CaSPiS (Calculus of Services with Pipelines and Sessions) using the Java framework IMC, where recurrent mechanisms for network applications are already provided. By using the session oriented and pattern matching communication mechanisms provided by IMC, it is relatively simple to implement in Java all CaSPiS abstractions and thus to easily write the implementation in Java of a CaSPiS process