Distributed Unsupervised Learning for Combinatorial User Assignment in mmWave Cell-Free Massive MIMO Using Graph Neural Networks

IEEE Transactions on Wireless Communications, vol. 25, pp. 18531–18546

Abstract

Smaller cells have been the most important contributor to throughput improvement since the birth of cellular networks. They are likely to evolve further in the shift to cell-free massive MIMO (CF mMIMO), where multiple closely placed access points (APs) collaborate to serve users. This scheme is particularly suitable for millimeter wave (mmWave) communication, which enables very high data rates with its large bandwidth, but encounters severe challenges of high path loss and blockage. The CF mMIMO network is a good countermeasure to these two challenges by utilizing overlapping signals from different APs and macro-diversity. In this work, we demonstrate that mmWave CF mMIMO network optimization is largely an AP-user assignment problem. To solve this large-scale, nondifferentiable problem, we propose an unsupervised machine learning (ML) approach, which looks for the optimal solution autonomously without labels. A customized graph neural network architecture tailored to the problem properties is proposed, which enables distributed optimization without a central unit, allows for a varying number of users, and hierarchical permutation-equivariance of APs and users. A teacher-student model is applied to prune the graph, where the teacher model uses a fully connected graph for maximum performance, and the student model uses a pruned graph to reproduce the teacher's behavior with less communication in fronthaul. Moreover, a special training method is designed, which relaxes the combinatorial problem to a continuous one. In this way, we can apply gradient-based neural network training. An entropy-inspired penalty is introduced to make the relaxed problem equivalent to the original one. The analytical augmented Lagrangian method is combined with ML for the constrained optimization. Simulation results show that the proposed approach outperforms baselines in both performance and computation time. In addition, with a properly pruned graph, the proposed approach performs inference in a distributed manner with sparse message passing between APs, realizing a low signaling overhead in fronthaul, and a performance close to the fully connected graph.

Authors 10

1. Bile Peng

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

2. Bihan Guo

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

3. Karl-Ludwig Besser

   Linköping University

   Affiliation as printed

   Department of Electrical Engineering, Linköping University, Linköping, Sweden

4. Luca Kunz

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

5. Zhengyang Li

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

6. Ramprasad Raghunath

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

7. Anke Schmeink Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   Chair of Information Theory and Data Analytics, RWTH Aachen University, Aachen, Germany

8. Eduard Axel Jorswieck

   Technische Universität Braunschweig

   Affiliation as printed

   Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany

9. Giuseppe Caire

   Technische Universität Berlin

   Affiliation as printed

   Communications and Information Theory Group, Technische Universität Berlin, Berlin, Germany

10. H. Vincent Poor

    Princeton University

    Affiliation as printed

    Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA