A Deep Neural Architecture for Real-Time Access Point Scheduling in Uplink Cell-Free Massive MIMO

In this paper, a novel hybrid architecture is proposed combining expert knowledge for optimal power allocation and deep artificial neural networks (ANN) to address the access-point scheduling problem in cell-free massive multiple-input multiple-output (MIMO) communication systems with a serial bandw...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on wireless communications Jg. 21; H. 3; S. 1529 - 1541
Hauptverfasser: Guenach, Mamoun, Gorji, Ali A., Bourdoux, Andre
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
access point scheduling
Algorithms
Artificial neural networks
Bandwidth
Bandwidths
Cell-free massive MIMO system
Coders
Communications systems
Computer architecture
deep artificial neural networks
Encoders-Decoders
Entropy (Information theory)
Image segmentation
Massive MIMO
Neural networks
Optimization
Power control
Real time
Real-time systems
Resource management
serial fronthaul architecture
Task scheduling
ISSN:1536-1276, 1558-2248
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, a novel hybrid architecture is proposed combining expert knowledge for optimal power allocation and deep artificial neural networks (ANN) to address the access-point scheduling problem in cell-free massive multiple-input multiple-output (MIMO) communication systems with a serial bandwidth-limited fronthaul architecture. The scheduling task is formulated as an image segmentation problem for which a supervised encoder-decoder like ANN is proposed. It consists of serially concatenated contraction and expansion layers to maximize the (regularized) cross-entropy, followed by a binary projection to undo the relaxation problem. Besides the robustness to scenarios with a time-varying system load and fronthaul bandwidth, the proposed architecture provides a complexity-efficient solution that fulfills the fronthaul bandwidth constraints and satisfies the real-time considerations. Our experimental results verify the competitive performance of the proposed solution with respect to both nonlinear solvers and state-of-art convex algorithms while the time efficiency of the ANN model outperforms the state of the art, especially, in scenarios with a large number of users.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2021.3104743