Deep Learning-Aided Finite-Capacity Fronthaul Cell-Free Massive MIMO with Zero Forcing

Manijeh Bashar, Ali Akbari, Kanapathippillai Cumanan, Hien-Quoc Ngo, Alister G. Burr, Pei Xiao, Merouane Debbah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing
technique is used at the CPU to detect the signals transmitted from all users. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine
both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF
use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control.
Original languageEnglish
Title of host publicationIEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)1550-3607
Publication statusPublished - 27 Jul 2020

Publication series

NameIEEE International Conference on Communications (ICC)
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883


Dive into the research topics of 'Deep Learning-Aided Finite-Capacity Fronthaul Cell-Free Massive MIMO with Zero Forcing'. Together they form a unique fingerprint.

Cite this