University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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

Bashar, Manijeh, Akbari, Ali, Cumanan, Kanapathippillai, Quoc Ngo, Hien, Burr, Alister G., Xiao, Pei and Debbah, Merouane (2020) Deep Learning-Aided Finite-Capacity Fronthaul Cell-Free Massive MIMO with Zero Forcing In: IEEE ICC 2020, 7 - 11 June, 2020, Dublin, Ireland.

ICC2020_overleaf.pdf - Accepted version Manuscript

Download (1MB) | Preview


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, thanks to the mapping obtained using DCNN.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Cumanan, Kanapathippillai
Quoc Ngo, Hien
Burr, Alister G.
Debbah, Merouane
Date : 27 January 2020
Funders : H2020-MSCARISE- 2015, UK Research and Innovation Future Leaders Fellowships, U.K. Engineering and Physical Sciences Research Council
Grant Title : H2020-MSCARISE- 2015 Grant
Depositing User : James Marshall
Date Deposited : 10 Apr 2020 16:33
Last Modified : 10 Apr 2020 16:33

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800