Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multipl...
Saved in:
Main Authors: | Jun-Yong Jang, Chan-Yeob Park, Beom-Sik Shin, Hyoung-Kyu Song |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/d0d86f43fcf743998cbb33968d66b70b |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
by: Omnia Mahmoud, et al.
Published: (2021) -
Scalable user selection in FDD massive MIMO
by: Xing Zhang, et al.
Published: (2021) -
Hybrid precoding for mmWave massive MU-MIMO systems with overlapped subarray: A modified GLRAM approach
by: Ting Ding, et al.
Published: (2021) -
A Capacity Achieving MIMO Detector Based on Stochastic Sampling
by: Jonathan C. Hedstrom, et al.
Published: (2021) -
An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
by: Ignacio Trojaola, et al.
Published: (2021)