Sum capacity of massive MIMO systems with quantized hybrid beamforming

Recently, hybrid beamforming, which consists of an analog RF precoder and a digital baseband precoder, has been proposed for massive MIMO systems to reduce the number of RF chains and power consumption at the base station (BS). This paper studies the impact of channel state information (CSI) on the...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings / IEEE International Symposium on Information Theory pp. 320 - 324
Main Authors: An Liu, Lau, Vincent
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.07.2016
Subjects:
Array signal processing
Asymptotic Analysis
Asymptotic properties
Baseband
Beamforming
Channels
Gain
Hybrid systems
Information theory
Massive MIMO
MIMO
MIMO (control systems)
Precoding
Quantized Hybrid Beamforming
Radio frequency
Real-time systems
Signal to noise ratio
Statistics
Sum Capacity
ISSN:2157-8117
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, hybrid beamforming, which consists of an analog RF precoder and a digital baseband precoder, has been proposed for massive MIMO systems to reduce the number of RF chains and power consumption at the base station (BS). This paper studies the impact of channel state information (CSI) on the sum capacity of massive MIMO systems with quantized hybrid beamforming where the RF precoder is selected from a finite size codebook. Two types of CSI at the BS (CSIT) are assumed: full instantaneous CSIT (full channel matrix between the BS and users) and hybrid CSIT (channel statistics plus the low dimensional effective channel matrix after RF precoding). We derive asymptotic sum capacity expressions under these two types of CSIT. We find that, in most cases, exploiting the full instantaneous CSIT can only achieve a marginal SNR gain and hybrid CSIT is sufficient to achieve the first order gain provided by massive MIMO.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:2157-8117
DOI:10.1109/ISIT.2016.7541313