Downlink MU-MIMO With QoS Aware Transmission: Precoder Design and Performance Analysis

In this paper, a downlink wireless communication channel is considered. The base station (BS) has common data for all users, unicast data for a set of intended users, and transmits the superposition of these messages. This setting neither falls into the non-orthogonal multiple access (NOMA) nor into...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 18; no. 2; pp. 969 - 982
Main Authors: Yalcin, Ahmet Zahid, Yuksel, Melda, Bahceci, Israfil
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Communication channels
Complexity
MIMO broadcast channel
MIMO communication
MU-MIMO
multi-group multicast
Multicasting
NOMA
Optimization
precoder design
Precoding
Receivers
Superposition (mathematics)
Unicast
Wireless communication
Wireless communications
zero-forcing
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:In this paper, a downlink wireless communication channel is considered. The base station (BS) has common data for all users, unicast data for a set of intended users, and transmits the superposition of these messages. This setting neither falls into the non-orthogonal multiple access (NOMA) nor into the multi-group multicasting literatures. In NOMA systems, the BS has unicast data for all users, and multiple users share the same resources. In multi-group multicasting, there are non-overlapping groups, each demanding a different multicast message. This paper studies precoder design to achieve maximum weighted sum rate (WSR). It is first shown that the precoders designed for WSR maximization and weighted minimum mean square error (WMMSE) minimization are equivalent. Second, an iterative low complexity algorithm (named WMMSE), based on WMMSE transmit precoders and receivers, is proposed. Another low-complexity precoder, the phase aligned zero forcing (PAZF) precoder, is also introduced. The results show that both algorithms converge fast. The WMMSE algorithm outperforms both PAZF and the zero-forcing (ZF) precoder for all signal-to-noise ratio ranges. It offers better interference management and high coherent combining gains for common data while PAZF finds the optimal phase rotation on the ZF precoder, and increases coherent combining gains.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2018.2886903