IEEE C802.16m-08/462r1
Project
IEEE 802.16 Broadband Wireless Access Working Group <http://ieee802.org/16>
Title
Downlink Multiple Input Multiple Output Design
Date
Submitted
2008-05-05
Source(s)
Yinggang Du, Hongjie Si, Qitao Song, Young Hoon Kwan, Voice: + 86 755 28970190
Martin Liu, Mingyang Sun, Xin Chang, Yi Lu, Yinwei E-mail: duyinggang@huawei.com
Zhao, Qi Li, Yang Tang, Shaowei Ge, Hufei Zhu, Jianmin
Lu
Huawei
Re:
IEEE 802.16m-08/016 “Call for Contributions on Project 802.16m System Description
Document (SDD)”
Target topic: “Downlink MIMO Scheme”
Abstract
The contribution presents the downlink MIMO design.
Purpose
To incorporate the proposals into the 802.16m SDD
Notice
Release
Patent
Policy
This document does not represent the agreed views of the IEEE 802.16 Working Group or any of its subgroups. It
represents only the views of the participants listed in the “Source(s)” field above. It is offered as a basis for
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contained herein.
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and any modifications thereof, in the creation of an IEEE Standards publication; to copyright in the IEEE’s name
any IEEE Standards publication even though it may include portions of this contribution; and at the IEEE’s sole
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Further information is located at <http://standards.ieee.org/board/pat/pat-material.html> and
<http://standards.ieee.org/board/pat>.
1
IEEE C802.16m-08/462r1
Downlink Multiple Input Multiple Output Design
Yinggang Du, Hongjie Si, Qitao Song, Young Hoon Kwan, Martin Liu, Mingyang Sun, Xin Chang,
Yi Lu, Yinwei Zhao, Qi Li, Yang Tang, Shaowei Ge, Hufei Zhu, Jianmin Lu
Huawei
Introduction
The document describes the design of multiple antennas in 802.16m radio interface. In this stage, the main focus is in
downlink MIMO design but we believe the basic design concept also applies to uplink MIMO.
One simple MIMO configuration is shown in Figure 1, where both the links from BS (Base Station) to MS(Mobile Station)
and from MS to BS are MIMO configured:
Base
Station
H
Mobile
Station
Figure 1: MIMO Configuration Diagram
A general signal model for MIMO system is shown in (1):
R  Hs  I  N
(1)
Where the received signal R is a Nr*1 vector, the channel gain matrix H is a Nr*Nt matrix and Nr is the number of receive
antenna, Nt is the number of transmit antenna, the transmitted signal s is a Nt*1 vector, I and N are the intereference and
additive white Gaussian noise respectively.
In our design, the configuration baseline is 2T2R for downlink transmission and 4T2R and 4T4R are also considered.
The spatial diversity (SD) may be used in the control and data channel to improve the quality of the signal. The spatial
multiplexing (SM) of modulation symbol streams to a single MS using the same time-frequency resource may be
considered in our design to improve the capacity of the system. Furthermore, for a single MS, the MIMO mode (SM or
SD) may be adapted to the channel quality in each subband.
Based on the number of users simultaneously supported, MIMO can be divided into single user MIMO (SU-MIMO) and
multiuser MIMO(MU-MIMO). Single Code Word (SCW) can be only for single user while Multiple Code Word (MCW)
can be for either single user or multiple users. Multi-User MIMO, i.e. SDMA, is supported to achieve spatial division
multiplexing of modulation symbol streams to different MSs using the same time-frequency resource. While in uplink,
virtual MIMO may be supported. Moreover, Code-book-based pre-coding is supported.
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IEEE C802.16m-08/462r1
To simplify the document, we directly give the text proposal.
Text Proposal to SDD
------------------------------------Start text proposal------------------------------------
[Adopt the following text in the ToC of P802.16m System Description Document (SDD)]
11.x MIMO
11.x.1 Single User MIMO
11.x.1.1 SCW (Single Code Word)
In SCW mode, the signal stream encoded and modulated before it is mapped into NT antennas. Based on the rank r of the
channel, a rate r SCW packet can be supported. Matrix A, B, C and D are defined depending on different rates.
Considering the fact that the control messages should be received by every target MS, we suggest the rate 1 transmission
to guarantee the reception reliability for control information.
11.x.1.1.1 2Tx
With two transmit antennas, rate 1 (r=1) Matrix A with spatial diversity gain of 2 is applied with Alamouti design:
s
A 1
s2
 s2* 

s1* 
where the ith row gives the symbols transmitted in ith antenna and the jth column gives the symbols transmitted in j th time
slot/subcarrier and it is same thereinafter.
With two transmit antennas, the default choices are: 1) Matrix A; 2) s1 and s2 belong to same packet. If there are other
choices, informing message is needed in explicit or implicit manner.
Rate 2 (r=2) Matrix B with spatial multiplexing order 2 is:
s 
B   1
 s2 
Since the rank information r is quasi static and can be exploited from long term statistics, it is possible to apply the spatial
multiplexing of order r for high mobile speed transmission or E-MBS transmission if the target users satisfy the rank
condition, which also applies to the system with 4 transmit antennas.
11.x.1.1.2 4Tx
With four transmit antennas, rate 1 (r=1) matrix As with spatial diversity gain of 2 are applied:
 s1

s
A1   2
0

 0
 s2*
*
1
0
s
0
0
s3
0
s4
 s1
0 


0 
0
, A2  
*
s2
 s4

* 
s3 
 0
 s2*
0
0
s1*
s3
0
0
s4
 s1
0 

*
 s4 
0
, A3  
0
0 

* 
s3 
 s2
 s2*
0
0
s3
0
s1*
s4
0
0 

 s4* 
s3* 

0 
These matrices are permutated one by one to achieve the diversity both in spatial and time (frequency) domain.
For rate 2 (r=2) transmission, the following matrix Bs with hybrid spatial diversity and spatial multiplexing are applied:
3
IEEE C802.16m-08/462r1
 s1

s
B1 =  2
 s3

 s 4
 s 2* 

s1* 
 s 4* 

s 3* 
 s1

s
B4=  2
 s3

 s4
 s1
 s2* 

* 
s1 
s
B5=  3
* 
 s2
s4

*
 s3 
 s4
 s1

s
B2=  3
 s2

 s4
 s2* 

 s4* 
s1* 

s3* 
 s2* 

s4* 
s1* 

 s3* 
 s1

s
B3=  3
s4

 s 2
 s1

s
B6=  3
 s4

 s2
 s 2* 

 s 4* 
s 3* 

s1* 
 s2* 

s4* 
 s3* 

s1* 
In each matrix, there is one Alamouti pair and each occupies two transmit antennas. These matrices are permutated one by
one to achieve the diversity both in spatial and time (frequency) domain.
For rate 3 (r=3) transmission, the following matrix Cs with hybrid spatial diversity and spatial multiplexing are applied:
 s1

s
C1   2
 s3

 s4
 s2*
s1*
s7
s8
s5
s6
s9
s10
 s1
s11 


s12 
s
,C2   2
* 
 s3
 s10

* 
s9 
 s4
 s3*
s5
s7
s8
s1*
s6
s9
s10
 s1
s11 

* 
 s10 
s
, C3   2
 s3
s12 

* 
s8 
 s4
s4*
s5
s7
s8
s6
s1*
s9
s10
s11 

s10* 
.
s9* 

s12 
These matrices are permutated one by one to achieve the diversity both in spatial and time (frequency) domain. Moreover,
the computational complexity of the SIC (successive interference cancellation) receiver for the above matrix Cs can be
similar to that of the SIC receiver for 3×3 MIMO.
Rate 4 (r=4) Matrix D with spatial multiplexing of order 4 is:
 s1 
s 
D   2
 s3 
 
s4 
Since the number of transmit antennas is a fixed parameter for a certain base station, there is no ambiguity on matrices
between 2 Tx configuration and 4 Tx configuration.
11.x.1.2 MCW (Multiple Code Word)
In MCW mode, the signal stream is divided into M packets and each packet is mapped to a stream before it is
independently encoded and modulated (M≤r).
Generally, Matrix A in 2.1 is applicable only to SCW. Matrices B, C and D in 2.1 can be reused for MCW mode. One
simple extension is to let M=r. With this extension assumption, it should be noticed that the symbols within one Alamouti
design belong to same packet.
To obtain the full spatial diversity for each packet, the permutation can be introduced into the MCW transmission. For 2
Tx, the permutation can be like this:
a b 
P

b a 
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IEEE C802.16m-08/462r1
Where a and b are for two independent data streams transmitted in parallel and a is the first layer and b is the second layer.
In the expression, two ‘a’ represent two different symbols in the same layer.
For 4 Tx, a simplified method to permutate the packets independent of the rank is:
a

b
P= 
c

 d
c
d
b
a
d
c
a
b
b  a

a c
d  b

c   d
c
b
d
a
d
a
c
b
b  a

d  c
a  d

c   b
b
d
c
a
c
b
a
d
d

a
b

c 
Where a, b, c and d are four data stream transmitted in parallel, of which each is encoded independently. And a, b, c and d
are respectively the first detected layer, the second detected layer, the third detected layer and the last detected layer.
11.x.2 Multiple User MIMO
Owing to the spatial multiplexing capability of MIMO system, it is possible for BS to schedule multiple users in the same
physical resource for information transmission, which is called SDMA (Spatial Division Multiple Access).
It is natural to take the MU MIMO as a special example of single user MCW described above. In single user MIMO
senario, all packets belong to a same user. While in MU MIMO cases, different users take different packets and the mode
itself can be looked on as a MCW mode. However, to reduce the ambiguity in this contribution, when we mention MCW,
it refers to single user case only.
In downlink transmission, when a BS has four antennas, it can support 2 users with 2 receiving antennas each. If we limit
one user can only have one stream, a 4-Tx BS can support up to 4 users. Obviously, these parallelly transmitted users will
cause interference to each other. Hence precoding is introduced to reduce or mitigate the interference. The BS should have
the ability to independently choose the precoding vectors for the UEs scheduled in the same physical resource.
Owing to the interference caused by each other, different interleavers for different streams are introduced. The function of
interleaver in transmitter is to suppress interference of spatial multiplexing by randomizing data of different users, and the
receiver suppress the multiple access interference by iterative processing as the different user adopts different interleaver,
especially for the cell edge users. If the interleavers are generated independently and randomly, the affection of
suppressing MAI will be optimal by iterative processing.
Based on the codebook type, the MU MIMO can be categorized as:

Linear MU MIMO
This scheme assumes that the transmitted signal is generated by a linear combination of data symbols contained in
a vector. Its advantage lies in its relatively simple computational complexity.
Linear MU MIMO can be further divided into two categories:

Unitary MU-MIMO
If the assembled precoding matrix is unitary, it is defined as unitary MU-MIMO, where the precoding vectors are
orthogonal to each other to further simplify the processing. However, it is hard to keep the orthogonality for scheduled
UEs in real transmissions.

Non-unitary MU-MIMO
If the assemble precoding matrix could be unitary or non-unitary, it is defined as non-unitary MU-MIMO, which
relaxes the orthoganlity limitation of the precoding vectors. The precoding vectors with lower cross-correlation could also
be scheduled together. Since Non Unitary precoding is a more general and less restricted case of unitary precoding, the
gains could possibly be higher, but possibly at the cost of higher control overhead. In non unitary based precoding where
5
IEEE C802.16m-08/462r1
ZF-beamforming is one example, the MS reports a quantized version of the channel based on some known codebook. The
BS then collects all the quantized channels and constructs a precoding matrix as a function of the quantized channels of
the scheduled users. This could lead to large DL control overhead and this is why dedicated pilots are used to indicate this
information. However, it is also true that under certain conditions the ZF action of the BS can be made transparent to the
MS and no additional control overhead is needed. Furthermore, since the non-unitary MU-MIO is more sensitive to CSI
quantization error, which can be partly solved by quantizing in an iterative manner

Nonlinear MU-MIMO
It assumes that the transmitted signal is generated by a non-linear combination of data symbols contained in a
vector, which requires higher computational complexity and more signaling overhead but can provide a better
performance than linear techniques.
11.x.3 Cooperative MIMO
The cooperative MIMO can apply to be among BSs, BS and RS, and RS. The principle is similar. There should be a
master station to tell all the antennas in the different BS(or RS) what and how to transmit. In cooperation mode, different
BS(RS) may transmit same or different signals based on the decision of master station. That is, the SFN transmission of
both SU-MIMO and MU-MIMO should also be supported.
11.x.4 MBS MIMO
Since the baseline configuration is 2T2R for downlink transmission, it is possible to apply MIMO technology in MBS
transmission.
11.x.4.1 Open loop spatial diversity
It is natural to apply STBC in MBS transmission. In MBS, the importance of different bits are naturally different so
unequal power allocation can be used to achieve the UEP property. The BS uses different powers for different signal
depending on the error protection level of the transmitted signals. Because the symbols in an Alamouti code can be
decoded independently while they go through same channels, it is possible to allocate the symbols within one Alamouti
code with different importance, i.e., BS delivers these symbols with different power while they can achieve the same
transmit diversity.
11.x.4.2 Open loop spatial multiplexing
To increase the spectral efficiency, spatial multiplexing should be supported for MBS. In that case, the MCS setting of
different code words affects the receiver’s performance directly. The optimum MCS should be set based on the effective
SINR of each code word in each MS. However, it is not feasible for MBS for large feedback signaling from multiple MS.
And, it is easy to select the optimum MCS if different code words have equal SINR for one MS, for example, equal MCS
for different code words without SIC or unequal MCS for interference cancellation gain with SIC. To average the
effective SINR of each code word for MS, using dynamic antenna mapping is an effective method. For example, with 2
Tx, the permutation matrix shown above can be reused:
a b 
P

b a 
------------------------------------End text proposal------------------------------------
6