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I. INTRODUCTION
Recently, the high-rate data transmission has been one
of key issues in wireless mobile communications. Various
classes of multimedia traffic need to be supported under
the wireless LAN (local area network) as well as cellular
environments. A number of approaches have been
considered to improve the performance of capacity and
spectral efficiency in wireless communication systems.
MIMO (multiple-input multiple-output) is an emerging
technology offering high spectral efficiency with the
increased link reliability and interference suppression
[1]~[4].
There are various categorized schemes of MIMO,
depending on the target performance characteristics.
Transmit diversity uses multiple transmit antennas to
enhance the link reliability by transmitting multiple copied
signaling in various ways [5]. Receive diversity is
reciprocal to the transmit diversity, where multiple receive
antennas are used to improve the error performance by
combining the received signals through multiple receive
antennas. Space-time coding is a popular solution for
diversity gain and/or coding gain, which can be easily
combined with all kinds of multiple antenna systems.
Space-time block coding has been already adopted in 3GPP
(3rd generation partnership project) standardizations, which
is characterized by its simple transmit and receive
structures for implementations [6]~[9]. Space-time trellis
coding is another type of space-time coding, achieving
diversity gain and coding gain at the cost of computational
complexity. Beamforming is a good candidate for
interference suppression and high capacity performance
with a long history of research work. At first, it was used
for military applications. Smart antenna exploits
beamforming to boost up the system capacity and reduce
the interference in cellular environments. Adaptive
beamforming keeps updating the complex weights of array
for an optimal SINR (signal-to-interference-plus-noise
ratio), while switched beamforming switches between the
predetermined beams selected from a library of weights
based on the received signal strength measurements.
Spatial multiplexing is the most advanced MIMO scheme.
1
In wireless mobile communications, the high-rate data transmission is significant to support different classes of
multimedia services. Multiple-input multiple-output (MIMO) is an emerging technology which offers high spectral
efficiency over wireless links. Recently, the industrial organizations have proposed their MIMO techniques in 3rd
generation partnership project (3GPP) standardizations. In 3GPP, various multi-antenna schemes are on active
discussions, especially when combined with high speed downlink packet access (HSDPA). In this paper, we investigate
the proposed MIMO techniques which have been agreed to be included in 3GPP MIMO working item technical report
(WI-TR).
Keywords: MIMO, 3GPP, HSDPA, MU-MIMO, SU-MIMO, Wireless scheduling
Sungjin Kim, Hojin Kim : Samsung Advanced Institute
Juho Lee: Samsung Electronics
Kwang Bok Lee: Seoul National University
An Overview of MIMO Technologies
for Enhanced 3GPP HSDPA
Sungjin Kim ·Hojin Kim ·Juho Lee ·Kwang Bok Lee
Lucent developed the Bell laboratories layered space-time
(BLAST) architecture, which may be categorized into vertical
BLAST (V-BLAST) and diagonal BLAST (D-BLAST) [1],
[10]. BLAST-based transmission schemes exploits spatial
multiplexing gain through different data streams on each
transmit antenna [1]. In V-BLAST, independent channel
coding is applied to each sub-layers, i.e. the data substream
corresponding to each transmit antenna. D-BLAST applies
independent coding across time as well as the antennas (sub-
layers) with high complexity. All sub-layers for both schemes
consist of the same rate code and the same order modulation,
of which the properties are modified in the enhanced schemes.
Based on the above basic MIMO technologies, a lot of
hybrid methods have been brought up for higher performance
gain. Most of them are some mixture of the basic MIMO
algorithms mentioned above. On the other hand, MIMO can
be also separated into two structures which are open-loop
(OL) and closed-loop (CL) architectures. In open-loop
MIMO (OL-MIMO), the transmitter has no channel
information for data transmissions, and hence fixed
transmit parameters are used. On the other hand, closed-
loop MIMO (CL-MIMO) exploits the channel state
information for transmissions [11], [12]. Using feedback
signaling, the transmitter adapts its parameters according
to channel conditions. The level of adaptation varies,
depending on the feedback information. CL-MIMO can be
easily combined together with adaptive modulation and
coding scheme for higher spectral efficiency. Thus, CL-
MIMO can achieve higher performance gain over OL-
MIMO at the expense of complexity. The performance of
CL-MIMO heavily depends on the channel conditions.
That is, if channel changes rather fast, feedback signaling
might be useless because the channel information at the
transmitter cannot follow up the current channel conditions.
In this case, OL-MIMO may be preferred for better
performance. CL-MIMO can be more applicable to slow
fading environments.
Most previous MIMO schemes are based on point-to-
point communications, i.e., single-user MIMO (SU-
MIMO). For the evaluation of system performance, the
multi-user environment needs to be considered [13], [14].
SU-MIMO systems focus on link performance without
any higher layer assumptions [15]. In multi-user MIMO
(MU-MIMO) systems, all users are coordinated for
communications by considering scheduling algorithms and
QoS (quality of service) requirements of each user. In the
case of CL-MIMO with multi-user communications, the
complexity is so high and a lot of research issues are brought
up, including feedback signaling, multi-user scheduling, and
transmit/receive optimization, etc [16], [17].
Recently, the industrial organizations have proposed
their MIMO techniques in 3rd generation partnership
project (3GPP) standardizations. In 3GPP, various multi-
antenna schemes are on active discussions, especially
when combined with high speed downlink packet access
(HSDPA). In this paper, we investigate the proposed
MIMO techniques which have been agreed to be included
in 3GPP MIMO working item technical report (WI-TR).
The rest of the paper is organized as follows. In
Section II, we examine the characteristics of SU-MIMO
and MU-MIMO showing different system architectures.
Section III investigates the capacity and the BER
performance of MIMO which are the fundamental issues
for all types of multi-antenna technologies, with
introducing some basic MIMO methods in Section IV,
where OL and CL schemes are compared. In Section V,
advanced MIMO technologies are presented, especially on
the current 3GPP MIMO candidates. We describe the
hybrid scheduling scheme for MU-MIMO in Section VI,
where system capacity can be shown to improve according
to scheduling methodologies. We conclude in Section VII.
2 Telecommunications Review·Vol. 14 No. 3·2004. 6
V-BLAST
Lucent, [29]
S-PARC
Ericsson, [26]
DSTTD-SGRC
Mitsubisi, [24]
RC-MPD
Nortel, [25]
Hybrid Scheme
MU-MIMO
R. Heath, [13]
PU2RC
Samsung&SNU
[22]
PU2RC +
PARC
Samsung&SNU
[23]
PARC
Lucent, [30]
D-STTD
TI, [25]
SU-MIMO
MU-MIMO with
space-time scheduling
Figure 1. History of MIMO technologies in 3GPP W-CDMA
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 3
II. CATEGORIZATION
MIMO schemes can be categorized into SU-MIMO
schemes and MU-MIMO schemes, as depicted in Figure 1,
which will be explained in detail in Section IV. Figure 2
illustrates one example showing both SU-MIMO system
and MU-MIMO system. SU-MIMO is a point-to-point
communication and so a single transmitter and receiver are
assumed. In this case, the coordination between antennas
is only possible for link performance. On the contrary,
MU-MIMO assumes multiple users for system design.
MU-MIMO is considered two types of scenarios, i.e.
point-to-multi-point and multi-point-to-point channel
environments. In the case of a point-to-multi-point
communications, a broadcast channel is modeled. Multiple
access channels are assumed for multi-point-to-point
communications.
III. PRELIMINARY ANALYSIS
1. System Model
In Figure 3 a broadcast MIMO system in wireless
mobile channels is illustrated, in which a radio base station
(Node B) communicates with K UEs. Each UE may have
the SIC (successive interference cancellation) receiver
structure for high performance gain and Mr receive
antennas, while the Node B has Mt transmit antennas [18].
The received signal for the kth user in a general
MIMO system may be expressed as
Esyk(t)=ˆ ---------Hk(t)T(t)x(t)+nk(t) (1)Nt
where Hk(t) is the Mr×Mt MIMO channel matrix from
the Node B to the kth UE(user equipment), T(t) is the Mt×Mt beamforming matrix at the transmitter, x(t) is the Mt×1 transmitted symbol vector, and nk(t) is a Mr×1
additive white Gaussian noise (AWGN) vector with
distribution CN(0,N0/2IMr). Es is the transmit energy of
the signal, and t is the index of the timeslot. The
appropriate scheduling and the beamforming, denoted by
matrix T(t) in (1), can be performed in Node B based on
the channel state information fed back from UEs,
especially for frequency division multiplexing (FDD)
systems. We investigate the performance of system
capacity in MIMO involving both single-user and multi-
user channels throughout this paper. In the next
subsection we present the achievable MIMO channel
capacity for single-user environment, for preliminary
understanding of the MIMO systems.
SU-MIMO
MU-MIMO
Figure 2. System configuration of SU-MIMO and MU-MIMO
antennas approaching infinity divided by the number of
antennas.
3. BER Performance
In the previous subsection, we reviewed the capacity
of MIMO channel. We now provide an analysis of the
performance of main MIMO schemes such as MLD
(maximum likelihood detection) [19], singular value
decomposition MIMO (SVD-MIMO) [2], and V-BLAST.
In the case of MLD, the transmitted symbol x(t) in (1)
is detected based on the maximum likelihood criterion as
Esx^ (t)+arg min‖yk(t)- ˆ---------Hk(t)x(t)‖2 (4)x(t) Nt
In [19], the detection error probability of the MLD at tth
time slot is closely approximated as
SNRpe(t)≈Q (ˆ2 -----------------χ2
2nr(t)) (5)
Knt
2. Channel Capacity
The channel capacity, which can obtained by optimal
MIMO transceiver, in SU-MIMO system is given by
Mt Es pmCO, k(t)= Σ log2(1+---------------------λm(Hk(t))) (2)m=1 N0
where the power distribution factor pm is set to 1/Mtin the OL case and can be found using water-pouring
algorithm in the CL case, and λm(A) is the mth nonzero
eigenvalue of AAH.
Later, we will show the capacities given by different
MIMO structures. The ergodic capacity for OL case may
be approximately represented by [20]:
Cn,nCMt, Mt
≅C1,1+(Mt-1)·lim ---------------- (3)n→∞ n
where C1,1 is the average capacity of SISO (single-
input single-output) Rayleigh channel, and other part is the
capacity of both the number of transmit and receive
4 Telecommunications Review·Vol. 14 No. 3·2004. 6
User
Select
AMC
AMC
Feedback
from UEs
MMSE
or
MMSE-SIC
MMSE
or
MMSE-SIC
T
K
Node-B
UE1
UEK
Figure 3. Broadcast MIMO systems
reason for this is that in such schemes the union bound is
determined by the weakest substream. We may have
further performance enhancement by using an independent
link adaptation per layer [11], [32].
Figure 4. compares the BER performance of these
MIMO schemes in the case of 2x2 MIMO channel.
IV. PROPOSED SCHEMES for 3GPP
In this section, we investigate advanced MIMO
solutions, mainly focusing on MIMO candidates in 3GPP
standardizations. In 3GPP, major industrial organizations
propose their schemes toward the official use of MIMO in
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 5
where Q(·) is the Q-function, and K is 1 or 2 representing themodulation order of BPSK and QPSK, respectively, andχ2
2nr(t) is a chi-square distributed random variable with 2nr
degrees of freedom.
On the other hand, the upper bounds of BER
performance of both SVD-MIMO and V-BLAST (no
ordering and for high SNR region) can be equally
represented as
1 SNR χ22-(nr-nt+1)(t)pe(t)≈-------Q(ˆ2 ------------------------------------------------------------- ) (6)
nt Knt nt
and are much poorer than the union bound of MLD. The
100
10-1
10-2
10-3
10-4
10-50 5 10 15 20 25
SNR(dB)
Figure 4. MIMO BER performance: 2x2-MIMO case
BER
SVD-MMO or V-BLAST
MLD
User1
UserK
11
M
V
M
Feedback Control
Figure 5. Transmitter of PU2RC architecture
{g}
{[SINR1....SINRM]}
Packet Data
Unit
(PDU)
User/Rate
Selection
Coding
Intereaving
Mapping
Coding
Intereaving
Mapping
Unitary
Basis
Trans-
formation
future wireless communication systems. As mentioned in
the previous sections, most techniques are based on the
mixture of basic MIMO algorithms for performance
improvement. We observe the system architecture of each
candidate and performance analysis.
1. PU2RC
As in Figure 5, PU2RC (per user unitary rate control)
was proposed by Samsung and SNU [22], [23]. PU2RC
uses spatial multiplexing to transmit data streams,
simultaneously to multiple users. Thus, multiple streams
are selected for transmissions to multiple users. All other
MIMO candidates are based on the single-user
6 Telecommunications Review·Vol. 14 No. 3·2004. 6
communications, while PU2RC is focused on multi-user
environments. More specifically, the transmissions are
precoded using a unitary matrix based on the singular
value decompositions of the MIMO channel. The unitary
basic matrix V at the transmitter is given by
V=[v1(gk)v2(gk) ... vM(gk)] (7)
which is the combination of the selected unitary basis
vectors from all UEs, where the selected unitary basis
vectors are all only from one UE or partly from several
UEs while total number of the vectors is fixed as M.
Data X1
X2
X3
X4
Figure 6. Transmitter of DSTTD-SGRC architecture
1
2
3
4
Encoder
Modulator
Encoder
Modulator
STTD
Encoder
STTD
Encoder
MCS
Selection
From UE
Multi-code
spreading
Multi-code
spreading
Multi-code
spreading
Multi-code
spreading
S/P
(DMUX)
14
12
10
8
6
4
2
0-2 0 2 4 6 8 10 12 14 16 18
lor/loc(dB)
Figure 7. DSTTD-SGRC: Single-user throughput at speed=30kmph with 4-slot feedback delay
Throughput (Mbps)
DSTTD-SGRCPARC-64 level
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 7
Furthermore, such selections are made on the space-time
multi-user diversity theory for maximum capacity
achievement. Each UEs optimum unitary basis matrix can
be different from each other and also different in Node-B’
s. Likewise, PU2RC is different from PARC since it uses
transmit weight matrix to transform input data vector and
supports spatial division multiplexing to utilize code-reuse
per user concept. Moreover, PU2RC is also distinguished
from PSRC (per stream rate control) since it provides the
efficient method of transmit weight matrix to support
MIMO SDMA (spatial division multiple access) by
restricting transmit weight matrix as unitary one in case of
the closed loop MIMO multi-user diversity.
2. DSTTD with SGRC
The schema of DSTTD (double space time transmit
diversity) with SGRC (sub group rate control) is shown in
Figure 6. DSTTD with SGRC was proposed by
Mitsubishi in 3GPP [24], noting that DSTTD without
considering SGRC had been proposed by TI [xx]. On the
other hand, DSTTD was originally proposed by Texas
Instruments in 3GPP, which was compared with PARC for
system performance.
DSTTD has no feedback signaling, resulting in
capacity degradation. Thus, Mitsubishi proposes the
improved version of DSTTD which is equipped with
adaptive modulations and feedback signaling for capacity
enhancement. Figure 7 describes the system performance.
3. Multipath Diversity (RC-MPD)
MPD (multi-path diversity) was proposed by Nortel,
as shown in Figure 8 [26]. MPD also uses spatial
multiplexing with rate control on each stream, namely RC-
MPD (rate control MPD). The difference is that each
stream is transmitted from two antennas with the
spreading codes differentiated by a delay of one chip
interval. MPD also uses space-time block coding as in
DSTTD. The system performance of MPD is described for
comparison with PARC in Figure 9.
4. Selective PARC
S-PARC (Selective PARC) is an extension version of
PARC, which was proposed by Ericsson in 3GPP as
shown in Figure 10 [27]. As the number of receive
antennas increases, S-PARC adaptively selects several
antennas from which to transmit, i.e., selects the best
mode, and further selects the best subset of antennas for
the chosen mode, where mode/antenna selection achieves
the highest information rate.
Generally speaking, the selective approach transmits
from fewer than the full number of antennas when
operating at lower SNRs and/or when the number of
receive antennas is less than the number of transmit
antennas and/or when the channel is heavily dispersive. In
Data
Bits
Channel
coding/
Modulation
MCS
selection
UE
Feedback
Figure 8. Transmitter of RC-MPD architecture
Spreading
Spreading
STTD
encoder+Tc
Ant1
Ant2S1
1----- -----√2
1----- -----√2
-S2*
-S1*
S2
S/P
8 Telecommunications Review·Vol. 14 No. 3·2004. 6
this way, excessive self-interference is avoided, and
diversity is obtained through both the channel and multiple
receive antennas. In S-PARC, the modulation and code
rate are assigned to the selected set of transmit antennas by
mode switching.
The system performance of S-PARC is described in
Figure 11. For instance, a 2 dB gain in SINR can be
observed at 2 Mbps. Though the gain is moderate, it is
interesting to note that the RX complexity can be reduced
since only one TX antenna is activated in this region.
5. TxAA based schemes
The Nokia proposal (closed loop MIMO with 4Tx and
2Rx) shown in Figure 12 is an extension of the closed loop
TxD used in Rel99 using receiver diversity [28]. Also,
another proposal (double TxAA) has been contributed
from LGE [29].
6. PARC - Enhanced V-BLAST
In this section, OL-MIMO and CL-MIMO are
described as 2 general schemes (i.e., V-BLAST and
30
25
20
15
10
5
01 2 3 4 5 6 7 8 9 10
Histogram:urban macro (4x4) 3km/h perfect channel
Throughput(Mb/s)
Figure 9. RC-MPD: Throughput comparison with PARC
Percentage %
PARC(4x4)
RC-MPD(4x4)
Power allocation,
Code allocation,
Queuing and traffic
information
Rx Feedback
Information
Antenna
Processor
M Active Signals
Figure 10. Transmitter of S-PARC architecture
Antenna 1
Antenna 2
Antenna N
User
Packet Data Unit
(PDU)
AMCS Decision
Controller
Encoding
Demultiplexing
Filtering
data streams are de-multiplexed to each transmit antenna
for transmission, and at the receiver the OSIC (ordered
successive interference cancellation) and MMSE
(minimum mean squared error) are used.
PARC (per antenna rate control) is a CL-MIMO as an
PARC, respectively) [31], [32]. When there is no channel
information available at the transmitter, the equal transmit
power is assigned for each antenna for optimal
performance. In Figure 13 (a), the transmitter and receiver
of V-BLAST architecture are described, where multiple
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 9
25
20
15
10
5
0-20 -10 0 10 20 30 40
4x2,3GPPTU Channel, 30% Pilot/Voice Power
lor/loc(dB)
Figure 11. S-PARC: Single-user throughput at speed=30kmph for 4x2 system on TU channel
Average Rate (Mbps)
S-PARC
PARC
CR-BLAST
RxDiv
SISO
Coded
Modulated
data-stream
Spread/scramble
W1 Pilot-1
Pilot-2
Σ
Σ
Σ
Σ
Pilot-3
Ant-1
Ant-2
Ant-3
Ant-4
Pilot-4
Weight Generation
Feedbackinformation
Figure 12. Closed loop MIMO with 4Tx and 2Rx
W2
W3
W1 W2 W3 W4
W4
enhanced version of V-BLAST, illustrated in Figure 13 (b)
including adaptive modulation and coding block for each
antenna based on Figure 13(a) [11]. In PARC, adaptive
modulation and coding schemes are used and the
predetermined combination sets of modulation level and
coding rate are provided at the receiver and fed back to the
transmitter based on the channel conditions. The feedback
capacity is limited and the perfect information of channel
information requires heavy loading of feedback signaling,
and hence the reduced set of feedback signaling
information is generated for limited feedback channel
capacity.
In Figure 14, we show the comparison of system
capacity in V-BLAST and PARC with different reception
schemes.
•V-BLAST with MMSE-OSIC, with MMSE-SIC, with
MMSE only•PARC with MMSE-SIC, with ZF-SIC, with MMSE
only and ZF only
where MMSE and ZF abbreviate the filtering using the
minimum mean square error criterion and Zero-forcing
criterion, respectively, and SIC and O-SIC abbreviate the
decoding process using the successive interference
cancellation procedure and the ordered-SIC, respectively.
For simplicity, we assume the antenna configuration
considering 4 transmit and 4 receive antennas in this
10 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월
Coding Interleave Demux
ANT1
ANT2
w1
w10
w1
w10
(a) V-BLAST (e.g. 2 Tx antennas)
Equal transmission
rates on each antenna
Packet
Data Unit
(PDU)
Coding
Interleaving
Mapping
Scrambling
Code
Scrambling
Code
Antenna 1
AntennaT
Coding
Interleaving
Mapping
Spreading Code 1
Spreading Code 2
Spreading Code C
(b) PARC
Figure 13. Transmitter of Basis MIMO schemes
DEMUX
analysis.
We investigate the instant MIMO capacities using the
distribution of SNR for each transmit antenna based on the
tight lower approximation described in [21]. The capacity
of PARC with MMSE-SIC approaches the optimal
capacity of the open-loop MIMO channel. Moreover, as in
the [11], the capacity obtained by using ZF-SIC in PARC
also very close to the capacity of PARC with MMSE-SIC.
Thus, we describe the capacity in this case as follows:
n SNRCPARC(ZF-SIC)= Σ log2(1+-------------·χ2
2n(n)n=1 n
≈CPARC(MMSE-SIC) (8)
where χ22n(n) denoting the post-detection signal-to-noise
ratio (SNR) of nth transmit antenna is a random variable
with the 2n-order chi-square distribution. N is the number
of transmit antennas. The capacity of PARC with ZF only
noting much poorer than the capacities of the previous
cases is denoted as
n SNRCPARC(ZF)= Σ log2(1+-------------·χ2
2(n))n=1 n
≦CPARC(MMSE) (9)
in which any diversity gains are never obtainable for all
transmit antennas.
The capacities of V-BLAST cases are highly dependent
on the SIC decoding whether it is using the ordering
process or not as well as the presence of the SIC entity in
the receiver, which is inline with the results on [11]. If there
is no SIC entity and no ordering process, which is the case of
V-BLAST with ZF only, the achievable capacity can be
represented as
CBLAST(MMSE)≤CBLAST(ZF)
≥N log2(1+min χ22(n)) , (10)
n=1..N
since we have known that all streams should be
transmitted with the same modulation and coding level in
V-BLAST cases. With SIC, the capacity improves as like
as in case of PARC with ZF-SIC so that it is again
expressed as follows:
CBLAST(MMSE-SIC)≥CBLAST(ZF-SIC)
≈N log2(1+min χ22n(n)) . (11)
n=1..N
Furthermore, its capacity increase much more by using the
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 11
12
10
8
6
4
2
00 1 2 3 4 5 6 7 8 9 10
Capacity of MIMO (4x4) for PARC and V-BLAST
SNR (dB)
Figure 14. Achievable capacities of basic MIMO schemes
Capacity (bits/s/Hz)
PARC (MMSE-SIC)BLAST (MMSE-OSIC)
BLAST (MMSE-SIC)PARC (MMSE-only)BLAST (MMSE-only)
PARC (ZF-SIC)
PARC (ZF-SIC)[Theory] OL-MIMO
[Theory] PARC (ZF-SIC)
[Theory] PARC (ZF-SIC)
ordering processing for the SIC decoding, then the
capacity is bounded by
CBLAST(MMSE-OSIC)≥CBLAST(ZF-OSIC)
≈N log2(1+ min ( max χ22n(i, n))) .
n=1..N i=1..N-n+1
(12)
where χ22n(i, n) is the independent and identically
distributed chi-square random variable distributed
identical independently for nth transmit antenna.
7. TPRC for CD-SIC MIMO
Transmit power ratio control (TPRC) was proposed by
SNU & Samsung [30]. To cancel out the effect of time-
domain interference signal, the code-domain interference
canceller, e.g. the code-domain successive interference
canceller (CD-SIC), may be a good choice rather than the
time-domain one because of its good performance and
simplicity. However, it should be carefully study the
properties of a canceller, especially those of the successive
interference canceller like one in the receiver of MIMO
with SIC (successive interference cancellation) systems, to
make use of its best advantages. In practice, the successive
interference canceller may cause unbalance of the post-
detection SINRs among the outputs of the detection
stages, e.g., the output SINRs of the different code
channels.
To compromise such problems, the following features
are applied to MIMO systems
•First, the Code-Domain SIC (CD-SIC), which is named
CD-SIC MIMO, and•Second, the Code-Domain Tx Power Ratio Control
(CD-TPRC), to take full advantage of CD-SIC, which is
named TPRC for CD-SIC MIMO.
The block diagram in Figure 15 shows the basic
physical layer structure of the TPRC for CD-SIC MIMO
for the HSDPA system, which is considering proposed
both CD-SIC and CD-TPRC in MIMO systems.
8. PARC and PU2RC with Scheduling
In this subsection, multi-user MIMO scheme with
scheduling is proposed for MIMO broadcast channel
12 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월
Input
data
Serial-to-
parallel
converter
Power Allocation
d1,1C1(t)
ˆP1/M
ˆPK/M
ˆPK/M
ˆP1/M
dK,1CK(t)
d1,MC1(t)
d^ 1,1
d^ K,1
d^ 1, M
d^ K, M
#1 #1
CD-SIC
with
MIMO
detection
Parallel-
to-
serial
converter
Output
data
#M #N
dK,MCK(t)
Figure 15. Basic physical layer structure of the TPRC for CD-SIC MIMO in the HS-DSCH
dk,m : data symbol
ck(t) : spreading code
Pk: code power
Power
AllocatorFeedback
channel
(Power Ratio)
Power
Calculator
Transmitter Channel Receiver
depicted in Figure 3. In previous sections, all solutions
excluding PU2RC scheme proposed by Samsung are based
on a single-user environment and a point-to-point
communication is considered for link performance. Note
that PU2RC scheme is considering the advantages of muti-
user channels, being explained in the previous section.
Practically speaking, multi-user communications need to
be assumed in cellular networks. Thus, scheduling
methodology is also considered because all users cannot
be served at the same time due to the limited resources
(e.g., the number of antennas, transmit power, etc.).
We consider two types of scheduling methods in
MIMO systems. Both techniques are based on multi-user
diversity. One of them is called MLT (maximum link
technique), in which each user employs spatial
multiplexing, and all transmit antennas are allocated to the
selected user according to the downlink channel
conditions. The other method is called IST (independent
stream technique), where all users compete independently
for each transmit antenna, and hence a user can be
assigned zero, one, or more antennas based on the link
channel conditions.
To consider the capacity of each scheduling method,
we, hereafter, denotes the SNR of the channel from the
mth transmit antenna to the kth UE as γk,m. The MLT
scheduler chooses the user satisfying kM=arg maxk Ck(t)
and so kM,m=kM for all m. Hence, the capacity can be
written as
Mt-1
CM(t)=max Ck(t)=max Σ cf (γk,m(t)) (13)m=0
Mt-1where Ck(t)= Σ cf (γk,m(t)).
m=0
In (13), cf (t)=log(1+x). Next, the capacity of the IST for
multiuser diversity is examined, especially when linear
receivers are included, while the capacity obtained with
SIC nonlinear receivers are observed in the following
subsection. In this case, the criterion for the IST
maximizes the capacity of each transmit antenna so that
the capacity is given by
Mt-1 Mt-1CIL(t)= Σ max cf (γL,k,m(t))= Σ cf (max γL,k,m(t)) (14)
m=0 k m=0 k
where we have used the simple equality
max cf (γL,k,m(t))=(max γL,k,m(t))k
and the user is selected for each transmit antenna if kIL,m=arg maxk γk,m(t) for m=0, 1, ..., Mt-1.
Consider choosing an appropriate multiuser diversity
technique for SIC receivers between MLT and IST. For
the MIMO systems using the SIC receivers, which are
nonlinear since cancellation processes such as SIC are
nonlinear, there is an obvious multiuser diversity scheme
to select the UE to receive information at a given time. If
the number of UE is greater than one, one may allow the
Node B to allocate antennas to different users in an
independent manner and not all to one user. The Node B
must then transmit information at a small enough rate so
that not only the desired UE can decode but also all other
interfered UEs are able to cancel, and the rate is
Mt-1CIS(t)= max Σ cf (min{γS,ki ,(oi )m
(t)}) (15){k
i},{o
i} m=0 i
where γS,ki ,(oi )m(t) denotes the SNR of the channel
between (oi)m antenna in the Node B to the kth UE, {ki} i
=0..L-1 is the set of the index of users receiving data
while L denotes the total number of receiving users in a
time slot, and oi is the vector representing ordering for the
SIC processing in the ith UE. Therefore, the MLT is the
best strategy in terms of the system capacity when the SIC
receivers are involved, contrary to the case of the linear
receiver.
As in previous subsection, MLT and IST schemes are
well combined with SIC and linear receivers, respectively
[18]. In a SU-MIMO environment, the SIC receiver
outperforms the linear receiver. It is also observed that in
a multi-user MIMO communications IST with linear
receiver outperforms MLT with SIC receiver when the
number of users gets large. Thus, we propose the hybrid
scheduling scheme which switches between the above two
scheduling algorithms by measuring the user capacities of
both scheduling systems. In our hybrid scheme, the user
capacities using both scheduling methods are measured
and compared to select either one of both scheduling
methods which achieves better performance.
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 13
Therefore, the maximum rate achieved by the hybrid
scheduler is CHB(t)=max{CIL(t),CMS(t)} since it chooses
a technique providing larger capacity. The user selected by
our proposed scheduler is kHB,m=arg maxk γL,k,m(t) for
each antenna m if CIL(t)>CMS(t), otherwise kHB,m=arg
maxk CS,k(t) for any m. In brief, it is shown that the
capacity achieved by the hybrid scheduler is always better
than the other two schemes since the hybrid scheduler
selects the larger one between the outputs of the two
schedulers.
In the hybrid scheme, the required feedback
information is the received SNRs for both the MMSE SIC
receiver and MMSE linear receiver, respectively. The
Node B then decides one scheduling scheme according to
the total user capacity, obtained by its corresponding
receiver structure. Here, the feedback information is
doubled due to using the respective received SNRs
measured by two different scheduling schemes. One idea
to cope with increasing feedback is to allow the system to
decide the switching point by the number of scheduled
users. The capacity performance of hybrid scheme, which
has been investigated in [33] using the order statistics [34],
is observed in Figure 16.
In Figure 16, the capacities achieved by the different
types of feedback information (at SNR=10dB) are
presented. We note that two schemes with scalar feedback
described in Subsection 2 approach the capacity of the
optimal vector feedback for small number of transmit
antennas. Thus, the ergodic capacity with varying K (at
SNR=10dB) is illustrated, where we compare the
capacity achieved by the proposed hybrid scheduler, CHB,
with the capacities of MLT and IST, CMS with qSIC=1
and CIL with qBF=0, respectively. qSIC indicates the level
of SIC processing, 1 for perfect SIC and 0 for linear
reception (i.e. no SIC), and qBF is the level of the
beamforming processing, 1 for beamforming with perfect
knowledge of the channel information and 0 for no
beamforming. We observe substantial improvement in the
average throughput by allowing the Node B to change the
current scheduling method by measuring the capacities of
both rules. As expected, the proposed scheduler
apparently outperforms both the MLT and the IST, for any
number of users, K.
V. PERFORMANCECOMPARISON
In this section, we compare the performance of
PU2RC with the suggested feedback structure to that of
other schemes with similar protocol conditions, as in detail
described in below. We assume the power allocation
14 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월
16
15
14
13
12
11
10
9
8
7
100 101 102
Erogodic Capacities of Different MU-MIMO(4x4) Schemes
#Users (K)
Figure 16. HBT: Multiuser throughput comparing with MLT and IST schedulers
BPS/Hz
CIL: qBF=1
CIL: qBF=0
CHB:qBF=0, qSIC=1
CMS:qSIC=1
CMS:qSIC=0
Basis MatrixSelection
1 bit
SINR
Basis Vector Selection
2 bits
Selected SINR
5 bits
Conventional part
Table 1. One example of CQI format for PU2RC scheme
CQI for each UE
Additional part
policy as in open-loop MIMO concept such as equal
power transmission for the utilized transmit antennas.
In multi-user environments, a two-step approach may
be executed as follows. For example, as described in
Table 1, we consider the following scenario for
information feedback, assuming the system with 4 Tx
antennas for Node-B and 4 Rx antennas for each UE. One
bit is used to specify the unitary basis set, denoted as g,
while totally seven bits are allocated for SINR feedback
being used for AMC decision in the Node-B, where two
bits are used to denote one appropriate basis vector among
the elements of the specified basis set maximizing the
system capacity and another five bits are for the SINR
corresponding to the selected basis vector. It is
noteworthy that the five bits are fully compatible with the
SINR feedback signalling in the R5 HSDPA specification.
We have minimized feedback burden by transmission of
index values of unitary basis set and basis vector. Table 1
shows the configuration of the feedback channel format
(for uncoded data). Similar protocol conditions for other
schemes such as PARC with 4 SINR feedback signaling
for all 4 transmit antennas, S-PARC with 2 SNR feedback
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 15
16
14
12
10
8
6
4100 101 102
Capacities of the Linear MIMO(4Tx-4Rx) Schemes at SNR=10dB
# of Users
Figure 17. The capacity of PU2RC compared with the capacities of S-PARC-1 and PARC, all with linear receiver complexity
Capacity (BPS/Hz)
PU2RC (full 4 CQI)
PU2RC-1 (1 CQ)
PARC /(full 4 CQI)
SPARC-1 (1 CQ)
16
14
12
10
8
6
4100 101 102
Capacity of MIMO(4Tx-4Rx) on SNR=10dB
# Users
Figure 18. The capacity of PU2RC compared with the capacities of S-PARC-1 and PARC, where especially a nonlinear receiver is used for PARC
BPS/Hz)
PU2RC (full FB)
PU2RC-1
PARC /(full FB)
SPARC-1
signaling for only two selected transmit antennas,
assuming open-loop power allocations.
In Figure 17 and 18, the receivers of PARC and S-
PARC-1 are assumed to be non-linear and high complex,
while a linear receiver is simply used in PU2RC. In both
figures, it is shown that PU2RC with the proposed
feedback structure outperforms other two schemes by
significant throughput gains, even having much smaller
feedback burden as shown in Figure 18. Table 2 describes
the feedback bits for 4x4 MIMO used in Figure 19.
VI. CONCLUSIONS
In this paper, we presented the advanced MIMO
technologies agreed to be included in 3GPP HSDPA
MIMO TR. We described the two categorized MIMO
schemes such as PARC, MPD, DSTTD with SGRC,
TxAA based schemes for SU-MIMO, TPRC and PU2RC
for multi-user MIMO, respectively. General information
on MIMO capacity was also overviewed and preliminary
results were examined. For future work, the efficient
resource allocation scheme is required for SU-MIMO and
MU-MIMO communications with feedback channel.
Moreover, spatial channel model (SCM) needs to be
combined together with the above MIMO schemes for fair
comparisons in real system environments, where SCM is
an official channel model agreed in 3GPP and ITU.
ACKNOWLEDGEMENTThis paper has been supported in part by the Samsung
Advanced Institute of Technology (SAIT) and in part by
the Institute of New Media & Communications, Seoul
National University (SNU). The authors would like to
thank Lab-President Seung-Yong Park and Dr. KiHo Kim
both from i-Networking Lab at SAIT and Mr. Changsoon
Park from MCL at SNU
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16 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월
250
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150
100
50
01 2 3 4 5 6 7 8 9 10
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Figure 19. The required feedback signaling for PU2RC compared with that of S-PARC-1 and PARC, function of the number of UEs
RFB(bits)
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PU2RC (full FB)
PARC
PU2RC-1
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18 TELECOMMUNICATIONS REVIEW·제10권 6호·2000. 11~12월
Sungjin Kim
Sungjin (James) Kim was born in Korea in 1969. He
obtained his Bachelor and Master of Engineering degree in
Electronics and Communications Engineering from the
College of Engineering, Hanyang University, Korea in
1994 and in 2000, respectively. He is now pursuing his
Doctor of Philosophy in Electrical and Computer
Engineering from the College of Engineering, Seoul
National University. In February 1994 he joined the i-
networking Lab, Samsung Advanced Institute of
Technology, and he is now a senior member of technical
research staff. Since 1999, he has been the Editor-in-Chief
of 3GPP (WCDMA standard) Transmit Diversity TR. His
research interests include the areas of transmit diversity
(TxD), multiple-input and multiple-output (MIMO),
wireless scheduling and adaptive signal processing for
3G+/4G wireless communications.
Tel.: 82-31-280-9222
Fax.: 82-31-280-9569
Email: [email protected]
An Overview of MIMO Technologies for Enhanced 3GPP HSDPA 19
Hojin Kim
Hojin Kim was born in Korea in 1973. He obtained
his Bachelor of Science in Electrical and Computer
Engineering from Purdue University, Indiana in 1997. He
received his Master of Science from the Electrical and
Computer Engineering at the University of Florida, Florida
in 2000. In 2000, he was with LG electronics institute of
technology as a research engineer. Since 2001, he has
been a research engineer at Samsung advanced institute of
technology. His research interests include MIMO, OFDM,
Ad-hoc network, and 3GPP standardization.
E-mail: [email protected]
Tel:+82-31-280-9222
Fax:+82-31-280-9569
Kwang Bok Lee
Kwang Bok Lee received the B.A.Sc. and M.Eng.
degrees from the University of Toronto, Toronto, Ont.,
Canada, in 1982 and 1986, respectively, and the Ph.D.
degree from McMaster University, Canada in 1990. He
was with Motorola Canada from 1982 to 1985, and
Motorola USA from 1990 to 1996 as a Senior Staff
Engineer. At Motorola, he was involved in the research
and development of wireless communication systems. He
was with Bell-Northern Research, Canada, from 1989 to
1990. In March 1996, he joined the School of Electrical
Engineering, Seoul National University, Seoul, Korea.
Currently he is an Associate Professor in the School of
Electrical Engineering. He was a Vice Chair of the School
of Electrical Engineering from 2000 to 2002. He has been
serving as a Consultant to a number of wireless industries.
Since 2003, he has been a senior member of the IEEE. His
research interests include mobile communications,
communication technique covering physical layer and
upper layer. He holds ten U.S. patents and four Korean
patents, and has a number of patents pending.
Dr. Lee was an Editor of the IEEE JOURNAL ON
SELECTED AREAS IN COMMUNICATIONS, Wireless
Series in 2001, and has been an Editor of the IEEE
TRANSACTIONS ON WIRELESS COMMUNICATIONS
since 2002. And he is a co-chair of the ICC2005 Wireless
Communication Symposium. He received the Best Paper
Award from CDMA International Conference 2000 (CIC
2000), and the Best Teacher Award in 2003 from College of
engineering, Seoul National University.
Tel:+82-31-880-8415
Fax:+82-31-880-8215
Email: [email protected]
Juho Lee
Juho Lee received B.S., M.S., and Ph.D. degrees from
Korea Advanced Institute of Science and Technology
(KAIST), Daejon, Republic of Korea, in 1993, 1995, and
2000, respectively, all in electrical engineering. Currently, he
is a senior engineer of Samsung Electronics Co., Republic of
Korea, working on standardization of mobile
communications. His main interests include wireless comm.,
CDMA, multicarrier modulation (such as OFDM), MIMO,
and signal processing for wireless communications.
Email: [email protected]
Tel:+82-31-279-5115
Fax:+82-31-279-5130