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IEEE SYSTEMS JOURNAL
1
Investigation of Transmission Schemes
for Millimeter-Wave Massive
MU-MIMO Systems
Yu Han, Student Member, IEEE, Haochuan Zhang, Member, IEEE, Shi Jin, Member, IEEE,
Xiao Li, Member, IEEE, Rong Yu, Member, IEEE, and Yan Zhang, Senior Member, IEEE
Abstract—A massive multiuser multiple-input–multiple-output
(MU-MIMO) system working at millimeter-wave frequencies is a
new concept that has appeared in recent years. In such systems, the
sparsity of the beamspace channel matrix makes the channel rank
reduction and limited feedback realizable. Based on these preferable features, this paper introduces two kinds of beamspace transformation and exploits their corresponding beamspace characters.
Then, the spatial division multiple access (SDMA) and the interference suppression precoding are investigated separately. Considering the advantages and disadvantages of these two traditional
schemes, the authors focus on the design of the limited-feedbackbased multiuser transmission schemes for millimeter-wave massive
MIMO systems. An enhanced SDMA scheme is first proposed in
this paper, which utilizes the beamspace sparsity and overcomes
the challenges of channel information acquisition and interference
control from a general perspective. Based on these, we analyze the
interuser interference condition and further propose a joint transmission scheme, which incorporates a double-precoding-based
SDMA and an interference suppression precoding. The performances of the proposed schemes are also evaluated via simulation,
which show that high system sum-rate capacity could be achieved
with only low computational complexity and a small amount of
feedback.
Manuscript received December 20, 2014; revised April 25, 2015 and July 3,
2015; accepted August 18, 2015. The work of Y. Han, S. Jin, and X. Li
was supported by the National Natural Science Foundation of China (NSFC)
under Grant 61531011, Grant 61571112, Grant 61450110445, Grant 61325004,
and Grant 61222102; by the Natural Science Foundation of Jiangsu Province
under Grant BK2012021; by the International Science and Technology Cooperation Program of China under Grant 2014DFT10300; by the National Science
and Technology Major Project of China under Grant 2013ZX03001032-004;
by A Foundation for the Author of National Excellent Doctoral Dissertation of
China (FANEDD) under Grant 201446; and by the Excellent Young Teachers
Program of Southeast University under Grant 2242015R30006. The work of
H. Zhang was supported by the National Natural Science Foundation of China
under Grant 61501127. The work of R. Yu was supported in part by programs
of the NSFC under Grant 61422201 and 61370159. The work of Y. Zhang
was supported in part by the Research Council of Norway through the Project
240079/F20 and in part by the European Commission FP7 Project CROWN
under Grant PIRSES-GA-2013-627490.
Y. Han was with National Mobile Communications Research Laboratory,
Southeast University, Nanjing 210096, China. She is now with the Huawei
Shanghai Research Institute, Shanghai, China (e-mail: hanyu@seu.edu.cn).
H. Zhang and R. Yu are with Guangdong University of Technology,
Guangdong 510006, China (e-mail: zhcbupt@gmail.com; yurong@ieee.org).
S. Jin and X. Li are with the National Mobile Communications Research
Laboratory, Southeast University, Nanjing 210096, China (e-mail: jinshi@seu.
edu.cn; li_xiao@seu.edu.cn).
Y. Zhang is with Simula Research Laboratory, 1364 Fornebu, Norway,
and also with the Department of Informatics, University of Oslo, 0373 Oslo,
Norway (e-mail: yanzhang@ieee.org).
Digital Object Identifier 10.1109/JSYST.2015.2481089
Index Terms—Massive multiple-input–multiple-output (MIMO),
millimeter wave, multiuser transmission.
I. I NTRODUCTION
C
URRENTLY, most of the mobile communication systems
work at the frequencies between 300 MHz and 6 GHz,
which is generally considered to be the optimum band for
creating preferable broadcast conditions. However, with the
increase of user number and the diversification of data services, frequency band below 6 GHz tends to be overloaded.
Frequencies between 6 GHz and 300 GHz become promising
and are full of exploiting potential [1] and [2]. Communication
systems working at this band use millimeter waves as carriers,
and they are named as millimeter-wave networks. In addition to
further developing the frequency resources, exploiting spatial
degrees of freedom is another approach to meet the evergrowing demands of data rate and quality of service. By deploying multiple antennas at the transmitter and/or the receiver,
a multiple-input–multiple-output (MIMO) system provides an
effective way to exploit the spatial degrees of freedom, and
this has become an important technique in modern wireless networks [4]. Transmission methods for MIMO systems to achieve
spatial diversity, multiplexing gain, and capacity improvement
have been well investigated [5]–[7]. In multiuser MIMO (MUMIMO) systems, the base station (BS) further exploits the
spatial degrees of freedom and serves more than one user
on the same time–frequency resource element [8] and [9]. In
recent years, a new technique named massive MIMO has been
proposed and widely studied [10] and [11]. In massive MIMO
systems, the deployment of a large-scale antenna array results
in much narrower transmit beams, along with an increase in
beamforming gain and a decrease in the interuser interference
(IUI). In addition to these advantages, massive MIMO systems
also face some challenges. For instance, the size of the antenna
array becomes much larger than that of a common MIMO
system. Due to the wavelength-scaled distance between two
adjacent antenna elements, millimeter-wave networks enable
the deployment of large-scale antenna array within a limited
space.
Massive MU-MIMO systems working at millimeter-wave
frequencies have attracted more and more attention. To mitigate
the problem of high-overhead channel state information (CSI)
feedback, a joint spatial division and multiplexing multiuser
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2
transmission scheme for millimeter-wave channels was introduced in [13]. Based on the sparsity of the beamspace channel
matrix, a low-rank multiuser transmission scheme that performs
well in IUI suppression was proposed in [14]. In the design
of multiuser transmission schemes, IUI suppression is the key
point and requires primary consideration [15], [16]. Generally
speaking, there are two commonly used methods to suppress
IUI, i.e., spatial division multiple access (SDMA) and interference suppression precoding. However, both of them have
advantages and disadvantages, and it is nontrivial to find a
hybrid scheme that balances the two.
In this paper, we analyze the beamspace channel features and
investigate the multiuser transmission schemes for millimeterwave massive MIMO systems working in frequency division
duplex (FDD) mode. The BS is deployed with a large-scale
uniform planar antenna array (UPA) according to the recent
3GPP 3-D channel model recommendations. Multiple singleantenna users are served by the BS at the same time–frequency
resource element. The contributions of this paper are listed as
follows.
(1) This paper introduces two kinds of beamspace transformation, i.e., the broadcasting beamforming method and the
dedicated beamforming method, and investigates the sparsity of
the beamspace channels. When exploring the beamspace characters, we discover the phase characters among the elements of
the broadcasting beamspace channel vectors.
(2) This paper makes an enhancement to the well-known
SDMA scheme by exploring the beamspace sparsity to solve
the channel information acquisition and interference suppression problem. For each user, the BS selects several beams that
capture the channel’s main lobe, to approximate the original
high-dimensional channel, and transmits downlink dedicated
pilot signals directly on the reduced-rank channels. Then, each
user estimates and feeds back its low-dimensional beamspace
channel vector to the BS. Finally, the BS rebuilds the physical channels, creates the interference suppression precoding
matrix, and conducts data transmission. This enhanced SDMA
scheme overcomes the difficulty in downlink CSI acquisition
for massive MIMO FDD systems.
(3) This paper proposes a joint multiuser transmission
scheme, which incorporates a double-precoding SDMA and
a zero-forcing (ZF)-precoding interference suppression. This
joint scheme is based on and improves the previous work
published in [17]. According to whether the beam overlapping
exists among different users, the user set is divided into two
subsets, i.e., a non-interference subset and an interference
subset. The non-interference subset adopts a double-precodingbased SDMA scheme, and the interference subset adopts
an interference-suppression-precoding-based transmission
scheme. This incorporation utilizes the advantages of both
SDMA and interference suppression precoding and further
reduces the feedback amount in FDD systems.
(4) Regarding [17], the new joint scheme introduces the
beamspace phase compensation precoding, which is motivated
by the discovery of the phase characters among the elements
of the broadcasting beamspace channel vectors. The precoding
vector of each SDMA user is comprised of an inner multibeam
selecting matrix and an outer phase compensating vector. This
IEEE SYSTEMS JOURNAL
Fig. 1. Single-cell millimeter-wave massive MIMO system.
scheme also can be viewed as an extension of [17] to the more
general and more complicated multipath propagation. This is
nontrivial work since the analyzing method used in [17] was
only faced to the single-path case.
Simulation results demonstrate that the enhanced SDMA
scheme has excellent sum-rate performance, while the joint
scheme reduces the feedback amounts greatly and requires
relatively low-complexity computations.
Notation: In this paper, matrices and vectors are denoted by
uppercase and lowercase boldface letters, respectively. We use
I to denote the identity matrix. The superscripts (·)H , (·)T , and
(·)∗ represent the conjugate-transpose, transpose, and conjugate
operations, respectively. Symbol E{·} represents the expectation with respect to all random variables within the brackets.
A ⊗ B is the Kronecker product of matrices A and B. We
use | · | and · to denote taking absolute value and modulus
operations, respectively, and round(·) to represent rounding a
decimal to its nearest integer.
II. S YSTEM M ODEL
Consider a single-cell millimeter-wave massive MIMO system working in FDD transmission mode. The BS is located at
the cell center, which is deployed with UPA antenna array, and
communicating with K single-antenna users simultaneously, as
shown in Fig. 1.
A. Signal Model
In the downlink (i.e., from the BS to the users), the received
signal at user i can be expressed as
hi wk xk + ni
(1)
yi = hi wi xi +
k=i
where hi ∈ C1×Nt denotes the downlink channel vector of user
i, Nt denotes the number of the antenna array elements at
the BS, wi ∈ CNt ×1 is the precoding vector of user i, xi is
the transmit symbol for user i, and ni is zero-mean complex
Gaussian white noise with the variance of σ 2 .
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HAN et al.: INVESTIGATION OF TRANSMISSION SCHEMES FOR MILLIMETER-WAVE MASSIVE MU-MIMO SYSTEMS
Denoting y = [y1 , y2 , . . . , yK ]T , we have
y = HWx + n
(2)
T
where H = [hT1 , hT2 , . . . , hTK ] is the downlink channel matrix, W = [w1 , w2 , . . . , wK ] is the precoding matrix, x =
[x1 , x2 , . . . , xK ]T is the transmit signal vector satisfying
E{xxH } = IK , and the precoded signal s = Wx satisfies the
power constrain E{sH s} ≤ P .
In Fig. 1 we can easily find out that, in a multiuser transmission system, some users are distributed far away from
each other, while some users may be close to each other. For
instance, users 5 and 6 are located very close, and severe IUI
exists between them. In this case, there is a need for interference control; otherwise, the performance of the multiuser
transmission system will degrade significantly. Since precoding
is one of the key techniques to control the interference in
downlink transmission schemes, proper design of W becomes
very important.
B. Channel Model
Precoding design is based on the practical channel state of
the millimeter-wave massive MIMO system. Here, we adopt
the spatial multipath channel model and write the downlink
physical channel vector of user i as [18]
hi = βi,0 at (Ωi,0 ) +
Np
βi,l at (Ωi,l )
(3)
l=1
where βi,0 and βi,l are the path factors of the line-of-sight
(LOS) and non line-of-sight (NLOS) paths, respectively; Ωi,j =
(θi,j , ϕi,j ) represents the angles of departure (AoDs) of the jth
path; θi,j and ϕi,j are the downtilt and the azimuth, respectively.1 at (Ω) ∈ C1×Nt is the steering vector of UPA antenna
array with AoD Ω = (θ, ϕ), and it can be written as [12]
(v)
at (Θ)
(h)
at (Ξ)
at (Ω) = at (θ, ϕ) =
⊗
(v)−1 1
−j2π Nt
Θ
−j2πΘ
=
,...,e
1, e
(v)
Nt
(h)−1 1
−j2π Nt
Ξ
(h)
at (Ξ) = 1, e−j2πΞ , . . . , e
(h)
Nt
(v)
at (Θ)
(v)
(h)
(4)
(v)
where Θ = dt /λ sin θ, Ξ = dt /λ cos θ sin ϕ, Nt = Nt ×
(h)
(v)
(h)
Nt , Nt and Nt represent the number of UPA rows and
(v)
(h)
columns, dt and dt are the distances between two adjacent
antenna elements in a row and a column, and λ is the carrier
wavelength. As mentioned earlier, for millimeter-wave systems,
the primary existing broadcast path is LOS, and NLOS suffers
from significant path loss; therefore, |βi,0 | |βi,l | for l ≥ 1.
In the remainder of this paper, we neglect the NLOS paths and
adopt the single-path model used in [14]. The physical channel
model (3) becomes
hi ≈ βi,0 at (Ωi,0 ) = βi,0 at (θi,0 , ϕi,0 ).
1 We
The following discussion will be based on this single-path
expression. Since the NLOS paths still exist in practical
environment, we make detailed analysis about the multipath
propagation scenario in corresponding sections.
III. BASICS ON B EAMSPACE
This section introduces some basic information about
beamspace that will be needed in the following sections. In
general, beamspace transformation is a powerful mathematical
tool for massive MIMO, which enables us to look at the
LOS millimeter-wave channels in the so-called beamspace
perspective.
Considering the single antenna equipment of user i, the
beamspace transformation of its downlink channel vector can
be expressed as [19]
(6)
h̃i = hi U
where h̃i ∈ C1×Nt is referred to as beamspace channel vector,
and U ∈ CNt ×Nt is the beamforming matrix expressed as
U = bH (Ω1 ), bH (Ω2 ), . . . , bH (ΩNt ) .
(7)
The AoD value set {Ω1 , Ω2 , . . . , ΩNt } contains Nt different
spatial directions; therefore, b (Ω1 ), . . . , b (ΩNt ) comprise a
set of basic 3-D beams. According to (6) and (7), the beamspace
channel vector h̃i is another linear presentation of hi and
reflects the channel energy distribution on the basic beams. In
addition, different choices of U contribute to different transformation results. Next, we introduce two kinds of beamspace
transformations, namely, broadcasting beamforming and dedicated beamforming.
A. Broadcasting Beamforming
As the name suggests, broadcasting beamforming is a
beamspace transformation method applicable to all users’ spatial channel vectors under the same system deployment, regardless of each user’s AoD/angle of arrival (AoA). It chooses
Nt beams corresponding to Nt uniformly distributed spatial
directions to be the set of basic beams. The jth basic beam used
in the broadcasting beamforming matrix is
m
n
(v)
(h)
bDFT (Ωj ) = at
⊗ at
(8)
(v)
(h)
Nt
Nt
(h)
(v)
(h)
where j = mNt +n, m = 0, . . . , Nt −1, n = 0, . . . , Nt −
1. These discrete Fourier transforming (DFT)-based beams
are orthogonal to each other and covers Nt fixed directions
independent with any user channel, as shown in Fig. 2. If we
denote
(v)
1
Nt − 1
(v)
(v)
(v)
(v)
, . . . , at
VDFT = at (0), at
(v)
(v)
Nt
Nt
(5)
use block fading to model the channel, where the channel fading
coefficients keep constant during the transmission of a data block and changes
independently (randomly) in another block.
3
(h)
VDFT
=
(h)
(h)
at (0), at
1
(h)
Nt
(h)
, . . . , at
(h)
Nt
−1
(h)
Nt
(9)
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4
IEEE SYSTEMS JOURNAL
Fig. 2. Nt orthogonal beams can be displayed in UPA topology, according to the azimuth and the downtilt. Beams in the same row have the same downtilt, and
those in a column have the same azimuth.
and substitute (9) into (7), we get the expression of the broadcasting beamforming matrix
(v)
(h)
Ubroa = VDFT ⊗ VDFT .
(10)
Assuming h̃broa,i,j and h̃broa,i,j+1 are two adjacent elements
in the same row, wherein Ψj = Ψj+1 , Yj+1 − Yj = (nj+1 −
(h)
(h)
nj /Nt ) = 1/Nt , then we get that
h̃broa,i,j+1
Therefore, the beamspace channel of user i is written as
h̃broa,i,j
Single-path channel model (5) gives us freedom to exploit
potential mathematical regulars. The following proposition further points out the phase difference between adjacent elements
of h̃broa,i .
Proposition 1: The phase difference between two row(h)
(h)
adjacent elements of h̃broa,i is ((Nt − 1)/Nt )π or −(π/
(h)
Nt ), and the phase difference between two column-adjacent
(v)
(v)
(v)
elements is ((Nt − 1)/Nt )π or −(π/Nt ).
Proof: According to the definition of beamspace channel,
h̃broa,i can be expressed as
H
h̃broa,i = βi,0 · at (Ωi,0 ) · bH
DFT (Ω1 ), . . . , bDFT (ΩNt ) .
(12)
Denote the jth element of h̃broa,i as h̃broa,i,j , we can get that
(v)
h̃broa,i,j = βi,0 · at (Ωi,0 )bH
DFT (Ωj ) = βi,0 · at
(h)
· at , (13)
where
(v)
at
(h)
at
=
1
(v)
Nt
1
=
Ψj =
e
e
(h)
Nt
mj
(v)
Nt
(v)
jπ Nt
−1 Ψj
(h)
jπ Nt
− Θi,0 ,
·
(v)
sin(πNt Ψj )
sin(πΨj )
,
(h)
sin(πNt Yj )
,
sin(πYj )
nj
Yj = (h) − Ξi,0 .
Nt
−1 Yj
·
(14)
Substitute (14) into (13), then we get the analytical expression
h̃broa,i,j = βi,0 ·
=e
N
(h)
−1
t
(h)
t
N
(h)
·
sin(πYj ) sin(πNt Yj+1 )
(h)
. (16)
sin(πYj+1 ) sin(πNt Yj )
(11)
h̃broa,i = hi Ubroa .
jπ
(v) (h) 1
jπ Nt −1 Ψj + Nt −1 Yj
·e
Nt
(v)
(h)
sin πNt Ψj sin πNt Yj
·
. (15)
sin(πΨj ) sin(πYj )
In other words, for a beamspace channel vector of the broadcasting beamforming, the phase difference between two adja(h)
(h)
cent elements in the same row is ((Nt − 1)/Nt )π. If the
(h)
sin(·) term is negative, the phase difference becomes ((Nt −
(h)
(h)
1)/Nt )π − π = −(1/Nt )π. Accordingly, the phase difference between two adjacent elements in the same column is
(v)
(v)
(v)
((Nt − 1)/Nt )π or −(1/Nt )π.
B. Dedicated Beamforming
Different from broadcasting beamforming, the dedicated
beamforming method is based on a specific user channel and is
applicable to this channel uniquely. Derived from the statistical
CSI, dedicated beamforming performs better in precisely describing the channel direction. To be specific, the transmit correlation matrix of user i and its eigenvalue decomposition are
H
R i = E hH
i hi = Vi Λi Vi ,
(17)
where2 Λi = diag{λi,1 , λi,2 , . . . , λi,Nt }, λi,1 ≥ λi,2 ≥ · · · ≥
λi,Nt are the eigenvalues of Ri , Vi = [vi,1 , vi,2 , . . . , vi,Nt ],
vi,j denotes the eigenvector corresponding to λi,j . For
dedicated beamforming, the beamforming matrix of user i
is defined as Udedi,i = Vi [20], and the single path channel
model enables the simplification for calculating the eigenmatrix
Vi . Substituting the single path channel model into (17), the
transmission correlation matrix of millimeter-wave massive
UPA antenna array can be written as
2
Ri = E βi,0 · aH
(Ω
)
·
a
(Ω
)
.
(18)
i,0
t
i,0
t
2 It is worth noting that, although the instantaneous matrix hH h in (17)
i
i
has only rank one, averaging over a certain period of time (i.e., expectation
implemented via averaging) yields a higher rank matrix Ri with very high
probability.
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HAN et al.: INVESTIGATION OF TRANSMISSION SCHEMES FOR MILLIMETER-WAVE MASSIVE MU-MIMO SYSTEMS
5
|h̃ibroa,1 |2 ≥ |h̃ibroa,2 |2 ≥ · · · ≥ |h̃ibroa,Nb |2 . In this case, we
approximate the beamspace channel as
(a)
h̃broa,i = h̃broa,i · Bbroa,i
(23)
where Bbroa,i ∈ RNt ×Nb is the beam selection matrix written as
(ibroa,Nb )
(i
) (ibroa,2 )
,
e
,
.
.
.
,
e
Bbroa,i = eNbroa,1
Nt
Nt
t
⎤T
⎡
⎥
⎢
(j)
eNt = ⎣0, . . . , 0, 1, 0, . . . , 0⎦ .
j−1
Fig. 3. Illustrations of the sparsity of |h̃broa,i |2 and |h̃dedi,i |2 , when
(v)
Nt
(h)
Nt
×
= 8 × 64. The graduated color from white to black represents
the increasing trend of channel energy distribution.
(v)
(h)
Since at (Ωi,0 ) = at (Θi,0 ) ⊗ at (Ξi,0 ), Ri in (18) can be
(v)
(h)
further written as Ri = Ri ⊗ Ri where
(v) 2 (v)H
(v)
(v)
Ri = E βi,0 · at
(Θi,0 )at (Θi,0 ) ,
(h)
Ri
(h) 2 (h)H
(h)
= E βi,0 · at
(Θi,0 )at (Θi,0 )
(19)
are the vertical and horizontal transmit correlation submatrices
with eigenvalue decomposition
(v)
Ri
(v)
(v)
(v)H
= Vi Λi Vi
,
(h)
Ri
(h)
(h)
(h)H
= Vi Λi Vi
(v)
. (20)
(v)
(h)
⊗ VDFT .
(21)
In this case, the beamspace channel of user i is expressed as
h̃dedi,i = hi Udedi,i .
Nt −j
Similarly, for the dedicated beamforming case, the indices of Nb largest elements of |h̃dedi,i |2 are indexdedi,i =
{idedi,1 , idedi,2 , . . . , idedi,Nb }, and the approximate beamspace
channel is
(a)
h̃dedi,i = h̃dedi,i · Bdedi,i
where the beam selection matrix is expressed as
(idedi,Nb )
(i
) (idedi,2 )
Bdedi,i = eNdedi,1
,
e
,
.
.
.
,
e
.
Nt
Nt
t
(25)
(26)
In Fig. 3, we can also observe that Nb Nt ; therefore, with
(a)
(a)
small-scaled h̃broa,i or h̃dedi,i , the challenges of computation
complexity and CSI feedback in massive MIMO FDD systems
disappear.
(h)
Therefore, we can get that Vi = Vi ⊗ Vi . It’s worth
noting that the Kronecker product based docomposition of Ri
and that of Vi hold in single path propagation environments,
while in multipath scenario they hold under some special
conditions [21]. In practical situations, the BS is horizontally
deployed with large numbers of antennas to enhance the
(h)
(h)
(h)
spatial resolution, and when Nt → ∞, Vi ≈ VDFT .
Therefore, the dedicated beamforming matrix can be
rewritten as
Udedi,i = Vi
(24)
(22)
C. Approximate Beamspace Channel
According to the aforementioned properties of the
millimeter-wave massive MIMO system, the downlink channel
is usually narrow and strongly directional; therefore, the
channel’s main lobe captures only a small number of basic
beams, which contributes to the sparsity of the beamspace
channel [14]. Fig. 3 gives an example of |h̃broa,i |2 and |h̃dedi,i |2
for the 8 × 64 UPA. Obviously, the beamspace channel holds
sparsity, and the channel energy is concentrated on a few
beams. Therefore, we can approximate the original highdimensional channel using these beams to reduce the channel
dimension. Take the broadcasting beamforming for instance.
Assume that indexbroa,i = {ibroa,1 , ibroa,2 , . . . , ibroa,Nb } contains the indices of Nb largest elements of |h̃broa,i |2 , and
IV. SDMA S CHEME AND I NTERFERENCE
S UPPRESSION P RECODING
Before moving forward to our proposal in Section V, we still
need to introduce two components that are indispensable. These
are SDMA and interference suppression precoding.
A. SDMA Scheme
As we know, the position of the user relative to the BS
reflects the AoD of the LOS path, i.e., the channel direction.
When the users are scattered far away from each other, their
channel directions are significantly distinct from each other and
are regarded to be spatial orthogonal to each other. Even if their
transmit signals share the same time–frequency resource element, their transmission processes are almost independent from
each other. This is the general idea of the SDMA scheme here.
Using the DFT beams introduced in Section III, the whole
3-D space is segmented by Nt dominant directions. Any channel direction can be approximately represented using several
dominant directions. If these user channels capture completely
different dominant directions, the BS can schedule them simultaneously and the interference will be small enough, as shown
in Fig. 4. For user i, having known its selected beam indices
indexbroa,i = {ibroa,1 , ibroa,2 , . . . , ibroa,Nb }, the BS directly
transmits signals on its approximate broadcasting beamspace
channel. The received symbol at user i is
(a)
yi = h̃broa,i xi +
h̃broa,i · Bbroa,k · xk + ni
k=i
= hi · Ubroa · Bbroa,i · xi + ni
(27)
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6
IEEE SYSTEMS JOURNAL
Fig. 4. General idea of SDMA scheme for millimeter-wave massive MIMO
systems. Assume that each user selects two DFT beams included in a dotted
line. Overlap may exist between the beams selected by different users.
where xi , xk ∈ Nb ×1 are the transmit signal vectors for user
i and k, and ni contains the slight IUI and the noise. It is
easy to see that Ubroa · Bbroa,i is only a precoding matrix
with the function of weakening sidelobe energy; hence, SDMA
precoding is equivalent to beam selection precoding.
In a crowded system, one dominant direction may be selected
by a few nearer users, which results in severe interference.
Therefore, the SDMA scheme requires user scheduling operation, i.e., only one of these mutually interfering users will be
scheduled by the BS according to a certain rule. Commonly
used scheduling criteria include the maximum sum rate, the
proportional fair, the round robin, etc. Take the maximum sumrate scheduling as an example. The BS makes user selection
round by round. During each round, the BS picks out only one
user who enhances the current multiuser system sum rate and
adds it into the scheduled user set.
B. Interference Suppression Precoding
Interference suppression precoding is a technique widely
adopted in MU-MIMO systems. ZF precoding among the most
popular precoding schemes decomposes the multiuser channel
into several orthogonal single-user subchannels and conduct
data transmission on these subchannels independently.
Denote the precoding matrix on (2) for downlink ZF MUMIMO systems as WZF . It is defined as
WZF = FP
†
F = HH (HHH )
#
# P = diag
P1 , . . . , PK
(28)
where (·)† represents the pseudoinverse [16]. Obviously HF =
†
HHH (HHH ) = I, and we have
$
1, i = j
hi fj =
0, i = j
for F = [f1 , f2 , . . . , fK ]. In other words, the precoding vector of
user i strengthens the channel energy of its own and suppresses
that of the others. P ∈ CK×K is used for transmit power
allocation, and one feasible example is the uniform allocation
scheme
Pi =
P
K · fi 2
(29)
Fig. 5. Multiuser transmission process based on the proposed limited feedback scheme.
where the total transmit power P is uniformly allocated to
all users.
ZF precoding is a linear precoding method with decent
throughput performance; however, it requires full CSI at the
BS and involves matrix multiplication and inversion. In massive
MIMO FDD systems, it is difficult for the BS to get the highdimensional channel matrix, and the computation complexity is
extremely high. This makes it difficult for ZF precoding to be
applied in massive MIMO FDD systems.
V. D ESIGN OF L IMITED -F EEDBACK -BASED
T RANSMISSION S CHEMES
In massive MIMO systems, due to the high dimension of
the channel matrix, difficulties exist in downlink orthogonal
pilot sequences designing. In addition, the commonly applied
pilot-reuse method brings in significant pilot contamination and
greatly impacts the system performance. Fortunately, as we
have discussed, the channel vectors for massive MIMO systems
working at millimeter-wave frequencies hold sparsity and can
be approximated by low-dimensional beamspace channels. This
makes the design of pilot sequences possible and further enables decreasing feedback amount in FDD transmission mode.
A. Enhanced SDMA Scheme
We first propose an enhanced SDMA scheme for millimeterwave massive MU-MIMO transmission systems. Both broadcasting and dedicated beamforming methods are applicable
here. Considering the existing interference among different
users, we adopt the ZF-precoding-based transmission scheme
and suppress IUI from a general perspective. The enhanced
SDMA scheme operates according to the following steps, as
shown in Fig. 5.
Step 1: Channel Sounding and Beam Selection: Users transmit uplink channel sounding signals, and then, the BS gets perfect uplink CSI through channel sounding. It has been proved
that the statistical CSI is independent of the carrier wavelength,
and reciprocity holds between the uplink and downlink statistical CSI in millimeter-wave massive MIMO FDD systems [13].
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HAN et al.: INVESTIGATION OF TRANSMISSION SCHEMES FOR MILLIMETER-WAVE MASSIVE MU-MIMO SYSTEMS
7
Considering the long-term feature of the AoD, we can apply
the uplink beam selection result to the downlink transmission
process. Assume that the uplink channel vector of user i is
gi ∈ CNt ×1 . Transform it into beamspace
g̃ i = UH
i gi
(30)
where Ui is the previously introduced downlink beamforming
matrix for user i. After calculating |g̃ i |2 , we sort its elements in
descending order, pick out the biggest Nb values, and get their
corresponding indices. Now, we know the selected beams and
the downlink beam selection matrix Bi .
Step 2: Pilot Transmission and Channel Estimation: For
each user, the BS transmits the downlink dedicated pilot sequence on the beams that it selects in step 1, i.e.,
(a)
yp,i = hi · Ui · Bi · pi + ni = h̃i
· pi + ni
(31)
where pi ∈ CNb ×1 is the pilot signal that the BS sends to
user i; pi and pj are orthogonal for i = j. We can find that
hi · Ui · Bi is only the approximate beamspace channel vector.
Having received its dedicated pilot sequence, user i estimates
its approximate beamspace channel every fading block by
ˆ (a) = y · p† .
h̃
p,i
i
i
(32)
Step 3: Feedback: Now, each user gets its reduced-rank
channel vector, and this vector contains the majority of the
complete downlink CSI; therefore, it only needs to feedback
its estimated approximate beamspace channel vector to the
BS. The total feedback amount in this step is K · Nb complex
numbers.
Step 4: Precoding and Data Transmission: According to the
received feedback values, the BS rebuilds the original highdimensional physical channel vectors. For user i, we have
(a)
ˆ
ĥi = h̃
i
· Bi T · UH
i .
(33)
Having known the rebuilt physical channel vectors of all users,
the BS generates the matrix
T T
T T
(34)
Ĥ = ĥ1 , ĥ2 , . . . , ĥK .
As mentioned earlier, IUI is suppressed from a general perspective. Substituting (34) into the expression of ZF precoding
matrix (28) and adopting the uniform power allocation scheme,
the precoding matrix is given as
H
H †
WZF = Ĥ (Ĥ Ĥ ) · P,
Fig. 6. User set division. According to whether overlap exists among the
beams selected by different users, the user set is divided into an noninterference subset and an interference subset.
(35)
where Pi is calculated by (29). Finally, the BS implements
downlink precoding and transmits the precoded signals to
all users.
This enhanced SDMA scheme fully utilizes the preferable
features of millimeter-wave massive MIMO systems and contributes to a multiuser transmission strategy with good sum-rate
performance and low computation complexity. Moreover, the
scheme is also applicable in multipath propagation scenarios,
provided that the channel shows sparsity and can be reduced.
However, each user must feedback its estimated low-rank channel, which is a complex vector and costs a wide frequency band
in feedback link. Furthermore, the general ZF scheme neglects
the specific IUI condition among different user pairs. All in all,
the enhanced SDMA is a simple but still expensive scheme that
achieves excellent performance, although it is less expensive
than the conventional method exploiting full CSI.
B. Joint Transmission Scheme
The proposed enhanced SDMA scheme provides a complete
transmission process for massive MIMO systems working at
millimeter-wave frequencies under FDD mode. However, there
is still some space for interference control, computation reduction, and feedback design. The scheme in [17] combines the
idea of SDMA and interference suppression precoding together
and reduces the feedback amount greatly. Considering the
preferable beamspace characters of millimeter-wave massive
MIMO systems, we improve the previous work and propose a
new joint transmission scheme.
1) User Set Division: In multiuser transmission systems, the
IUI condition varies greatly among different user pairs. As for
the case in Fig. 1, there is only slight IUI between users 1, 2, 3
and 4, while severe IUI exists between users 5 and 6. Therefore,
we can classify users 1–4 and users 5–6 into two groups, i.e.,
one contains users 1–4, and the other contains 5 and 6.
Based on this concept, the BS divides the users into two
subsets, i.e., a non-interference subset U0 and an interference
subset UI . Different from the enhanced SDMA scheme, only
the broadcasting beamforming is applicable here because of its
generality. Assume that the index set of the selected beams is
{indexbroa,i }i=1,...,K . If the beams selected for user i are not
chosen for all the other users, then user i will be included into
U0 ; otherwise, it will be included into UI . Assuming that the
user set is U = {1, 2, . . . , K} and the non-interference subset
is U0 = {u0,1 , u0,2 , . . . , u0,K0 }, then user u0,k satisfies
indexbroa,u0,k ∩ indexbroa,i = ∅
i = 1, . . . , K and i = u0,k , k = 1, . . . , K0 .
(36)
The interference subset UI is the complement of U0 , i.e., UI =
U − U0 , as shown in Fig. 6. Considering the different IUI
conditions, we apply different transmission schemes to the two
subsets. U0 employs a double-precoding-based SDMA scheme,
and UI employs an interference-suppression-precoding-based
transmission scheme. The detailed description is given as
follows.
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IEEE SYSTEMS JOURNAL
(l)
For codeword fu0,k ,2 , the value set of φl,j+1 is
{−Xj · π, c · π − Xj · π},
Xj =
mj
(v)
Nt
$
1, Xj > 0
+ (h) , c =
−1, Xj < 0.
Nt
nj
(41)
Since the two elements in the φl,j+1 value set have opposite
signs, the positive one and the negative one are marked with 1
and 0, respectively.
Having estimated the low-dimensional beamspace channel
ˆ (a)
approximation h̃broa,u0,k in step 2, we turn it into
Fig. 7. Spatial relations among the selected beams when Nb = 4. Central
red circle represents the strongest beam, and the surrounding orange circles
represent the potential substrong beams.
ˆ (a)
(eq)
h̃
broa,u0,k .
ˆ
h̃broa,u0,k =
= 1, ejϑ1 , . . . , ejϑNb −1
(a)
ˆ
h̃broa,u0,k ,1
(42)
2) Double-Precoding-Based SDMA Scheme: Considering
the non-overlapping feature among the beams selected by different SDMA users, the BS transmits signals directly along
their corresponding approximate broadcasting beamspace
channels. As mentioned earlier, double precoding is used
in the SDMA scheme. The preliminary precoding vector is
expressed as
ˆ (a)
ˆ (a)
where h̃broa,u0,k ,1 is the first element of h̃broa,u0,k . Since the
phase difference ϑj ∈ (−π, π] is predictable, here, we do the
following transformation:
$
>0→1
(43)
ϑj
< 0 → 0.
(37)
That is, if ϑj is positive, we sign it by 1, else by 0. Therefore, (ϑ1 ϑ2 , . . . , ϑNb −1 ) is represented by an Nb − 1-bit 0/1
sequence for simplicity. Then, we convert this binary 0/1
sequence to a decimal number, i.e., 000, 001, 010, 111 are
converted to 0, 1, 2, 7, respectively, and this decimal number is
fed back to the BS as the precoding matrix indicator (PMI). In
step 4, the BS converts this 1-bit PMI inversely to the original
Nb − 1-bit 0/1 sequence, reverts the corresponding φj+1 , and
finally settles the complete fu0,k ,2 .
3) Interference-Suppression-Precoding-Based
Scheme:
Given UI , we notice that, under proper user grouping, users
in the same group may not interfere with each other, which
means that we still have room to suppress the interference.
Following this thread, a new algorithm is proposed, with
the maximum interference user group. If a user group
UM = {uM,1 , uM,2 , . . . , uM,KM } satisfies the criteria that
any pair of users in UM , i.e., uM,i , uM,j ∈ UM , hold the
relationship (uM,i , uM,j ) or uM,i , uM,j , this group is called
a maximum interference user group. Here, (uM,i , uM,j )
represents direct correlation, which means that overlap exists
between indexuM,i and indexuM,j . uM,i , uM,j represents
indirect correlation, and it holds when both (uM,i , uM,k ) and
(uM,k , uM,j ) exist. The indirect correlation can be multistep,
according to the concept of maximum interference. Therefore,
we can further divide UI into several maximum interference
(s)
user groups {UM }s=1,...,S . Since there is no interference
among different groups, we only need to suppress the inner
group interference with the following steps.
Having received the estimated approximate beamspace chan(s)
(s)
nel vectors that user uM,i ∈ UM feeds back, the BS first
rebuilds its original physical channel
fSDMA = F1 f2
where F1 ∈ CNt ×Nb is the inner precoding matrix for rough
positioning, and f2 ∈ CNb ×1 is the outer precoding vector for
multibeam integration and beamspace phase compensation.
Having known the selected beam indices of user u0,k , we define
the inner precoding as
Fu0,k ,1 = Ubroa · Bbroa,u0,k
(38)
which is the beam selection precoding matrix introduced
(a)
previously. The phase character of h̃broa,u0,k referred to as
Proposition 1 gives us a new idea of beamspace phase compensation, i.e., that we can design a codebook specialized for
fu0,k ,2 . The lth codeword in the outer precoding codebook is
H
(l)
fu0,k ,2 = 1, ejφl,1 , ejφl,2 , . . . , ejφl,Nb −1
(39)
where φl,j+1 is the phase difference between the jth and the
(a)
first element of h̃broa,u0,k . Since user u0,k does not know its
selected beam index indexbroa,u0,k = {u0,k,1 , u0,k,2 , . . . ,
u0,k,Nb }, this codebook is only created at the BS. When
Nb = 4, all the possible spatial relations among these selected
beams are presented in Fig. 7, where the central red circle
represents the strongest beam, and the orange circles represent
the potential position of the remaining Nb − 1 substrong
beams. Assume that
u0,k,j − u0,k,1
mj = round
(h)
Nt
(h)
nj = u0,k,j − u0,k,1 − mj Nt
j = 2, . . . , Nb , −2 ≤ mj ≤ 2, −2 ≤ nj ≤ 2.
(40)
ˆ (a)
ĥu(s) = h̃broa,u(s) · BTbroa,u(s) UH
broa .
M,i
M,i
M,i
(44)
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HAN et al.: INVESTIGATION OF TRANSMISSION SCHEMES FOR MILLIMETER-WAVE MASSIVE MU-MIMO SYSTEMS
9
Then, stack all the rebuilt physical channel vectors of group
(s)
(s)
(s)
(s)
UM = {uM,1 , uM,2 , . . . , uM,Ks } into a matrix, and get that
T
T
T
T
(s)
.
(45)
Ĥ M = ĥu(s) , ĥu(s) , . . . , ĥu(s)
M,1
M,2
M,Ks
We keep adopting ZF precoding as the means of interference
suppression. As mentioned earlier, the precoding matrix is
calculated as
(s) (s)H †
(s)H
(s)
Ĥ M Ĥ M
(46)
FZF,M = Ĥ M
(s)
where the ith column of FZF,M represents the preliminary
(s)
precoding vector of user uM,i .
4) Power Allocation and Transmission Process: In the joint
transmission scheme, we still adopt the uniform allocation
method, and the total transmit power P is uniformly allocated
to all the users, no matter which subset it belongs to. The final
precoding matrix is calculated as
W = [f1 , f2 , . . . , fK ]P,
where fi is the preliminary precoding vector of user i which is
calculated in Subsection B Part 2 and 3.
Generally speaking, the nonuniform allocation methods are
also applicable to our joint transmission scheme. In practical
situations, we can design proper power allocation methods,
according to different performance requirements.
Based on the transmission process described in the previous
subsection, our proposed joint double-precoding-based SDMA
and interference suppression precoding multiuser transmission
scheme operates as follows.
Step 1) Channel sounding, beam selection, and user sorting:
The users transmit uplink channel sounding signals
to the BS, and the BS uses the sounding results to
find out the strongest beams for each user. According
to the indices of the selected beams, the BS classifies
each user into either U0 or UI .
Step 2) Pilot transmission, channel estimation and feedback
value calculation: For each user, the BS transmits
the downlink dedicated pilot sequence on the beams
that it selects. Having received the pilot sequence,
each user estimates its approximate broadcasting
ˆ (a)
beamspace channel h̃broa,i . Since the SDMA scheme
is based on double precoding, users in U0 should
calculate the outer PMI.
Step 3) Feedback: The users in U0 feedback the PMI, while
users in UI feedback their estimated approximate
broadcasting beamspace channel vectors.
Step 4) Precoding and data transmission: According to the
received feedback values, the BS calculates the precoding vectors and finally transmits the precoded
signals to the two subsets, separately.
5) Extensibility to Multipath Propagation Scenarios: In this
paper, we limited our focus on millimeter-wave frequency
communication, in which a typical coverage radius was shown,
in the literature, to be less than 200 m. In this small coverage
area, the LOS component was proved to be more dominant
Fig. 8. In multipath propagation scenarios, the existence of NLOS paths
brings in a bias to the phase difference value derived in the single-path
propagation scenario.
than the NLOS components. However, according to the field
measurement results presented in [1]–[3], [13], [22], and [23],
the NLOS paths still exist and capture a small part of the
channel energy. Therefore, here, we will discuss whether the
joint scheme is applicable in multipath propagation scenarios.
Derived from the single-path channel model, the beamspace
phasic relations referred to as Theorem 1 no longer hold, which
further challenges the outer precoding and its PMI calculation
in the double-precoding-based SDMA scheme. Considering the
NLOS paths, the jth element of h̃uni,i is written as
h̃broa,i,j = βi,0 · at (Ωi,0 ) · bH
DFT (Ωj )
+
Np
βi,l · at (Ωi,l ) · bH
DFT (Ωj ). (47)
l=1
Then, it becomes difficult to derive the expression of the phase
difference between h̃broa,i,j and h̃broa,i,j+1 . We use Fig. 8 to
illustrate the beamspace phase difference under this multipath
propagation condition. The black solid lines represent three
row-/column-adjacent DFT beams. ΔΩ is only the value of the
phase difference derived from the single-path channel model.
However, in multipath propagation scenarios, the existence of
NLOS paths will bring in a bias to ΔΩ. For the case of poor
NLOS scenarios, the bias is slight enough. The practical phase
difference ΔΩ1 is the angle between the pink dotted line and
the bottom black line, and we can still use ΔΩ to approximate
ΔΩ1 . While in strong NLOS scenarios, the practical phase
difference is hard to predict. If the real value is ΔΩ2 , it will
not be reasonable to approximate ΔΩ2 by ΔΩ.
Fortunately, it has been proved that, in millimeter-wave
networks, the NLOS paths result in 15–40 dB greater path
loss than free-space LOS paths [3], and the expected number of broadcast paths is not more than 4 [23]. We have
investigated the phasic feature by simulation. The numerical
results show that, in poor NLOS condition, the phase difference
between two row-adjacent elements of h̃broa,i floats almost
(h)
(h)
within (−(π/2Nt ), (π/2Nt )) relative to the derived values
(h)
(h)
(h)
{((Nt − 1)/Nt )π, −(π/Nt )} in Proposition 1, and the
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IEEE SYSTEMS JOURNAL
phase difference between two column-adjacent elements floats
(v)
(v)
(v)
almost within (−(π/2Nt ), (π/2Nt )) relative to {((Nt −
(v)
(v)
1)/Nt )π, −(π/Nt )}, similar to what the pink dotted line
shows in Fig. 8. In other words, the beamspace phasic relations
hold approximately in millimeter-wave networks. If we keep
adopting the double-precoding-based SDMA scheme and use
the single-path values in Proposition 1, the outer precoding still
works well, except for slight inaccuracy in phasic compensation. However, for rich scattering environment and full-rank
channel models, the outer precoding of the SDMA scheme will
not be usable.
TABLE I
S IMULATION PARAMETERS
VI. P ERFORMANCE AND N UMERICAL R ESULTS
To evaluate the performance of the proposed enhanced
SDMA scheme and the joint transmission scheme for
millimeter-wave massive MIMO systems, we make computer
simulations, and the numerical results are provided here. The
two proposed transmission schemes are represented as scheme 1
and scheme 2 for short, and the broadcasting-beamforming- and
the dedicated-beamforming-based enhanced SDMA schemes
are referred to as DFT scheme1 and dedicated scheme1, respectively. We focus on the downlink sum rate expressed as3
Rsum =
K
log2 (1 + SINRi )
(48)
i=1
where the signal-to-interference-and-noise ratio (SINR) for
user i is calculated as
SINRi =
|hi wi |2
K
%
.
(49)
|hi wk |2 + σ 2
k=1,k=i
We also investigate the performance of three additional
multiuser transmission schemes for comparison, which are DFT
unscheduled, dedicated unscheduled, and DFT SDMA+ZF. In
DFT unscheduled and dedicated unscheduled schemes, the BS
directly transmits signals to each user on the DFT or dedicated
beams that it selects without employing user scheduling operations. The DFT SDMA+ZF scheme is the previous joint scheme
proposed in [17], where the inner beam selection precoding
is solely implemented for SDMA users and the beamspace
phase compensation is reduced. We regard the performance of
these three schemes as the comparison baselines. Consider a
500-m-radius cell in the millimeter-wave massive MIMO system. The users are uniformly distributed in the cell. The
simulation parameters are listed in Table I.
Fig. 9 compares the system sum rate of these schemes when
SNR = 30 dB and Nb = 2/4. The performance of the two
unscheduled schemes are poor, particularly for the dedicated
unscheduled scheme, where each user adopts a specific set
of basic beams. This is because, without user scheduling or
interference control, beams selected by different users are
highly correlated. After dividing the user set into two subsets and applying interference suppression to the interference
3 Equation (48) is an upper bound of the system sum rate. As an upper bound,
it is reasonable to assume that user i knows perfectly hi wk for all k.
Fig. 9. System sum-rate comparison when SN R = 30 dB and Nb = 2/4.
user subset, the DFT SDMA+ZF scheme brings in significant
improvement. If we further introduce the phase compensation
concept, i.e., our proposed scheme 2, the precoded transmit
signals can better match the beamspace channels and the system
sum rate increases greatly. Since the DFT scheme 1 utilizes
much more channel information, it presents obvious superiority
over the scheme 2. The dedicated scheme1 further employs the
dedicated beamforming method, and it outperforms the DFT
scheme1 and all the other schemes.
Fig. 10 gives the system sum-rate comparison of the six
schemes when K = 30 and Nb = 2/4. It is easy to find out that,
as the SNR increases, the achieved system sum rate is improved
for all these six schemes. The dedicated scheme 1 performs
best, while the DFT scheme 1 is slightly inferior. The scheme 2
requires far less feedback and computation but still performs
well. Meanwhile, when we raise Nb from 2 to 4, more users
are included into the interference user subset. Subsequently, the
DFT SDMA+ZF scheme achieves much higher sum rate, while
the two unscheduled schemes go backward instead. Therefore,
interference control is essential to multiuser systems. However,
with the expansion of the interference user subset, IUI suppression requires more complicated ZF precoding calculation.
If we keep increasing K and Nb , the size of the interference
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HAN et al.: INVESTIGATION OF TRANSMISSION SCHEMES FOR MILLIMETER-WAVE MASSIVE MU-MIMO SYSTEMS
11
sparsity. In other words, our previous work is applicable in
single-path and weak multipath scenarios, which correspond to
the sparse channel networks.
VII. C ONCLUSION
Fig. 10. System sum-rate comparison when K = 30 and Nb = 2/4.
Millimeter-wave massive MIMO systems provide preferable conditions for multiuser transmission. In this paper, we
have investigated the beamspace characters under two transformations and gave the low-rank representation of beamspace
channel vectors. Due to the sparsity, we first proposed an
enhanced SDMA scheme, which overcomes the difficulties
of downlink CSI feedback in massive MIMO FDD systems
and performs well in achieving high sum rate. Based on the
enhanced SDMA scheme and the beamspace phasic relations,
we further proposed a joint transmission scheme, which incorporates a double-precoding-based SDMA and a ZF-precoding
interference suppression to reduce the feedback amount and
computation complexity. Simulation results illustrated that both
schemes have excellent performance in achieving their corresponding targets of sum-rate maximization or complexity
reduction.
R EFERENCES
Fig. 11. System sum-rate comparison, when four NLOS paths exist and the
energy of each NLOS path is 5 dB/3 dB lower than the LOS path.
user subset will increase and the scheme 2 approaches the DFT
scheme 1. In other words, both the proposed schemes perform
well, and there should be a tradeoff between system sum rate
and feedback and computation.
We further evaluate the extensibility of the outer precoding of
scheme 2 to multipath propagation scenarios. Consider the previously mentioned poor NLOS environment, where only four
NLOS paths exist. Fig. 11 compares the sum-rate performance
of scheme 2 with the DFT SDMA+ZF scheme, when the energy
of each NLOS path is 5 dB/3 dB lower than the LOS path. It can
be seen from the solid lines that the outer precoding still works
well. If the energy difference is reduced from 5 dB to 3 dB, the
performance of outer precoding becomes poor, and the system
sum rate decreases significantly due to the loss of beamspace
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Yu Han (S’14) received the B.S. degree in
communications engineering from the Hangzhou
Dianzi University, Hangzhou, China, in 2012 and the
M.S. degree in communications and information systems from the Southeast University, Nanjing, China,
in 2015.
In May 2015, she joined the Huawei Shanghai
Research Institute, Shanghai, China. Her research
interests include massive multiple-input–multipleoutput systems, millimeter wave, multiuser transmission schemes, and beamforming techniques.
Haochuan Zhang (M’15) received the Ph.D. degree
in communication and information systems from
the Beijing University of Posts and Telecommunications, Beijing, China, in 2011.
From 2011 to 2014, he was with Ericsson Research, Stockholm, Sweden. In 2014, he joined the
Faculty of Guangdong University of Technology,
Guangzhou, China, where he is currently an Associate Professor in the School of Automation. He has
published more than 15 technical papers (mostly in
IEEE journals and flagship conferences), and he also
holds 15 patents (in U.S., Europe, or China). His current research interests
include signal processing, system design, and performance analysis of wireless
communications.
Shi Jin (S’06–M’07) received the B.S. degree in
communications engineering from Guilin University
of Electronic Technology, Guilin, China, in 1996; the
M.S. degree from Nanjing University of Posts and
Telecommunications, Nanjing, China, in 2003; and
the Ph.D. degree in communications and information systems from the Southeast University, Nanjing,
in 2007.
From June 2007 to October 2009, he was a Research Fellow with the Adastral Park Research Campus, University College London, London, U.K. He is
currently with the Faculty of the National Mobile Communications Research
Laboratory, Southeast University. His research interests include space–time
wireless communications, random matrix theory, and information theory.
Dr. Jin and his coauthors were recipients of the 2011 IEEE Communications
Society Stephen O. Rice Prize Paper Award in the field of communication
theory and the 2010 Young Author Best Paper Award from the IEEE Signal
Processing Society. He serves as an Associate Editor for the IEEE T RANS ACTIONS ON W IRELESS C OMMUNICATIONS, the IEEE C OMMUNICATIONS
L ETTERS , and IET Communications.
Xiao Li (S’06–M’10) received the Ph.D. degree in
communication and information systems from the
Southeast University, Nanjing, China, in 2010.
She then joined the School of Information Science and Engineering, Southeast University, where
she has been an Associate Professor in information systems and communications since May 2014.
From January 2013 to January 2014, she was a
Postdoctoral Fellow at The University of Texas at
Austin, Austin, TX, USA. Her current research interests include massive multiple-input–multiple-output
(MIMO), 3-D beamforming, multiuser MIMO.
Dr. Li was a recipient of the 2013 National Excellent Doctoral Dissertation
of China for her Ph.D. dissertation.
Rong Yu (S’05–M’08) received the Ph.D. degree
from Tsinghua University, Beijing, China, in 2007.
After that, he joined the School of Electronic and
Information Engineering, South China University of
Technology (SCUT), Guangzhou, China. In 2010,
he joined the Institute of Intelligent Information
Processing, Guangdong University of Technology
(GDUT), Guangzhou, where he is currently a Full
Professor. He is the coinventor of over ten patents
and author or coauthor of over 70 international
journal and conference papers. His research interest
mainly focuses on wireless communications and networking, including cognitive radio, wireless sensor networks, and home networking.
Dr. Yu is a member of the Home Networking Standard Committee in China,
where he leads the standardization work of three standards. He currently serves
as the Deputy Secretary General of the Internet of Things (IoT) Industry
Alliance, Guangdong, China, and as the Deputy Head of the IoT Engineering
Center, Guangdong, China.
Yan Zhang (SM’10) received the Ph.D. degree
in electrical and electronics engineering from the
Nanyang Technological University, Singapore.
He is currently the Head of the Department of
Networks at Simula Research Laboratory, Fornebu,
Norway, and an Associate Professor (part time) with
the Department of Informatics, University of Oslo,
Norway. His current research interest include wireless networks and reliable and secure cyberphysical
systems (e.g., healthcare, transport, smart grid, etc.).
Dr. Zhang is a Senior Member of the IEEE Communications and IEEE Vehicular Technology Societies. He serves as a Technical Program Committee Member for numerous international conferences,
including the IEEE International Conference on Computer Communications
(INFOCOM), the IEEE International Conference on Communications (ICC),
the IEEE Global Communications Conference (GLOBECOM), and the IEEE
Wireless Communications and Networking Conference (WCNC). He has been
a recipient of seven Best Paper Awards. He serves as an Associate Editor
or on the editorial board of a number of well-established scientific international journals, e.g., Wiley Wireless Communications and Mobile Computing
(WCMC). He also serves as the Guest Editor for the IEEE T RANSACTIONS
ON I NDUSTRIAL I NFORMATICS , the IEEE C OMMUNICATIONS M AGAZINE,
the IEEE W IRELESS C OMMUNICATIONS, and the IEEE T RANSACTIONS ON
D EPENDABLE AND S ECURE C OMPUTING. He has chair positions in a number
of conferences, including the IEEE International Symposium on Personal,
Indoor and Mobile Radio Communications (PIMRC) 2016, the IEEE Consumer
Communications and Networking Conference (CCNC) 2016, the Wireless Internet Conference (WICON) 2016, the IEEE International Conference on Smart
Grid Communications (SmartGridComm) 2015, and the IEEE International
Conference on Cloud Computing Technology and Science (CloudCom) 2015.