Gao, Z., Dai, L., Mi, D., Wang, Z., Imran, M. A., and Shakir, M. Z. (2015)
MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network.
IEEE Wireless Communications, 22(5), pp. 13-21.
There may be differences between this version and the published version. You are
advised to consult the publisher’s version if you wish to cite from it.
http://eprints.gla.ac.uk/132680/
Deposited on: 12 December 2016
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1
MmWave Massive MIMO Based Wireless Backhaul
for 5G Ultra-Dense Network
Zhen Gao, Linglong Dai, De Mi, Zhaocheng Wang, Muhammad Ali Imran, and Muhammad Zeeshan Shakir
Abstract—Ultra-dense network (UDN) has been considered as a
promising candidate for future 5G network to meet the explosive
data demand. To realize UDN, a reliable, Gigahertz bandwidth,
and cost-effective backhaul connecting ultra-dense small-cell base
stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave
(mmWave) can provide the potential Gbps traffic for wireless
backhaul. Moreover, mmWave can be easily integrated with
massive MIMO for the improved link reliability. In this article,
we discuss the feasibility of mmWave massive MIMO based
wireless backhaul for 5G UDN, and the benefits and challenges
are also addressed. Especially, we propose a digitally-controlled
phase-shifter network (DPSN) based hybrid precoding/combining
scheme for mmWave massive MIMO, whereby the low-rank
property of mmWave massive MIMO channel matrix is leveraged
to reduce the required cost and complexity of transceiver with
a negligible performance loss. One key feature of the proposed
scheme is that the macro-cell BS can simultaneously support
multiple small-cell BSs with multiple streams for each small-
cell BS, which is essentially different from conventional hybrid
precoding/combining schemes typically limited to single-user
MIMO with multiple streams or multi-user MIMO with single
stream for each user. Based on the proposed scheme, we further
explore the fundamental issues of developing mmWave massive
MIMO for wireless backhaul, and the associated challenges,
insight, and prospect to enable the mmWave massive MIMO
based wireless backhaul for 5G UDN are discussed.
Index Terms—Ultra-dense network (UDN), mmWave back-
haul, massive MIMO, precoding/combining.
I. INTRODUCTION
T
HE explosive traffic demand is challenging current cellu-
lar networks, including the most advanced 4G network.
It has been the consensus that future 5G network should
realize the goals of thousand-fold system capacity, hundred-
fold energy efficiency, and tens of lower latency. To realize
such aggressive 5G version, ultra-dense network (UDN) has
been considered as a promising system architecture to enable
Gbps user experience, seamless coverage, and green commu-
nications [1].
In UDN, as shown in Fig. 1, the macro-cell base stations
(BSs) with large coverage usually control the user scheduling,
resource allocation, and support high-mobility users, while
Z. Gao, L. Dai, and Z. Wang are with Tsinghua National Laboratory
for Information Science and Technology (TNList), Department of Electronic
Engineering, Tsinghua University, Beijing 100084, P. R. China (E-mails: gao-
z11@mails.tsinghua.edu.cn; {daill, zcwang}@tsinghua.edu.cn).
D. Mi and M. A. Imran are with Institute for Communication Systems
(ICS), Home of 5G Innovation Center (5GIC), University of Surrey, Guildford,
UK (E-mails: {d.mi, m.imran}@surrey.ac.uk).
M. Z. Shakir is with the Department of Electrical and Computer Engineer-
ing, Texas A&M University at Qatar (TAMUQ), Education City, P.O. Box
23874, Doha, Qatar (E-mail: muhammad.shakir@qatar.tamu.edu).
This work was supported in part by the International Science & Technology
Cooperation Program of China (Grant No. 2015DFG12760), the National Nat-
ural Science Foundation of China (Grant No. 61201185 and 61271266), the
Beijing Natural Science Foundation (Grant No. 4142027), and the Foundation
of Shenzhen government.
Fig. 1. MmWave massive MIMO based wireless backhaul for 5G UDN.
many ultra-dense small-cell BSs with much smaller coverage
provide the high data rate for low-mobility users. Due to ultra-
dense small-cell BSs, better frequency reuse can be achieved,
and energy efficiency can be also substantially improved due
to the reduced path loss in small cells [1].
To enable UDN, a reliable, cost-effective, and Gigahertz
bandwidth backhaul connecting macro-cell BS and the asso-
ciated small-cell BSs is prerequisite. It has been demonstrated
that backhaul with 1∼10 GHz bandwidth is required to ef-
fectively support UDN [2]. Conventional optical fiber enjoys
large bandwidth and reliability, but its application to UDN as
backhaul may not be an economical choice for operators due to
the restriction of deployment and installation. Hence, wireless
backhaul, especially millimeter-wave (mmWave) backhaul, is
more attractive to overcome the geographical constraints. The
advantages of mmWave backhaul are:
• A large amount of underutilized band in mmWave can be
leveraged to provide the potential Gigahertz transmission
bandwidth, which is different from scarce microwave
band in conventional cellular networks [3].
• A large number of antennas can be easily employed for
mmWave communications due to the small wavelength
of mmWave, which can improve the signal directivity
(reduce the co-channel interference) and link reliability
(mitigate the large path loss) for mmWave backhaul [4].
This article combines mmWave with a large number of
antennas, which is also referred to as mmWave massive
MIMO, to provide wireless backhaul for future 5G UDN. The
contributions of this article are listed as follows:
• We discuss the feasibility and challenges of the mmWave
massive MIMO based backhaul for UDN, where its
advantages, differences compared with conventional mas-
sive MIMO working at sub 3∼6 GHz for radio access
networks (RAN) are also addressed. Moreover, the spar-
sity of mmWave massive MIMO channels is stressed.
• We explore key issues and potential research directions
2
of the cost-effective mmWave massive MIMO for UDN
backhaul. Especially, a digitally-controlled phase-shifter
network (DPSN) based hybrid precoding/combining and
the associated compressive sensing (CS) based channel
estimation is proposed.
• We address the benefits of the wireless backhaul for 5G
UDN with the technique of mmWave massive MIMO,
which may provide a viable approach to realize the novel
backhaul network topology, scheduling strategy, efficient
in-band backhaul in mmWave.
II. FEASIBILITY AND CHALLENGES OF MMWAVE
MASSIVE MIMO FOR WIRELESS BACKHAUL IN 5G UDN
In UDN, small cells are densely deployed in hotspots (e.g.,
office buildings, shopping malls, resident apartments) with
high data rate to provide traffic offload from macro cells,
since the large majority of traffic demand comes from these
hotspots. Hence, the backhaul between the macro-cell BS and
the associated small-cell BSs should provide large bandwidth
with reliable link transmission. Besides, power efficiency and
deployment cost are also key considerations for operators.
A. MmWave is Suitable for Wireless Backhaul in 5G UDN
Traditionally, mmWave is not used for RAN in existing
cellular networks due to its high path loss and expensive
electron components. However, mmWave is especially suitable
for backhaul in UDN due to the following reasons.
• High Capacity and Inexpensive: The large amount of
underutilized mmWave including unlicensed V-band (57-
67GHz) and lightly licensed E-band (71-76GHz and 81-
86GHz) (the specific regulation may vary from country to
country) can provide the potential Gigahertz transmission
bandwidth [3]. For example, more than one Gbps back-
haul capacity can be supported over 250 MHz channel in
E-band [2].
• Immunity to Interference: Transmission distance comfort
zone for E-band is up to several kilometers due to the
rain attenuation, while that for V-band is about 500-
700m due to both the rain and oxygen attenuation. Owing
to the high path loss, mmWave is suitable for UDN,
where the improved frequency reuse and reduced inter-
cell interference are expected. It should be pointed out
that rain attenuation is not a big issue for mmWave
used in UDN. If we consider the very heavy rainfall of
25mm/hr, the rain attenuation is only around 2 dB in E-
band if we consider the distance of backhaul link is 200m
in typical urban UDN [3].
• Small Form Factor: The small wavelength of mmWave
implies that massive antennas can be easily equipped
at both macro and small-cell BSs, which can improve
the signal directivity and compensate severe path loss of
mmWave to achieve larger coverage in turn [4]. Hence
the compact mmWave backhaul equipment can be easily
deployed with low cost sites (such as light poles, building
walls, bus stations) and short installation time.
B. MmWave Massive MIMO is Different from Microwave
Massie MIMO
Inheriting the advantages from conventional microwave
massive MIMO, mmWave massive MIMO has the flexi-
ble beamforming, spatial multiplexing, and diversity. Hence
mmWave massive MIMO brings not only the improved relia-
bility of backhaul link, but also new architecture of backhaul
network including the flexible network topology, scheduling
scheme, which will be further detailed in Section VI. However,
compared with conventional microwave (sub 3∼6 GHz) mas-
sive MIMO used for RAN, the implementation of mmWave
massive MIMO also brings challenges as follows.
• First, the cost and complexity of transceiver including
high-speed analog-digital converters (ADCs) and digital-
analog converters (DACs), synthesizers, mixers, etc., in
mmWave communications are much larger than that in
conventional microwave communications. Hence, mas-
sive low-cost antennas but a limited number of expensive
baseband (BB) chains can be an appealing transceiver
structure for mmWave massive MIMO, which, however,
challenges conventional precoding/combining schemes.
• Second, the number of antennas in mmWave at both
macro and small-cell BSs can be much larger than that in
conventional microwave massive MIMO due to the much
smaller wavelength of mmWave. This implies the chal-
lenge that channel estimation in mmWave massive MIMO
can be more difficult even when time division duplex
(TDD) leveraging the channel reciprocity is considered.
Even for TDD-based mmWave communications, the syn-
chronization and calibration error of radio frequency (RF)
chains to guarantee the channel reciprocity are not trivial
[5].
• Third, since single-antenna users are typically considered
in microwave massive MIMO due to the limited form fac-
tor, only channel state information at transmitter (CSIT)
is required for precoding. However, for mmWave massive
MIMO where each small-cell BS can be equipped with
massive antennas, precoding in the uplink and combining
in the downlink at small-cell BSs are also necessary,
since precoding/combining can effectively support multi-
ple streams and directional transmission for the improved
link reliability. Therefore, channel state information at
receiver (CSIR) is also required for mmWave massive
MIMO, which indicates another challenge that channel
estimation acquired in the uplink by leveraging the chan-
nel reciprocity should also be feedback to small-cell BSs.
III. MMWAVE CHANNEL CHARACTERISTICS
As discussed above, the mmWave massive MIMO based
backhaul is apt to the transceiver with the limited number of
BB chains. Compared with microwave massive MIMO using
full digital precoding, precoding/combining with the smaller
number of BB chains than that of antenna elements can make
mmWave massive MIMO suffer from a certain performance
loss, which is largely dependent on the propagation condition
of mmWave massive MIMO channels.
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Fig. 2. Energy probability distribution of singular values with descending
order of mmWave massive MIMO channel matrix versus different L’s, where
N
T
= 512 and N
R
= 32.
A. MmWave Channels with Spatial/Angular Sparsity
Extensive experiments have shown that mmWave massive
MIMO channels exhibit the obviously spatial/angular sparsity
due to its high path loss for non-line-of-sight (NLOS) signals,
where only a small number of dominated multipaths (typi-
cally, 3∼5 multipaths in realistic environments [6]) consist of
mmWave MIMO multipath channels. If we consider the widely
used uniform linear array (ULA), the point-to-point mmWave
massive MIMO channel can be modeled as [6]
H=
s
N
T
N
R
ρ
L
X
l=1
α
l
a
T
(θ
l
) b
∗
R
(ϕ
l
)=
s
N
T
N
R
ρ
A
T
DB
∗
R
,
(1)
where N
T
and N
R
are the numbers of transmit and receive
antennas, respectively, ρ is the average path loss, L is the
number of multipaths, α
l
is the complex gain of the lth
path, θ
l
∈ [0, 2π] and ϕ
l
∈ [0, 2π] are azimuth angles of
departure or arrival (AoD/AoA). In addition, a
T
(θ
l
) =
1
√
N
T
1, e
j2πd sin(θ
l
)/λ
, · · · , e
j2π(N
T
−1)d sin(θ
l
)/λ
T
and
b
R
(ϕ
l
) =
1
√
N
R
1, e
j2πd sin(ϕ
l
)/λ
, · · · , e
j2π(N
R
−1)d sin(ϕ
l
)/λ
T
are steering vectors at the transmitter and receiver,
respectively, A
T
= [a
T
(θ
1
) |a
T
(θ
2
) | · · · |a
T
(θ
L
)],
B
R
= [b
R
(ϕ
1
) |b
R
(ϕ
2
) | · · · |b
R
(ϕ
L
)]
∗
, and the diagonal
matrix D = diag {α
1
, α
2
, · · · , α
L
}, where λ and d are
wavelength and antenna spacing, respectively.
B. Low-Rank Property of MmWave Massive MIMO Channels
The spatial/angular sparsity of mmWave channels with
small L (e.g., 3∼5) and massive MIMO channel matrix with
large N
T
, N
R
(dozens even hundreds) implies that mmWave
massive MIMO channel matrix has the low-rank property [7].
For example, Fig. 2 provides the energy probability distribu-
tion of singular values of H with descending order against
different L’s, where N
T
= 512, N
R
= 32, and path gains fol-
low the independent and identically distributed (i.i.d.) complex
Gaussian distribution. It can be observed that the mmWave
massive MIMO channel matrix has the obvious low-rank
property. If we consider single user (SU)-MIMO with CSIT
for precoding and CSIR for combining, the low-rank channel
matrix indicates that the number of effective independent
streams which can be exploited is small. Theoretical analysis
has shown that the capacity of MIMO systems over sparse
mmWave channels appears ceiling effect with the increased
number of BB chains [7]. Hence, we can leverage the finite
number of BB chains to maximize the backhaul capacity over
sparse mmWave channels, where the number of BB chains can
be as small as the effective rank of mmWave massive MIMO
channel matrix.
IV. KEY ISSUES OF DESIGNING MMWAVE MASSIVE
MIMO FOR 5G UDN BACKHAUL
A. Hybrid Precoding/Combining Design
In order to realize the reliable point-to-multiple-points
(P2MP) backhaul link, mmWave massive MIMO for UDN
… …
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…
…
…
…
…
…
PA
…
LNA
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…
…
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Fig. 3. Comparison of precoding/combining schemes, where PA denotes power amplifier and LNA denotes low-noise amplifier: (a) Analog pre-
coding/combining scheme in mmWave multi-antenna systems; (b) Digital precoding in microwave massive MIMO for RAN; (c) Conventional hybrid
precoding/combining in mmWave massive MIMO for RAN; (d) Proposed DPSN based hybrid precoding/combining for mmWave massive MIMO in UDN
backhaul.
4
backhaul should exploit the flexible beamforming and spatial
multiplexing to simultaneously support multiple small-cell
BSs and provide multiple streams for each small-cell BS,
which challenges conventional precoding/combining schemes.
1) Overview of Existing Precoding/Combining Schemes:
Conventional mmWave multi-antenna systems utilize single
RF chain and analog (e.g., ferrite based) phase-shifters for
precoding/combining as shown in Fig. 3 (a), but it is limited
to SU-MIMO with single stream. Full digital precoding in mi-
crowave massive MIMO, as shown in Fig. 3 (b), can simultane-
ously support multiple single-antenna users, i.e., MU-MIMO,
but it requires one specific RF chain to be connected to each
antenna, which can be unaffordable in mmWave communi-
cations [7]. Recently, the hybrid precoding/combining scheme
consisting of analog and digital precoding/combining as shown
in Fig. 3 (c), has been proposed for mmWave massive MIMO
with the reduced cost and complexity of transceiver. However,
state-off-the-art hybrid precoding/combining schemes are usu-
ally limited to SU-MIMO with multiple streams or multi-user
(MU)-MIMO with single stream for each user [4], [6]–[8].
2) Proposed DPSN Based Hybrid Precoding/Combining:
Multi-User and Multi-Stream: To support multi-user and
multi-stream, we propose the DPSN based hybrid precod-
ing/combining scheme as shown in Fig. 3 (d), which can effec-
tively reduce the cost and complexity of transceiver. Specifi-
cally, consider the macro-cell BS has N
Ma
a
antennas but N
Ma
BB
BB chains, where N
Ma
a
≫ N
Ma
BB
, while each small-cell BS
has N
Sm
a
antennas but N
Sm
BB
BB chains, where N
Sm
a
≫ N
Sm
BB
.
The number of simultaneously supported small-cell BSs is K.
H
k
∈ C
N
Ma
a
×N
Sm
a
with N
Ma
a
> N
Sm
a
denotes the mmWave
massive MIMO channel matrix associated with the macro-cell
BS and the kth small-cell BS, and it can be expressed as
follows according to singular value decomposition (SVD):
H
k
=
U
1
k
|U
2
k
Σ
1
k
0
0 Σ
2
k
0 0
V
1
k
∗
V
2
k
∗
≈ U
1
k
Σ
1
k
V
1
k
∗
,
(2)
where both
U
1
k
|U
2
k
∈ C
N
Ma
a
×N
Ma
a
and
V
1
k
|V
2
k
∗
∈
C
N
Sm
a
×N
Sm
a
are unitary matrices, Σ
1
k
∈ C
R
k
×R
k
and Σ
2
k
∈
C
(
N
Sm
a
−R
k
)
×
(
N
Sm
a
−R
k
)
are diagonal matrices whose diagonal
elements are singular values of H
k
, and R
k
is the effective
rank of H
k
. The approximation in (2) is due to the low-rank
property of H
k
with Σ
2
k
≈ 0, so that U
1
k
∈ C
N
Ma
a
×R
k
and
V
1
k
∗
∈ C
R
k
×N
Sm
a
.
Eq. (2) indicates that N
Ma
BB
and N
Sm
BB
can be reduced to
R
k
in SU-MIMO due to only N
s
= R
k
effective independent
streams. Moreover, we can use the precoding matrix P
k
=
U
1
k
∗
and the combining matrix C
k
= V
1
k
to effectively real-
ize the independent multi-stream transmission [9]. To achieve
this goal, we can use the emerging low-cost silicon-based SiGe
and CMOS based programmable DPSN [10] to realize partial
precoding/combining in the analog RF. With the cascade of
the digital precoding matrix P
d,k
∈ C
R
k
×R
k
(or combin-
ing matrix C
d,k
∈ C
R
k
×R
k
) and analog precoding matrix
P
a,k
∈ C
R
k
×N
Ma
a
(or combining matrix C
a,k
∈ C
N
Sm
a
×R
k
),
we can use P
d,k
P
a,k
(or C
a,k
C
d,k
) to approximate P
k
(or C
k
). Consider the precoding for instance, we can use
the following iterative approach to acquire P
d,k
and P
a,k
that can minimize kP
k
− P
d,k
P
a,k
k
F
with the constraint
that elements in P
a,k
are constant modulus. We initialize
that
˜
P
k
← P
k
. Then, we perform the following operations
iteratively until P
a,k
and P
d,k
converge: 1) every element of
P
a,k
has the same phase with the corresponding element in
˜
P
k
; 2) P
d,k
← P
k
(P
a,k
)
†
, 3)
˜
P
k
← (P
d,k
)
†
P
k
. Note that
P
a,k
always meets the constraint of constant modulus and ()
†
is the Moore-Penrose pseudoinverse. Similarly, we can acquire
C
d,k
and C
a,k
according to C
k
with the same approach.
Besides, some power allocation strategies such as waterfilling
can be integrated in the digital baseband precoding/combining
to further improve the achievable capacity.
Furthermore, consider the downlink MU-MIMO, where the
channel matrix between macro-cell BS and K small-cell BSs
can be denoted as H ∈ C
N
Ma
a
×KN
Sm
a
, and it can be repre-
sented as H= [H
1
|H
2
| · · · |H
K
] with H
k
≈ U
1
k
Σ
1
k
V
1
k
∗
for
1 ≤ k ≤ K according to (2). Hence we further obtain
H ≈
U
1
1
|U
1
2
| · · · |U
1
K
× diag
Σ
1
1
, Σ
1
2
, · · · , Σ
1
K
× diag
n
V
1
1
∗
,
V
1
2
∗
, · · · ,
V
1
K
∗
o
,
(3)
where H
k
for 1 ≤ k ≤ K are assumed to share the same
effective rank R
k
= R. For precoding/combining in the
proposed MU-MIMO system, the analog precoding matrix at
macro-cell BS is P
a
=
P
T
a,1
|P
T
a,2
| · · · |P
T
a,K
T
∈ C
KR×N
Ma
a
,
and the analog and digital combining matrices for the kth
small-cell BS can be C
a,k
and C
d,k
, respectively. To further
eliminate the multi-user interference, digital precoding P
d
=
˜
P
d
P
a
˜
U
−1
is proposed at the macro-cell BS, where
˜
P
d
=
diag {P
d,1
, P
d,2
, · · · , P
d,K
} and
˜
U =
U
1
1
|U
1
2
| · · · |U
1
K
.
The precoding/combining in the uplink of mmWave massive
MIMO based backhaul is similar to the downlink, which will
not be detailed in this article owing to the space limitation.
The proposed precoding/combining scheme can diagonal-
ize the equivalent channel P
d
P
a
Hdiag {C
1
, C
2
, · · · , C
K
}
with C
k
= C
a,k
C
d,k
to realize multi-user and multi-stream
transmission, which is essentially different from existing
schemes. Moreover, thanks to the obvious low-rank property
of mmWave massive MIMO channel matrix as shown in Fig.
2, the proposed precoding/combining with the reduced number
of BB chains only suffers from a negligible performance loss,
which will be shown in Section V.
B. CSI Acquisition for MmWave Massive MIMO
To effectively realize the proposed DPSN based hybrid pre-
coding/combining scheme, a reliable CSI acquisition scheme
with low overhead is another challenge.
1) Challenging Channel Estimation for MmWave Massive
MIMO: As we have discussed in Section II-B, mmWave
massive MIMO may suffers from the prohibitively high over-
head for channel estimation, and calibration error of RF
chains as well as synchronization are also not trivial in TDD.
Additionally, due to the much smaller number of BB chains
than that of antennas, the effective dimensions that can be
exploited for channel estimation will be substantially reduced
although massive antennas are employed. Furthermore, chan-
nel estimation in the digital baseband should consider the
