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3066 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 20, NO. 4, FOURTH QUARTER 2018
the hybrid beamforming architecture, analog and digital beam-
forming techniques for mmWave and massive MIMO systems,
and beamforming in small cells.
A. Hybrid Beamforming Architectures
In contrast to general 5G overview papers, there are only
few papers in literature that illustrates the hardware aspects of
hybrid beamforming. Molisch et al. [21] categorized hybrid
beamforming architectures for the downlink transmission at
BS according to the required CSI, the complexity (full com-
plexity, reduced complexity, and switched), and the carrier
frequency range (cm-wave versus mmWave). The utilization
of the large array antenna elements aims to increase the
capacity gains in massive MIMO systems. Based upon the
analysis provided in the paper, it is clear that there is no sin-
gle hybrid beamforming structure that can provide the best
trade-off between complexity and performance. Thus, in order
to get the best performance from hybrid beamforming, the
structure needs to be dynamic based upon the application
and channel conditions. Typical mmWave hybrid beamform-
ing architectures, signal processing algorithms, and RF system
implementation aspects are described in [22]. In addition,
determination of the optimal number of RF chains (which
has a direct impact on the complexity, cost and power con-
sumption) is carried out under practical constraints such as
the number of multiplexed streams, antenna elements, con-
stant amplitude and quantized phases of the analog phase
shifters, is generally based on the sum rate maximization [22].
The authors present hybrid beamforming architectures, signal
processing algorithms and implementation aspects for various
indoor and outdoor environments such as WPAN, WLAN and
outdoor, which can scratch many use cases for visualizing
hybrid beamforming in a 5G network.
Heath et al. [12] classify the hybrid beamforming on the
basis of analog beamforming components. The analog beam-
forming can be implemented either by digitally controlled
phase shifters, electronic switches or lens antenna. The dig-
itally controlled phase shifters can eliminate the residual
interference between data streams but they suffer from high
power consumption and the quantization error because only
finite step phase shifts are available. Alternatively, switch
based analog combiner exploits the sparse nature of mmWave
massive MIMO channel and only a subset of antennas is
selected instead of an optimization over all quantized phase
values. The third approach uses lens antenna for the ana-
log beamforming at the front-end. In this architecture, the
continuous aperture lens antenna steers the beam, controlled
by the mmWave beam selector. The lens based front-end
can be realized by the unitary discrete Fourier transform
matrix. However, this paper only presents the various design
of analog beamforming without discussing the detail of digital
counter-part. The hardware complexity of hybrid beamform-
ing in terms of ADC resolution is surveyed in [63]. It has
been shown that low resolution ADC (1-bit ADC) has gained
much attention in research community. Although 1-bit ADCs
impose EE but suffer from rate loss and require long training
sequence for channel estimation. This paper does not account
the other hardware components like phase shifters or switches
in different hybrid beamforming architectures.
In summary, none of the aforementioned paper comprehen-
sively reviews the architecture, performance, and the complex-
ity of the hybrid beamforming. Whereas, in this paper, we
provide the complete review of hybrid beamforming archi-
tectures, namely: fully-connected, partially- or sub-connected,
fully-connected with virtual sectorization, dynamic subar-
ray, and hybrid beamforming with low complexity analog
beamforming. Also, we compare various hybrid beamforming
architectures on the basis of power hungry ADCs and phase
shifters.
B. Analog and Digital Beamforming Techniques for
MmWave and Massive MIMO Systems
Next generation cellular networks will utilize mmWave to
support more users and to achieve higher data rates. However,
network nodes tuned at mmWave experience small coverage
area problems as well as outdoor penetration difficulty. The
main challenges in mmWave cellular networks are found in
spatial management, link margin operation, interference man-
agement, object blockage, etc. The link margin, for instance,
can be overcome by enabling beamforming approach in high
directional antenna arrays (massive MIMO). Niu et al. [54]
conduct a survey to delve deeper into proposed solutions for
combating the mmWave challenges. In the light of these solu-
tions, architecture and protocols for mmWave communications
have been proposed. Furthermore, mmWave applications in 5G
wireless networks such as wireless backhaul, small cell access,
etc., are presented. Some investigations to unveil open research
issues related to mmWave of 5G have been outlined that will
help continuing the development of mmWave for 5G wireless
network.
Massive MIMO possesses many potential benefits including
extensive use of inexpensive low-power components, reduced
latency, MAC layer simplification, and robustness against
intentional jamming. However, massive MIMO technology
uncovers new problems such as pilot contamination which
needs to be immediately addressed. Accurate and timely CSI
estimation is vital for wireless communications. In case of
multiuser MIMO or massive MIMO communications, this
CSI becomes more important to enable the multiple streams
and eliminate the inter-user interference. The estimation of
the CSI is carried out by the training sequences (also called
pilots). The pilot contamination is caused by the inter-cell
and intra-cell interference during pilot transmissions from the
UEs to BS. In this context, [64] provides an up-to-date sur-
vey on pilot contamination and presents the major sources
that impact the massive MIMO system performance using
TDD. These sources include non-reciprocal transceivers and
hardware impairments, etc. Subsequently, the paper reviews
and categorizes some of the established theories that ana-
lyze the effect of pilot contamination on the performance of
massive MIMO systems. Finally, the paper outlines the open
research issues of pilot contamination which include computa-
tional complexity, training overhead, and channel reciprocity
usage. The paper addresses and proposes a real challenge in