40 research outputs found
A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives
The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain
Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens
Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens
Identification of key research topics in 5G using co-word analysis
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe aim of this research is to better understand the field of 5G by analyzing the more than 10000 publications found in the Web of Science database. To achieve this, a co-word analysis was performed to identify research topics based on the author keywords and a strategic diagram was used to measure their level of maturity and relevance to the field. In total this analysis identified that all the articles can be grouped into seven topics, from which, two are mature but peripheral, one is both well developed and central to the field, and the rest are central, but underdeveloped. The value of this research, was the usage of a well-established technique that has been used in many fields, but never in the field of 5G which is growing in relevance
On the performance of hybrid beamforming for millimeter wave wireless networks
The phenomenal growth in the demand for mobile wireless data services is pushing the boundaries of modern communication networks. Developing new technologies that can provide unprecedented data rates to support the pervasive and
exponentially increasing demand is therefore of prime importance in wireless communications. In existing communication systems, physical layer techniques are
commonly used to improve capacity. Nevertheless, the limited available resources
in the spectrum are unable to scale up, fundamentally restricting further capacity increase. Consequently, alternative approaches which exploit both unused and
underutilised spectrum bands are highly attractive. This thesis investigates the
use of the millimeter wave (mmWave) spectrum as it has the potential to provide
unlimited bandwidth to wireless communication systems.
As a first step toward realising mmWave wireless communications, a cloud radio access network using mmWave technology in the fronthaul and access links
is proposed to establish a feasible architecture for deploying mmWave systems
with hybrid beamforming. Within the context of a multi-user communication
system, an analytical framework of the downlink transmission is presented, providing insights on how to navigate across the challenges associated with high-frequency transmissions. The performance of each user is measured by deriving
outage probability, average latency and throughput in both noise-limited and
interference-limited scenarios. Further analysis of the system is carried out for
two possible user association configurations. By relying on large antenna array
deployment in highly dense networks, this architecture is able to achieve reduced
outages with very low latencies, making it ideal to support a growing number of
users.
The second part of this work describes a novel two-stage optimisation algorithm
for obtaining hybrid precoders and combiners that maximise the energy efficiency
(EE) of a general multi-user mmWave multiple-input, multiple-output (MIMO)
interference channel network involving internet of things (IoT) devices. The hybrid transceiver design problem considers both perfect and imperfect channel
state information (CSI). In the first stage, the original non-convex multivariate
EE maximization problem is transformed into an equivalent univariate problem
and the optimal single beamformers are then obtained by exploiting the correlation between parametric and fractional programming problems and the relationship between weighted sum rate (WSR) and weighted minimum mean squared
error (WMMSE) problems. The second stage involves the use of an orthogonal
matching pursuit (OMP)-based algorithm to obtain the energy-efficient hybrid
beamformers. This approach produces results comparable to the optimal beam-forming strategy but with much lower complexity, and further validates the use
of mmWave networks in practice to support the demand from ubiquitous power-constrained smart devices.
In the third part, the focus is on the more practical scenario of imperfect CSI for
multi-user mmWave systems. Following the success of hybrid beamforming for
mmWave wireless communication, a non-traditional transmission strategy called
Rate Splitting (RS) is investigated in conjunction with hybrid beamforming to
tackle the residual multi-user interference (MUI) caused by errors in the estimated
channel. Using this technique, the transmitted signal is split into a common
message and a private message with the transmitted power dynamically divided
between the two parts to ensure that there is interference-free transmission of the
common message. An alternating maximisation algorithm is proposed to obtain
the optimal common precoder. Simulation results show that the RS transmission
scheme is beneficial to multi-user mmWave transmissions as it enables remarkable
rate gains over the traditional linear transmission methods.
Finally, the fourth part analyses the spectral efficiency (SE) performance of a
mmWave system with hybrid beamforming whilst accounting for real-life practice transceiver hardware impairments. An investigation is conducted into three
major hardware impairments, namely, the multiplicative phase noise (PN), the
amplified thermal noise (ATN) and the residual additive transceiver hardware impairments (RATHI). The hybrid precoder is designed to maximise the SE by the
minimisation of the Euclidean distance between the optimal digital precoder and
the noisy product of the hybrid precoders while the hybrid combiners are designed
by the minimisation of the mean square error (MSE) between the transmitted
and received signals. Multiplicative PN was found to be the most critical of the
three impairments considered. It was observed that the additive impairments
could be neglected for low signal-to-noise-ratio (SNR) while the ATNs caused a
steady degradation to the SE performance
A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks
The diverse service requirements coming with the
advent of sophisticated applications as well as a large number
of connected devices demand for revolutionary changes in the
traditional distributed radio access network (RAN). To this end,
Cloud-RAN (CRAN) is considered as an important paradigm
to enhance the performance of the upcoming fifth generation
(5G) and beyond wireless networks in terms of capacity, latency,
and connectivity to a large number of devices. Out of several
potential enablers, efficient resource allocation can mitigate various
challenges related to user assignment, power allocation, and
spectrum management in a CRAN, and is the focus of this paper.
Herein, we provide a comprehensive review of resource allocation
schemes in a CRAN along with a detailed optimization taxonomy
on various aspects of resource allocation. More importantly,
we identity and discuss the key elements for efficient resource
allocation and management in CRAN, namely: user assignment,
remote radio heads (RRH) selection, throughput maximization,
spectrum management, network utility, and power allocation.
Furthermore, we present emerging use-cases including heterogeneous
CRAN, millimeter-wave CRAN, virtualized CRAN, Non-
Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex
enabled CRAN to illustrate how their performance can
be enhanced by adopting CRAN technology. We then classify
and discuss objectives and constraints involved in CRAN-based
5G and beyond networks. Moreover, a detailed taxonomy of
optimization methods and solution approaches with different
objectives is presented and discussed. Finally, we conclude the
paper with several open research issues and future directions
Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming
The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing.
Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between and . Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than dB.
On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art
Energy-Efficient Hybrid Precoding Design for Integrated Multicast-Unicast Millimeter Wave Communications with SWIPT
In this paper, we investigate the energy-efficient hybrid precoding design
for integrated multicast-unicast millimeter wave (mmWave) system, where the
simultaneous wireless information and power transform is considered at
receivers. We adopt two sparse radio frequency chain antenna structures at the
base station (BS), i.e., fully-connected and subarray structures, and design
the codebook-based analog precoding according to the different structures.
Then, we formulate a joint digital multicast, unicast precoding and power
splitting ratio optimization problem to maximize the energy efficiency of the
system, while the maximum transmit power at the BS and minimum harvested energy
at receivers are considered. Due to its difficulty to directly solve the
formulated problem, we equivalently transform the fractional objective function
into a subtractive form one and propose a two-loop iterative algorithm to solve
it. For the outer loop, the classic Bi-section iterative algorithm is applied.
For the inner loop, we transform the formulated problem into a convex one by
successive convex approximation techniques and propose an iterative algorithm
to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop
a zero forcing (ZF) technique-based low complexity iterative algorithm.
Specifically, the ZF technique is applied to cancel the inter-unicast
interference and the first order Taylor approximation is used for the
convexification of the non-convex constraints in the original problem. Finally,
simulation results are provided to compare the performance of the proposed
algorithms under different schemes.Comment: IEEE_TVT, Accep