49 research outputs found
Capacity Maximisation for Hybrid Digital-to-Analog Beamforming mm-Wave Systems
Millimetre waves (mm-Waves) with massive multiple input and multiple output (MIMO) have the potential to fulfill fifth generation (5G) traffic demands. In this paper, a hybrid digital-to-analog (D-A) precoding system is investigated and a particle swarm optimisation (PSO) based joint D-A precoding optimisation algorithm is proposed. This algorithm maximises the capacity of the hybrid D-A mm-Wave massive MIMO system. The proposed algorithm is compared with three known hybrid D-A precoding algorithms. The analytical and simulation results show that the proposed algorithm achieves higher capacity than the existing hybrid D-A precoding algorithms
Low-Complexity and Robust Hybrid Beamforming Design for Multi-Antenna Communication Systems
This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer is comprised of a baseband digital beamformer and a constant modulus analog beamformer in the radio frequency (RF) part of the system. As in singular-value-decomposition (SVD)-based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived by defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to a wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration
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
Novel transmission and beamforming strategies for multiuser MIMO with various CSIT types
In multiuser multi-antenna wireless systems, the transmission and beamforming strategies that achieve the sum rate capacity depend critically on the acquisition of perfect Channel State Information at the Transmitter (CSIT).
Accordingly, a high-rate low-latency feedback link between the receiver and the transmitter is required to keep the latter accurately and instantaneously informed about the CSI.
In realistic wireless systems, however, only imperfect CSIT is achievable due to pilot contamination, estimation error, limited feedback and delay, etc.
As an intermediate solution, this thesis investigates novel transmission strategies suitable for various imperfect CSIT scenarios and the associated beamforming techniques to optimise the rate performance.
First, we consider a two-user Multiple-Input-Single-Output (MISO) Broadcast Channel (BC) under statistical and delayed CSIT.
We mainly focus on linear beamforming and power allocation designs for ergodic sum rate maximisation.
The proposed designs enable higher sum rate than the conventional designs.
Interestingly, we propose a novel transmission framework which makes better use of statistical and delayed CSIT and smoothly bridges between statistical CSIT-based strategies and delayed CSIT-based strategies.
Second, we consider a multiuser massive MIMO system under partial and statistical CSIT.
In order to tackle multiuser interference incurred by partial CSIT, a Rate-Splitting (RS) transmission strategy has been proposed recently.
We generalise the idea of RS into the large-scale array.
By further exploiting statistical CSIT, we propose a novel framework Hierarchical-Rate-Splitting that is particularly suited to massive MIMO systems.
Third, we consider a multiuser Millimetre Wave (mmWave) system with hybrid analog/digital precoding under statistical and quantised CSIT.
We leverage statistical CSIT to design digital precoder for interference mitigation while all feedback overhead is reserved for precise analog beamforming.
For very limited feedback and/or very sparse channels, the proposed precoding scheme yields higher sum rate than the conventional precoding schemes under a fixed total feedback constraint.
Moreover, a RS transmission strategy is introduced to further tackle the multiuser interference, enabling remarkable saving in feedback overhead compared with conventional transmission strategies.
Finally, we investigate the downlink hybrid precoding for physical layer multicasting with a limited number of RF chains.
We propose a low complexity algorithm to compute the analog precoder that achieves near-optimal max-min performance.
Moreover, we derive a simple condition under which the hybrid precoding driven by a limited number of RF chains incurs no loss of optimality with respect to the fully digital precoding case.Open Acces
Channel estimation and beam training with machine learning applications for millimetre-wave communication systems
The fifth generation (5G) wireless system will extend the capabilities of the fourth generation
(4G) standards to serve more users and provide timely communication. To this end, the carriers
of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave
(mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher
data rates. As a result, the deployment of 5G networks is required to accommodate more antennas
and offer pervasive coverage with controlled power consumption. The complexity of 5G
systems introduces new challenges to traditional signal processing techniques. To address these
challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication
systems. ML can learn patterns from datasets to achieve control and optimisation of
complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel
estimation methods and beam training strategies with the application of ML algorithms for
mmWave wireless systems.
Firstly, the channel estimation and signal detection problem is investigated for orthogonal
frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep
neural network (DNN)-based joint channel estimation and signal detection approach is proposed
to achieve multi-user detection in a one-shot process for non-orthogonal multiple access
(NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by
learning the channel implicitly from suitable training. The proposed approach can be adapted to
work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput
(MIMO) systems. This DNN-based approach is shown to provide good performance for
OFDM systems that suffer from severe inter-symbol interference or where small numbers of
pilot symbols are used.
Secondly, the beam training and tracking problem is studied for mmWave channels with receiver
mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity
beam training strategy is proposed for mobile mmWave channels, which searches
a set of selected beams obtained based on the recent beam search results. By searching only
the adjacent beams to the one recently used, the proposed beam training strategy can reduce
the beam training delay significantly while maintaining high transmission rates. The proposed
strategy works effectively for channel datasets generated using either the stochastic or the raytracing
channel model. This strategy is shown to approach the performance for an exhaustive
beam search while saving up to 92% on the required beam training overhead.
Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep
reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm
is proposed, which can intelligently switch between different beam training methods
such that the average beam training overhead is minimised while achieving good spectral efficiency
or energy efficiency performance. Given the desired performance requirement in the
reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised
for the current channel condition by controlling the number of activated RF chains. The
DRL-based approach can adjust the amount of beam training overhead required according to
the dynamics of the environment. This approach can provide a good overhead-performance
trade-off and achieve higher data rates in channels with significant levels of signal blockage
A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach
5G spectrum uses cutting-edge technology which delivers high data rates, low latency, increased capacity, and high spectrum utilization. To cater to these requirements various technologies are available such as Multiple Access Technology (MAT), Multiple Input Multiple Output technology (MIMO), Millimetre (mm) wave technology, Non-Orthogonal Multiple Access Technology (NOMA), Simultaneous Wireless Information and Power Transfer (SWIPT). Of all available technologies, mmWave is prominent as it provides favorable opportunities for 5G. Millimeter-wave is capable of providing a high data rate i.e., 10 Gbit/sec. Also, a tremendous amount of raw bandwidth is available i.e., around 250 GHz, which is an attractive characteristic of the mmWave band to relieve mobile data traffic congestion in the low frequency band. It has a high frequency i.e., 30 – 300 GHz, giving very high speed. It has a very short wavelength i.e., 1-10mm, because of this it provides the compact size of the component. It will provide a throughput of up to 20 Gbps. It has narrow beams and will increase security and reduce interference. When the main beam of the transmitter and receiver are not aligned properly there is a problem in ideal communication. To solve this problem beam management is one of the solutions to form a strong communication link between transmitter and receiver. This paper aims to address challenges in beam management and proposes a framework for realization. Towards the same, the paper initially introduces various challenges in beam management. Towards building an effective beam management system when a user is moving, various steps are present like beam selection, beam tracking, beam alignment, and beam forming. Hence the subsequent sections of the paper illustrate various beam management procedures in mmWave using conventional methods as well as using deep learning techniques. The paper also presents a case study on the framework's implementation using the above-mentioned techniques in mmWave communication. Also glimpses on future research directions are detailed in the final sections. Such beam management techniques when used for mmWave technology will enable build fast, efficient, and capable 5G networks
Integrated Data and Energy Communication Network: A Comprehensive Survey
OAPA In order to satisfy the power thirsty of communication devices in the imminent 5G era, wireless charging techniques have attracted much attention both from the academic and industrial communities. Although the inductive coupling and magnetic resonance based charging techniques are indeed capable of supplying energy in a wireless manner, they tend to restrict the freedom of movement. By contrast, RF signals are capable of supplying energy over distances, which are gradually inclining closer to our ultimate goal – charging anytime and anywhere. Furthermore, transmitters capable of emitting RF signals have been widely deployed, such as TV towers, cellular base stations and Wi-Fi access points. This communication infrastructure may indeed be employed also for wireless energy transfer (WET). Therefore, no extra investment in dedicated WET infrastructure is required. However, allowing RF signal based WET may impair the wireless information transfer (WIT) operating in the same spectrum. Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer (SWIPT), which evolves to Integrated Data and Energy communication Networks (IDENs). To this end, a ubiquitous IDEN architecture is introduced by summarising its natural heterogeneity and by synthesising a diverse range of integrated WET and WIT scenarios. Then the inherent relationship between WET and WIT is revealed from an information theoretical perspective, which is followed by the critical appraisal of the hardware enabling techniques extracting energy from RF signals. Furthermore, the transceiver design, resource allocation and user scheduling as well as networking aspects are elaborated on. In a nutshell, this treatise can be used as a handbook for researchers and engineers, who are interested in enriching their knowledge base of IDENs and in putting this vision into practice
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