194 research outputs found
MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity
In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts
Intelligent Processing in Wireless Communications Using Particle Swarm Based Methods
There are a lot of optimization needs in the research and design of wireless communica- tion systems. Many of these optimization problems are Nondeterministic Polynomial (NP) hard problems and could not be solved well. Many of other non-NP-hard optimization problems are combinatorial and do not have satisfying solutions either. This dissertation presents a series of Particle Swarm Optimization (PSO) based search and optimization algorithms that solve open research and design problems in wireless communications. These problems are either avoided or solved approximately before.
PSO is a bottom-up approach for optimization problems. It imposes no conditions on the underlying problem. Its simple formulation makes it easy to implement, apply, extend and hybridize. The algorithm uses simple operators like adders, and multipliers to travel through the search space and the process requires just five simple steps. PSO is also easy to control because it has limited number of parameters and is less sensitive to parameters than other swarm intelligence algorithms. It is not dependent on initial points and converges very fast.
Four types of PSO based approaches are proposed targeting four different kinds of problems in wireless communications. First, we use binary PSO and continuous PSO together to find optimal compositions of Gaussian derivative pulses to form several UWB pulses that not only comply with the FCC spectrum mask, but also best exploit the avail- able spectrum and power. Second, three different PSO based algorithms are developed to solve the NLOS/LOS channel differentiation, NLOS range error mitigation and multilateration problems respectively. Third, a PSO based search method is proposed to find optimal orthogonal code sets to reduce the inter carrier interference effects in an frequency redundant OFDM system. Fourth, a PSO based phase optimization technique is proposed in reducing the PAPR of an frequency redundant OFDM system. The PSO based approaches are compared with other canonical solutions for these communication problems and showed superior performance in many aspects. which are confirmed by analysis and simulation results provided respectively. Open questions and future
Open questions and future works for the dissertation are proposed to serve as a guide for the future research efforts
Bio-inspired optimization algorithms for smart antennas
This thesis studies the effectiveness of bio-inspired optimization algorithms in
controlling adaptive antenna arrays. Smart antennas are able to automatically
extract the desired signal from interferer signals and external noise. The angular
pattern depends on the number of antenna elements, their geometrical arrangement,
and their relative amplitude and phases. In the present work different
antenna geometries are tested and compared when their array weights are optimized
by different techniques. First, the Genetic Algorithm and Particle Swarm
Optimization algorithms are used to find the best set of phases between antenna
elements to obtain a desired antenna pattern. This pattern must meet several
restraints, for example: Maximizing the power of the main lobe at a desired direction
while keeping nulls towards interferers. A series of experiments show that
the PSO achieves better and more consistent radiation patterns than the GA in
terms of the total area of the antenna pattern. A second set of experiments use
the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization
algorithms to find the array weights that configure a rectangular array. The results
suggest an advantage in performance by reducing the number of iterations
taken by the PSO, thus lowering the computational cost. During the development
of this thesis, it was found that the initial states and particular parameters of
the optimization algorithms affected their overall outcome. The third part of this
work deals with the meta-optimization of these parameters to achieve the best
results independently from particular initial parameters. Four algorithms were
studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal
Sampling and Pattern Search performed better to set the initial parameters
and obtain the best performance of the bio-inspired methods studied
Multi-User Ultra-Massive MIMO for very high frequency bands (mmWave and THz): a resource allocation problem
A dynamic subarray allocation for multi-user massive MIMO systems working in very high frequency bands (mmWave and THz) is proposed as a promising technique to unleash the capacity limits in future cellular networks capable of supporting high consuming bandwidth applications
Low-complexity antenna selection techniques for massive MIMO systems
PhD ThesisMassive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology
in wireless communications, where hundreds of antennas are exploited at the base
station (BS) to serve a much smaller number of users. Employing large antenna
arrays can improve the performance dramatically in terms of the achievable rates
and radiated energy, however, it comes at the price of increased cost, complexity,
and power consumption.
To reduce the hardware complexity and cost, while maintaining the advantages of
M-MIMO, antenna selection (AS) techniques can be applied where only a subset of
the available antennas at the BS are selected. Optimal AS can be obtained through
exhaustive search, which is suitable for conventional MIMO systems, but is prohibited
for systems with hundreds of antennas due to its enormous computational
complexity. Therefore, this thesis address the problem of designing low complexity
AS algorithms for multi-user (MU) M-MIMO systems.
In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired,
and heuristic methods are applied for AS in uplink MU M-MIMO systems.
It was demonstrated that quantum-inspired and heuristic methods outperform
the bio-inspired techniques in terms of both complexity and performance.
In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter
(MF) precoding. Two novel AS algorithms are proposed where the antennas are
selected without any vector multiplications, which resulted in a dramatic complexity
reduction. The proposed algorithms outperform the case where all antennas are
activated, in terms of both energy and spectral efficiencies.
In chapter 5, three AS algorithms are designed and utilized to enhance the performance
of cell-edge users, alongside Max-Min power allocation control. The
algorithms aim to either maximize the channel gain, or minimize the interference
for the worst-case user only.
The proposed methods in this thesis are compared with other low complexity AS
schemes and showed a great performance-complexity trade-off
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Development of Novel Independent Component Analysis Techniques and their Applications
Real world problems very often provide minimum information regarding their causes. This is mainly due to the system complexities and noninvasive techniques employed by scientists and engineers to study such systems. Signal and image processing techniques used for analyzing such systems essentially tend to be blind. Earlier, training signal based techniques were used extensively for such analyses. But many times either these training signals are not practicable to be availed by the analyzer or become burden on the system itself. Hence blind signal/image processing techniques are becoming predominant in modern real time systems. In fact, blind signal processing has become a very important topic of research and development in many areas, especially biomedical engineering, medical imaging, speech enhancement, remote sensing, communication systems, exploration seismology, geophysics, econometrics, data mining, sensor networks etc. Blind Signal Processing has three major areas: Blind Signal Separation and Extraction, Independent Component Analysis (ICA) and Multichannel Blind Deconvolution and Equalization. ICA technique has also been typically applied to the other two areas mentioned above. Hence ICA research with its wide range of applications is quite interesting and has been taken up as the central domain of the present work
Waveform Design for 5G and beyond Systems
5G traffic has very diverse requirements with respect to data rate, delay, and reliability. The concept of using multiple OFDM numerologies adopted in the 5G NR standard will likely meet these multiple requirements to some extent. However, the traffic is radically accruing different characteristics and requirements when compared with the initial stage of 5G, which focused mainly on high-speed multimedia data applications. For instance, applications such as vehicular communications and robotics control require a highly reliable and ultra-low delay. In addition, various emerging M2M applications have sparse traffic with a small amount of data to be delivered. The state-of-the-art OFDM technique has some limitations when addressing the aforementioned requirements at the same time. Meanwhile, numerous waveform alternatives, such as FBMC, GFDM, and UFMC, have been explored. They also have their own pros and cons due to their intrinsic waveform properties. Hence, it is the opportune moment to come up with modification/variations/combinations to the aforementioned techniques or a new waveform design for 5G systems and beyond. The aim of this Special Issue is to provide the latest research and advances in the field of waveform design for 5G systems and beyond
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