4,563 research outputs found
Error models for digital channels and applications to wireless communication systems
Digital wireless channels are extremely prone to errors that appear in bursts or clusters.
Error models characterise the statistical behaviour of bursty profiles derived from
digital wireless channels. Generative error models also utilise those bursty profiles in
order to create alternatives, which are more efficient for experimental purposes. Error
models have a tremendous value for wireless systems. They are useful for the design
and performance evaluation of error control schemes, in addition to higher layer protocols
in which the statistical properties of the bursty profiles are greatly functional.
Furthermore, underlying wireless digital channels can be substituted by generated
error profiles. Consequently, computational load and simulation time can be significantly
reduced when executing experiments and performing evaluation simulations
for higher layer communications protocols and error control strategies.
The burst error statistics are the characterisation metrics of error models. These
statistics include: error-free run distribution; error-free burst distribution; error burst
distribution; error cluster distribution; gap distribution; block error probability distribution;
block burst probability distribution; bit error correlation function; normalised
covariance function; gap correlation function; and multigap distribution. These burst
error statistics scrutinise the error models and differentiate between them, with regards
to accuracy. Moreover, some of them are advantageous for the design of digital
components in wireless communication systems.
This PhD thesis aims to develop accurate and efficient error models and to find applications
for them. A thorough investigation has been conducted on the burst error
statistics. A breakdown of this thesis is presented as follows.
Firstly, an understanding of the different types of generative error models, namely,
Markovian based generative models, context-free grammars based generative models,
chaotic models, and deterministic process based generative models, has been presented.
The most widely used models amongst the generative models have been
compared with each other consulting the majority of burst error statistics. In order
to study generative error models, error burst profiles were obtained mainly from the
Enhanced General Packet Radio Service (EGPRS) system and also the Long Term
Evolution (LTE) system.
Secondly, more accurate and efficient generative error models have been proposed.
Double embedded processes based hidden Markov model and three-layered processes
based hidden Markov model have been developed. The two types of error profiles,
particularly the bit-level and packet-level error profiles were considered.
Thirdly, the deterministic process based generative models’ parameters have been
tuned or modified in order to generate packet error sequences rather than only bit
error sequences. Moreover, a modification procedure has been introduced to the same
models to enhance their generation process and to make them more desirable.
Fourthly, adaptive generative error models have been built in order to accommodate
widely used generative error models to different digital wireless channels with different
channel conditions. Only a few reference error profiles have been required in order to
produce additional error profiles in various conditions that are beneficial for the design
and performance evaluation of error control schemes and higher layer protocols.
Finally, the impact of the Hybrid Automatic Repeat reQuest (HARQ) on the burst
error statistics of physical layer error profiles has been studied. Moreover, a model that
can generate predicted error sequences with burst error statistics similar to those of
error profiles when HARQ is included has been proposed. This model is constructive
in predicting the behaviour of the HARQ in terms of a set of higher order statistics
rather than only predicting a first order statistic. Moreover, the whole physical layer
is replaced by adaptively generated error profiles in order to check the performance
of the HARQ protocol.
The developed generative error models as well as the developed adaptive generative
error models are expected to benefit future research towards the testing of many
digital components in the physical layer as well as the wireless protocols of the link
and transport layers for many existing and emerging systems in the field of wireless
communications
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
Modeling Poisson Error Process on Wireless Channels
Burst error modeling has seen extensive research and progress over several decades evolving into ever more complex modeling techniques used today. This paper analyzed usefulness of the most prominent generative and descriptive (analytical) methods. Data containing error bits and packets from real wireless transmission has been used to obtain statistical information about error burst and gap behavior in the channel and various generative and descriptive modeling techniques were applied to model the error process with the goal of establishing advantages and disadvantages of each technique. Generative methods were represented by the commonly implemented Elliot’s model with parameters calculated using a generalized algebraic form and descriptive methods were represented by one of the most flexible exponentially shaped distributions with regard to parameterization and heavy-tailed function modeling - gamma distribution, and lastly a technique represented by Markov modulated Poisson process (MMPP-2) producing second-order hyper-exponentially distributed characteristics. Results of the experiments were highly in favor of Elliot’s and MMPP-2 model demonstrating possible application of MMPP-2 model in application to commonly observed exponentially-shaped error process on the wireless channel
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
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