2,696 research outputs found
Constellation Mapping for Physical-Layer Network Coding with M-QAM Modulation
The denoise-and-forward (DNF) method of physical-layer network coding (PNC)
is a promising approach for wireless relaying networks. In this paper, we
consider DNF-based PNC with M-ary quadrature amplitude modulation (M-QAM) and
propose a mapping scheme that maps the superposed M-QAM signal to coded
symbols. The mapping scheme supports both square and non-square M-QAM
modulations, with various original constellation mappings (e.g. binary-coded or
Gray-coded). Subsequently, we evaluate the symbol error rate and bit error rate
(BER) of M-QAM modulated PNC that uses the proposed mapping scheme. Afterwards,
as an application, a rate adaptation scheme for the DNF method of PNC is
proposed. Simulation results show that the rate-adaptive PNC is advantageous in
various scenarios.Comment: Final version at IEEE GLOBECOM 201
Secure optical layer flexibility in 5G networks
We propose an adaptive resource allocation framework for on-demand communications in a software-defined mobile fronthaul (MFH) network that supports dynamic processing resource sharing. Our theoretical and experimental studies point to the feasibility of secure bidirectional transmission with guaranteed bit error rate (BER) service using adaptive modulation and coding.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation
This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined.
The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Constellation design for future communication systems: a comprehensive survey
[EN] The choice of modulation schemes is a fundamental building block of wireless communication
systems. As a key component of physical layer design, they critically impact the expected communication
capacity and wireless signal robustness. Their design is also critical for the successful roll-out of wireless
standards that require a compromise between performance, efficiency, latency, and hardware requirements.
This paper presents a survey of constellation design strategies and associated outcomes for wireless
communication systems. The survey discusses their performance and complexity to address the need for
some desirable properties, including consistency, channel capacity, system performance, required demapping
architecture, flexibility, and independence. Existing approaches for constellation designs are investigated
using appropriate metrics and categorized based on their theoretical algorithm design. Next, their application
to different communication standards is analyzed in context, aiming at distilling general guidelines applicable
to the wireless building block design. Finally, the survey provides a discussion on design directions for future
communication system standardization processes.This work was supported in part by the Basque Government under Grant IT1234-19, in part by the PREDOC under
Program PRE_2020_2_0105, and in part by the Spanish Government through the Project PHANTOM (MCIU/AEI/FEDER, UE) under Gran
Advanced constellation and demapper schemes for next generation digital terrestrial television broadcasting systems
206 p.Esta tesis presenta un nuevo tipo de constelaciones llamadas no uniformes. Estos esquemas presentan una eficacia de hasta 1,8 dB superior a las utilizadas en los últimos sistemas de comunicaciones de televisión digital terrestre y son extrapolables a cualquier otro sistema de comunicaciones (satélite, móvil, cable¿). Además, este trabajo contribuye al diseño de constelaciones con una nueva metodologÃa que reduce el tiempo de optimización de dÃas/horas (metodologÃas actuales) a horas/minutos con la misma eficiencia. Todas las constelaciones diseñadas se testean bajo una plataforma creada en esta tesis que simula el estándar de radiodifusión terrestre más avanzado hasta la fecha (ATSC 3.0) bajo condiciones reales de funcionamiento.Por otro lado, para disminuir la latencia de decodificación de estas constelaciones esta tesis propone dos técnicas de detección/demapeo. Una es para constelaciones no uniformes de dos dimensiones la cual disminuye hasta en un 99,7% la complejidad del demapeo sin empeorar el funcionamiento del sistema. La segunda técnica de detección se centra en las constelaciones no uniformes de una dimensión y presenta hasta un 87,5% de reducción de la complejidad del receptor sin pérdidas en el rendimiento.Por último, este trabajo expone un completo estado del arte sobre tipos de constelaciones, modelos de sistema, y diseño/demapeo de constelaciones. Este estudio es el primero realizado en este campo
- …