`Kee, UNIVERSITAT
`‘ lib POLITECNICA
`
`tes”
`DE VALENCIA
`
`°
`
`DEPARTAMENTODE
`COMUNICACIONES
`
`Non-Uniform Constellations
`for Next-Generation Digital
`Terrestrial Broadcast Systems
`
`Departamento de Comunicaciones
`
`Universitat Politécnica de Valencia
`
`A thesis for the degree of
`
`PhD in Telecommunications Engineering
`
`Valencia, June 2017
`
`Manuel Fuentes Muela
`
`Author:
`
`Supervisors:
`Dr. David Gémez Barquero
`Prof. Narcis Cardona Marcet
`
`LGE 1030
`LG Electronics, Inc. v. Constellation Designs, LLC
`IPR2023-00319
`
`LGE 1030
`LG Electronics, Inc. v. Constellation Designs, LLC
`IPR2023-00319
`
`1
`
`

`

`2
`
`

`

`Abstract
`
`Nowadays, the digital terrestrial television (DTT) market is characterized by
`the high capacity needed for high definition TV services, and the limited spec-
`trum available. There is a need for an efficient use of the broadcast spec-
`trum, which requires new technologies to guarantee increased capacities. Non-
`Uniform Constellations (NUC)arise as one of the most innovative techniques
`to approach those requirements. These constellations have been implemented
`in next-generation broadcast systems such as DVB-NGH(Digital Video Broad-
`casting - Next Generation Handheld) or ATSC 3.0 (Advanced Television Sys-
`tems Committee - Third Generation). NUCs reduce the gap between uniform
`Gray-labelled Quadrature Amplitude Modulation (QAM) constellations and
`the theoretical unconstrained Shannon limit. With these constellations, sym-
`bols are optimized in both in-phase (I) and quadrature (Q) components by
`means ofsignal geometrical shaping, considering a certain signal-to-noise ratio
`(SNR) and channel model.
`There are two types of NUC, depending on the numberof real-valued di-
`mensions considered in the optimization process, i.e. one-dimensional and two
`dimensional NUCs (1D-NUC and 2D-NUC,respectively). 1D-NUCs maintain
`the squared shape from QAM, but relaxing the distribution between constella-
`tion symbols in a single component, with non-uniform distance between them.
`These constellations provide better SNR performance than QAM,without any
`demapping complexity increase. 2D-NUCsalso relax the square shape con-
`straint, allowing to optimize the symbol positions in both dimensions,
`thus
`achieving higher capacity gains and lower SNR requirements. However, the
`use of 2D-NUCsimplies a higher demapping complexity, since a 2D-demapper
`is needed, i.e. I and Q components cannot be separated.
`In this dissertation, NUCs are analyzed from both transmit and receive
`point of views, using either single-input single-output (SISO) or multiple-input
`multiple-output (MIMO) antenna configurations. In SISO transmissions, 1D-
`NUCsand 2D-NUCsare optimized for a wide range of SNRs, several channel
`models and different constellation orders, using the Nelder-Mead optimization
`
`

`

`ABSTRACT
`
`algorithm. The optimization of rotated 2D-NUCsis also investigated, including
`the rotation angle as an additional variable in the optimization. Even though
`the demapping complexity is not increased, the SNR gain of these constellations
`is not significant. The highest rotation gain is obtained for low-order conste-
`llations and high SNRs. However, with multi-RF techniques such as Channel
`Bonding (CB) or Time-Frequency Slicing (TFS), the SNR gain is drastically
`increased, since I and Q components are transmitted in different RF channels.
`In this thesis, multi-RF gains of NUCs with and without rotation are provided
`for some representative scenarios.
`At the receiver,
`two different implementation bottlenecks are explored.
`First, the demapping complexity of all considered constellations is analyzed.
`Afterwards, two complexity reduction algorithms for 2D-NUCs are proposed.
`Both algorithms drastically reduce the number of distances to compute the
`output log-likelihood ratios (LLR). Moreover, both are finally combined in a
`single demapper. Quantization of NUCsis also explored in this dissertation,
`since LLR values and I/Q components are modified when using these conste-
`llations, compared to traditional QAM constellations. A new algorithm that is
`based on the optimization of the quantizer levels for a particular constellation
`is proposed. The proposed algorithm reduces the number of quantization bits
`and can be also extrapolated to QAM.
`The use of NUCsin multi-antenna communications is also investigated. In
`this dissertation, parameters that affect the optimization process are evaluated,
`when using a 2 x 2 dual polarized MIMOsystem. It includes the optimization
`in one or two antennas, the use of power imbalance, the cross-polar discrim-
`ination (XPD) between receive antennas, the use of different optimum and
`sub-optimum demappers, equalization methods and different channel models.
`Assuming different values for the parameters evaluated, new Multi-Antenna
`Non-Uniform Constellations (MA-NUC)are obtained by means of a particu-
`larized re-optimization process, specific for MIMO. At the receiver, an extended
`demapping complexity analysis is performed, where it is shown that the use of
`2D-NUCs in MIMOextremely increases the demapping complexity. In multi-
`antenna systems, the optimum demapping complexity grows exponentially with
`the number of antennas and the constellation order. As an alternative, an ef-
`ficient solution for 2D-NUCs and MIMOsystems based on Soft-Fixed Sphere
`Decoding (SFSD) is proposed. The main drawback is that SFSD demappers do
`not work with 2D-NUCs,since they perform a Successive Interference Cancel-
`lation (SIC) step that needs to be performed in separated I and Q components.
`The proposed method quantifies the closest symbol using Voronoi regions and
`allows SFSD demappers to work.
`
`

`

`Resumen
`
`Hoy en dia, el mercadodela televisién digital terrestre (TDT) esta caracter-
`izado por la alta capacidad requerida para transmitir servicios de televisi6n
`de alta definicién y el espectro disponible, el cual se encuentra muy limitado.
`Es necesario por tanto un uso eficiente del espectro radioeléctrico, el cual re-
`quiere nuevas tecnologias para garantizar mayores capacidades. Las constela-
`ciones no-uniformes (NUC) emergen como unadelas técnicas mas innovadoras
`para abordar tales requerimientos. Estas constelaciones han sido adoptadas
`en sistemas de televisién de siguiente generacién tales como DVB-NGH (Dig-
`ital Video Broadcasting - Next Generation Handheld) o ATSC 3.0 (Advanced
`Television Systems Committee - Third Generation). Las NUC reducen el es-
`pacio existente entre las constelaciones uniformes QAMyel limite tedrico de
`Shannon. Con estas constelaciones, los simbolos se optimizan en ambas com-
`ponentes fase (I) y cuadratura (Q) mediante técnicas geométricas de modelado
`de la senal, considerando un nivel sefal a ruido (SNR) concreto y un modelo
`de canal especifico.
`Hay dos tipos de NUC, dependiendo del nimero de dimensionesreales con-
`sideradas en el proceso de optimizacion, es decir, NUCs unidimensionales y bidi-
`mensionales (1D-NUC y 2D-NUC, respectivamente). Las 1D-NUC mantienen
`la forma cuadrada de las QAM, pero permiten cambiar la distribucién entre
`los simbolos en una componente concreta, teniendo una distancia no uniforme
`entre ellos. Estas constelaciones proporcionan un mejor rendimiento SNR que
`QAM,sin ningtin incremento en la complejidad en el demapper. Las 2D-NUC
`también permiten cambiar la forma cuadrada de la constelacién, permitiendo
`optimizar los simbolos en ambas dimensiones y por tanto obteniendo mayores
`ganancias en capacidad y menores requerimientos en SNR. Sin embargo,el uso
`de 2D-NUCsimplica una mayor complejidad en el receptor, puesto que se nece-
`sita un demapper 2D, donde las componentes I y Q no pueden ser separadas.
`En esta tesis se analizan las NUC desde el punto de vista tanto de trans-
`misi6n como de recepcion, utilizando bien configuraciones con una antena
`(SISO) o con miltiples antenas (MIMO). En transmisiones SISO, se han op-
`
`

`

`RESUMEN
`
`timizado 1D-NUCs para un rango amplio de distintas SNR, distintos modelos
`de canal y varios 6rdenes de constelacién. También se ha investigado la op-
`timizaci6én de 2D-NUCsrotadas, donde el angulo de rotacion se incluye en la
`optimizacién como unavariable adicional. Aunque la complejidad no aumenta,
`la ganancia SNRdeestas constelaciones noessignificativa. La mayor ganancia
`por rotacién se obtiene para bajos é6rdenes de constelacién y altas SNR. Sin
`embargo, utilizando técnicas multi-RF como Channel Bonding (CB) o Time-
`Frequency Slicing (TFS), la ganancia aumenta drdsticamente puesto que las
`componentes I y Q se transmiten en distintos canales RF. En esta tesis, se
`han estudiado varias ganancias multi-RF representativas de las NUC, con o sin
`rotacion.
`En el receptor, se han identificado dos cuellos de botella diferentes en la
`implementacion. Primero, se ha analizado la complejidad en el receptor para
`todas las constelaciones consideradas y, posteriormente, se proponen dosalgo-
`ritmos para reducir la complejidad con 2D-NUCs. Ambos algoritmos reducen
`drasticamente el numero de distancias para computar los LLR en el demapper
`con 2D-NUCs. Ademas, los dos pueden combinarse en un tinico demapper.
`También se ha explorado la cuantizacién de estas constelaciones, ya que tanto
`los valores LLR como las componentes I/Q se ven modificados, comparando
`con constelaciones QAM tradicionales. Ademas, se ha propuesto un algoritmo
`que se basa en la optimizacién para diferentes niveles de cuantizacién, para una
`NUC concreta. El algoritmo propuesto reduce el numero de bits a utilizar y
`puedeser utilizado también con QAM.
`Igualmente, se ha investigado en detalle el uso de NUCs en MIMO. En
`esta tesis se han evaluado los distintos parametros que afectan al proceso de
`optimizacion cuandoseutilizan sistemas MIMO 2 x 2 dual polarizados. Se ha
`incluido la optimizacion en una sola o en dos antenas, el uso de un desbalance
`de potencia, factores de discriminacién entre antenas receptoras (XPD), el uso
`de distintos demappers 6ptimos y subdptimos, métodos de ecualizacion y dis-
`tintos canales. Asumiendo distintos valores, se han obtenido nuevas constela-
`ciones multi-antena (MA-NUC)gracias a un nuevo proceso de re-optimizacién
`especifico para MIMO.Enelreceptor, se ha extendidoel andlisis de compleji-
`dad en el demapper, la cual se incrementa enormemente con el uso de 2D-NUCs
`y sistemas MIMO. En concreto, la complejidad aumenta exponencialmente con
`el numero de antenas y el orden de constelacién. Como alternativa, se propone
`una solucién basada en el algoritmo Soft-Fixed Sphere Decoding (SFSD). El
`principal problema es que estos demappers no funcionan con 2D-NUCs, puesto
`que necesitan de un paso adicional en el que las componentes I y Q necesitan
`separarse. El método propuesto cuantifica el simbolo mas cercano utilizando
`las regiones de Voronoi, permitiendoel uso de este tipo de receptor.
`
`

`

`Resum
`
`Actualment, el mercat de la televisié digital terrestre (TDT) esta caracter-
`itzat per l’alta capacitat requerida per a transmetre servicis de televisio d’alta
`definicié i l’espectre disponible, el qual es troba molt limitat. Es necessari
`per tant un us eficient de l’espectre radioelectric, el qual requereix noves tec-
`nologies per a garantir majors capacitats i millors servicis. Les constel-lacions
`no-uniformes (NUC) emergeixen com una de les técniques més innovadores
`en els sistemes de televisid de segiient generacid per a abordar tals requeri-
`ments. Les NUC redueixen l’espai existent entre les constel-lacions uniformes
`QAMi el limit tedric de Shannon. Amb estes constel-lacions, els simbols
`s’optimitzen en ambddés components fase (I) i quadratura (Q) per mitja de
`tecniques geometriques de modelatge del senyal, considerant un nivell senyal a
`soroll (SNR) concret i un model de canal especific.
`Hi ha dos tipus de NUC, depenent del nombre de dimensions reals consid-
`eradesen el procés d’optimitzacid, és a dir, NUCs unidimensionals i bidimen-
`sionals (1D-NUC i 2D-NUC, respectivament). 1D-NUCs mantenen la forma
`quadrada de les QAM,pero permet canviar la distribucié entre els simbols en
`una component concreta, tenint una distancia no uniforme entre ells. Estes
`constel-lacions proporcionen un millor rendiment SNR que QAM, sense cap
`increment en la complexitat al demapper. 2D-NUC també canvien la forma
`quadradade la constel-lacié6, permetent optimitzar els simbols en ambdés di-
`mensions i per tant obtenint majors guanys en capacitat i menors requeriments
`en SNR. No obstant aixo, l’is de 2D-NUCs implica una major complexitat en
`el receptor, ja que es necessita un demapper 2D, on les components I i Q no
`poden ser separades.
`Enesta tesi s’analitzen les NUC des del punt de vista tant de transmissié
`com de recepcié, utilitzant bé configuracions amb una antena (SISO) o amb
`multiples antenes (MIMO). En transmissions SISO,s’han optimitzat 1D-NUCs,
`per a un rang ampli de distintes SNR, diversos models de canal i diferents ordes
`de constel-lacid. També s’ha investigat l’optimitzacid de 2D-NUCsrotades,
`on l’angle de rotacié s’inclou en l’optimitzacid com una variable addicional.
`
`

`

`RESUM
`
`Encara que la complexitat no augmenta, el guany SNR d’estes constel-lacions
`no és significativa. El major guany per rotacio s’obté per a baixos ordes de
`constel-lacié i altes SNR. No obstant aixo, utilitzant tecniques multi-RF com
`Channel Bonding (CB) o Time-Frequency Slicing (TFS) , el guany augmenta
`drasticament ja que les components I i Q es transmeten en distints canals RF.
`Enesta tesi, s’ha estudiat el guany multi-RF de les NUC, ambosense rotacio.
`En el receptor, s’han identificat dos colls de botella diferents en la imple-
`mentaci6. Primer, s’ha analitzat la complexitat en el receptor per a totes
`les constel-lacions considerades i, posteriorment, es proposen dos algoritmes
`per a reduir la complexitat amb 2D-NUCs. Ambdés algoritmes redueixen
`drasticament el nombre de distancies per a computar els LLR en el demap-
`per amb 2D-NUCs. A més, els dos poden combinar-se en un tinic demap-
`per. També s’ha explorat la quantitzacié d’estes constel-lacions, ja que tant
`els valors LLR com les components I/Q es veuen modificats, comparant amb
`constel-lacions QAM tradicionals. A més, s’ha proposat un algoritme que es
`basa en l’optimitzacio per a diferents nivells de quantitzaci6, per a una NUC
`concreta. L’algoritme proposat redueix el nombre de bits a utilitzar i pot ser
`utilitzat també amb QAM.
`Igualment, s’ha investigat en detall l’s de NUCs en MIMO.Enesta tesi
`s’han avaluat els distints parametres que afecten el procés d’optimitzacié quan
`s’utilitzen sistemes 2 x 2 MIMO dual polaritzats. S’ha inclos l’optimitzacié en
`una sola o en dos antenes, |’tis d’un desbalang de poténcia, factors de discrim-
`inacié entre antenes receptores (XPD) , l’tis de distints demappers optims i
`suboptims, métodes d’equalitzacié i distints canals. Assumint distints valors,
`s’han obtingut noves constel-lacions multi-antena (MA-NUC)gracies a un nou
`procés de re-optimitzacié especific per a MIMO. Enel receptor, s’ha modi-
`ficat l’analisi de complexitat al demapper, la qual s’incrementa enormement
`amb l’us de 2D-NUCsi sistemes MIMO.En concret, la complexitat augmenta
`exponencialment amb el nombre d’antenes i l’orde de constel-lacié. Com a
`alternativa, es proposa una solucié basada en l’algoritme Soft-Fixed Sphere
`Decoding (SFSD). El principal problema és que estos demappers no funcionen
`amb 2D-NUCs,ja que necessiten d’un pas addicional en qué les componentsI
`i Q necessiten separar-se. El metode proposat quantifica el simbol més proxim
`utilitzant les regions de Voronoi, permetent 1|’lis d’este tipus de receptor.
`
`

`

`Acknowledgements
`
`First of all, I would like to thank my two supervisors for their assistance and
`guidance. From the very beginning, Dr. David Gomez Barquero gave me the
`opportunity to be part of the Institute de Telecommunications and Multimedia
`Applications (TEAM)at the Universitat Politécnica de Valéncia (UPV). His
`advice and support during all these years has been fundamental to improve
`as an engineer and researcher. Prof. Narcis Cardona also received me with
`open arms and gave me the chance to pursuit my Ph.D. in his group. Thanks
`to both of them, I could present my work to many people around the world,
`enjoying the experience and achieving new skills.
`Very special thanks go to my colleagues of the Mobile Communications
`Group (MCG). During these three years,
`the people that had been part of
`this group have helped me to grow not only as a professional, but also as a
`person. Thank you to my friends Gerardo, Edu, Carlos Andreu, Carlos Barjau,
`Alejandro, Jordi Joan, Shitomi, Conchi, Tere, Josetxo, Carlos Herranz, Alicia,
`Sonia, Irene, Jorge, Sofia, Martina, José Luis, Sergio, Sandra and many others.
`I also want to thank my old colleagues David Vargas, Jaime and Jefferson for
`the good moments we spent together. I’m also indebted to Prof. Gerald Matz
`from the Technical University of Vienna, for inviting me to his research team.
`It has been one of the best experiences in mylife, and I will be eternally grateful
`to him. Very special thanks also go to Georg Pichler, who helped me every
`single day during my stay there.
`Y por supuesto, esta tesis va dedicada a toda mi familia. En especial a mis
`padres, por darme la oportunidad de venir a Valencia a estudiar y convertirme
`en lo que hoy en dia soy. A mi hermano, por los grandes momentos que hemos
`vividos juntos y los que nos quedan por vivir. Y a mi novia, por su grandisimo
`apoyo, amor incondicional e incontables consejos que me ha dado durante estos
`anos. Os quiero a todos.
`
`

`

`ACKNOWLEDGEMENTS
`
`10
`
`

`

`Table of contents
`
`1
`
`Introduction
`15
`1.1 Evolution and New Challenges of Digital Terrestrial Broadcasting 15
`1.2 Preliminaries ..... 2... 0.0.0... 000000022 eee 19
`1.3. Research Challenges on Non-Uniform Constellations ..... .
`22
`1.4 Objectives and Scope. ............-.2220200005 23
`1.5 State-of-the-Art... 2.0.0.0... 0.000.020 000000004
`25
`
`1.6 Thesis Outline and Contributions. ................
`
`1.7 List of Publications... 2... 2.2. ..020.2..0.2.2...000048.
`
`1.7.1 Publications and Activities Related to this Thesis ...
`
`1.7.2. Other Publications... ...............0.0.
`
`2 Background
`2.1 System Model Overview ............-.-.+2.22+000-
`2.1.1
` Multi-Antenna Considerations ..............
`2.1.2 BICM Components. .................2..
`2.2. BICM Capacity Limits. ......................
`2.2.1. The Unconstrained Shannon Limit ............
`2.2.2 Capacity Calculation for BICM............2..
`2.2.3. BICM Limits for Uniform QAM Constellations .....
`2.2.4 Extension to MIMO-BICM Systems ...........
`2.3 Single-Antenna Receivers
`...........-.2.-02.22+000-
`2.3.1. Demapping Algorithms ..................
`2.3.2
`Signal Quantization .................-.4.
`2.4 Multi-Antenna Receivers. ............-.-.2.22+000-
`2.4.1 ML and Max-Log Demappers...............
`2.4.2 ZF and MMSE Detectors .................
`2.4.3.
`Sphere Decoding Techniques ...............
`
`3l
`
`34
`
`34
`
`35
`
`37
`37
`39
`Al
`43
`43
`44
`45
`48
`49
`50
`53
`55
`56
`57
`58
`
`11
`
`

`

`TABLE OF CONTENTS
`
`3 Optimization and Performance Evaluation of Non-Uniform Cons-
`tellations
`61
`3.1 Non-Uniform Constellations Optimization ............
`61
`3.1.1. One-Dimensional Non-Uniform Constellations... ...
`62
`3.1.2. Two-Dimensional Non-Uniform Constellations .....
`70
`3.1.3. BICM Capacity Improvements ..............
`79
`3.2 Non-Uniform Rotated Constellations ...............
`77
`3.2.1. Optimization Before Rotation. ..............
`78
`3.2.2. Optimization with Additional Rotation .........
`80
`3.3. Application of NURC to Multi-RF Techniques .........
`82
`3.4 Performance Analysis Based on Physical Layer Simulations ..
`84
`3.4.1. Non-Uniform Constellations Gain... ........2..
`85
`3.4.22 Rotation Gain ...........2.2.. 0.2.22 00.
`87
`3.4.3. Non-Uniform Rotated Constellations with Multiple RF
`Channels 2... 2... 0... ee ee 87
`3.5 Conclusion
`.. 2... 2. ee 94
`
`97
`4 Low-Complexity Demapping and Quantization Algorithms
`98
`4.1 Demapping Complexity at the Receiver .............
`4.2 Low-Complexity Demapping Algorithm ............. 100
`4.2.1 Quadrant Search Reduction (QSR)............
`100
`4.2.2 Condensed Symbols Reduction (CSR) .......... 102
`4.2.3 Quadrant Condensed Search Reduction (QCSR) .... 104
`4.3 Performance Evaluation: Minimum Numberof Distances. .. .
`105
`4.3.1 Calculation of the Minimum Number of Distances ...
`105
`4.3.2
`Performance Loss with Alternative Channel Models
`..
`108
`4.3.3. QCSR with Non-Uniform Rotated Constellations .... 110
`44 Digital Quantization of LLR and I/Q Components ....... 111
`4.4.1.
`System Model and Considered Scenario ......... 112
`4.4.2 Quantization of Log-Likelihood Ratios .......... 115
`4.4.3 Quantization of [/Q Components.............
`118
`4.5 Quantization Loss vs. Time De-Interleaving Memory Trade-Off
`121
`4.5.1
`Performance Loss of LLR Quantization ......... 121
`4.5.2
`Performance Loss of I/Q Components Quantization ..
`124
`4.5.3 Time De-Interleaving Memory Requirements ...... 125
`4.6 Conclusion
`........2..2.02.2.0 0000002002004 127
`
`5 Non-Uniform Constellations for MIMO Communications
`129
`5.1 Multi-Antenna Non-Uniform Constellations ........... 130
`5.1.1 Preliminary Design... ........0.......0.0. 131
`5.1.2. Re-optimization without Power Imbalance ........ 138
`
`12
`
`

`

`TABLE OF CONTENTS
`
`5.1.3 Re-optimization with Power Imbalance. .........
`5.2. Performance Evaluation of Multi-Antenna Non-Uniform Cons-
`tellations 2...
`5.2.1 Transmission without Power Imbalance .........
`5.2.2 Transmission with Power Imbalance of 6dB.......
`5.3 Demapping Complexity Analysis .................
`5.4 Fixed Sphere Decoder for Two-Dimensional NUCs in MIMO
`5.4.1
`Successive Interference Cancellation ...........
`5.4.2 Voronoi Regions Selection Algorithm ...........
`5.4.3 Resolution vs. Performance Trade-Off ..........
`5.5 Conclusion .... 2... 020.0000 ee
`
`140
`
`146
`
`147
`
`153
`
`Conclusions and Future Work
`6.1 Concluding Remarks .................-..+.2045
`6.1.1 Non-Uniform Constellations Optimization ........
`6.1.2 Complexity Implications at the Receiver. ........
`155
`6.1.3 Multi-Antenna Optimization and Complexity Reduction 157
`6.2 Constellation and CR Recommendation .............
`158
`6.3 Future work... 2... 0. ee
`161
`6.3.1 High-Order Two-Dimensional Non-Uniform Constellations 161
`6.3.2 Further Optimization for MIMO Systems ........ 161
`6.3.3 The Future of Broadcasting: Constellations for 5G Com-
`munications... 2... ee ee 161
`
`154
`
`154
`
`163
`Physical Layer Simulator
`A.1 Transmitter Block Diagram .................... 163
`A.2 Receiver Block Diagram ...............-....2-45 168
`A.3 Channel Models... 2... ..0.020.0002020.0 000000000045 170
`
`175
`Optimization Algorithm
`175
`B.1 Nelder-Mead Simplex Method...................
`B.2 Application to Use Cases Considered ..............- 179
`
`Acronyms
`
`References
`
`183
`
`187
`
`13
`
`

`

`TABLE OF CONTENTS
`
`14
`
`

`

`Chapter 1
`
`Introduction
`
`1.1 Evolution and New Challenges of Digital
`Terrestrial Broadcasting
`
`Television (TV) is one of the most popular and extended telecommunication
`systems in the world. Commercial TV as it is known today began in the late
`1940s. Its implementation introduced dramatic social changes and facilitated
`the appearance of new business models. TV has coexisted with society during
`more than 70 years, experiencing big transformations such as the transition
`from black and white to color, or from analog to digital. With the arrival
`of flat-screen displays, Digital Terrestrial Television (DTT) communications
`and video compression systems, TV has experienced a high-speed and large
`evolution in the 21st century.
`The switch from analog to digital entailed several advantages such as the
`transmission of noise-free high-quality video and audio, a larger exploitation of
`the Radio Frequency (RF) spectrum, the delivery of multilingual audio tracks,
`subtitles and interactivity, or the use of a flexible network with configurable pa-
`rameters such as transmission power, capacity or quality of service. Currently,
`DTTis the main TV system adopted in many European countries including the
`United Kingdom, France, Spain, Portugal and Italy, being ahead other services
`such as cable or satellite TV. DTT systems are capable of providing a specific
`set of services without any restriction in the numberof users [1]. DTT allows
`for an efficient delivery of free-to-air content to large audiences with a guar-
`anteed quality of service, and provides a near universal coverage of over 98 %
`population [2]. With DTT, the Ultra-High Frequency (UHF) spectrum needed
`to transmit a single analog channel is used to carry several multiplexed digital
`
`15
`
`

`

`CHAPTER 1. INTRODUCTION
`
`services. In other words, the same set of services can be transmitted using just
`a reduced part of the spectrum available. As a consequence, the spectral effi-
`ciency increase offered by DT'T systems attracted emerging technologies such
`as Long Term Evolution (LTE) to request part of the UHF spectrum.
`
`First and Second Digital Dividends
`
`In the World Radiocommunication Conference (WRC)-07, the International
`Telecommunications Union (ITU) decided to allocate the upper part of the TV
`broadcasting band to International Mobile Telecommunications (IMT) tech-
`nologies, giving room to which is knownas Digital Dividend (DD) [3]. Regions
`1 (Europe and Africa) and 3 (Asia) allocated the 800 MHz band (790-862
`MHz, channels 61-69) for fourth generation (4G) LTE services, and Region 2
`(America) allocated the 700 MHz band (698-806 MHz, channels 52-69).
`In
`the WRC-12, the ITU concluded with a decision to allocate additional UHF
`spectrum to mobile services. This situation will remain for more than 10 years,
`since in the WRC-15 it was decided that there will not be any change to the
`allocation in the 470-694 MHz bandfor the time being.
`The new mobile allocation, also known as Second Digital Dividend (DD2),
`is to be made in Region 1 in the 700 MHz band. The main difference compared
`to the 800 MHz bandlies in the fact that the Uplink (UL) is located in the
`lower part, instead of the Downlink (DL). For most countries, releasing the 700
`MHz band will require a new re-tune of existing DTT networks. Implementing
`the DD2 within ITU Region 1 affects up to eleven more DTTchannels (49-60),
`creating a numberof challenges. Since cellular terminals are closer to the DTT
`receivers than base stations, interference issues may be relevant in the 700 MHz
`band [4]. The DD2 is particularly problematic in countries where terrestrial
`TV is the main distribution platform.
`The DD2arises as a turning point for introducing new DTTsystems and
`video compression standards,in order to increase the network spectral efficiency
`and provide new services such as Ultra High-Definition TV (UHDTV).
`In
`fact, reference [5] presents an overview of the upcoming television broadcast
`spectrum incentive auction in the United States, reviews the potential plans
`for the 600 MHz band, and discusses the opportunities that could bring the
`use of new digital terrestrial television specifications.
`
`Initial DTT Technologies
`
`Nowadays,several first generation DTT technologies are in place over the world,
`such as Advanced Television Systems Committee (ATSC) in North Amer-
`ica and South Korea [6], Integrated Services Digital Broadcasting — Terres-
`trial (ISDB-T) in Japan and South America [7], or Digital Terrestrial Multime-
`
`16
`
`

`

`1.1 Evolution and New Challenges of Digital Terrestrial
`Broadcasting
`
`dia Broadcast (DTMB)in China[8]. Although these technologies are utilized in
`many countries, Digital Video Broadcasting - Terrestrial (DVB-T) is the most
`widely implemented DTTstandard in the world. DVB-T permits to configure a
`numberof parameters in order to adapt the system to a particular network and
`transmission requirements. The DVB-T specification provides bit rates ranging
`from 4 to 30 Mbps [9]. DVB-T, together with ISDB-T and DTMBspecifica-
`tions, is based on the multi-carrier Orthogonal Frequency-Division Multiplex-
`ing (OFDM) modulation [10]. All data carriers are modulated using differ-
`ent uniform Quadrature Amplitude Modulation (QAM)constellations, thatis,
`QPSK, 16QAM or 64QAM. DVB-Tpermits to use several Coding Rates (CR),
`Guard Intervals (GI) or Fast Fourier Transform (FFT) sizes to adapt the sig-
`nal. However,first generation standardsarestill far from the theoretical Shan-
`non capacity limit [11]. Motivated by technological progress and new advanced
`techniques, different standardization forums decided to develop next-generation
`DTT specifications.
`
`Next-Generation Digital Terrestrial Broadcasting
`
`The DVB forum developed a second generation standard, known as DVB -
`Terrestrial Second Generation (DVB-T2) [12], which provides a 50%increase
`of spectral efficiency compared to DVB-T. It permits to use a more advanced
`configuration of parameters to transmit, including a wider set of coding rates.
`DVB-T2 employs a serial concatenation of inner Low Density Parity Check
`(LDPC) codes and outer Bose Chadhuri Hocquenghem (BCH)codes. It is also
`based on the multi-carrier OFDM modulation, and permits the use of a single
`or multiple Physical Layer Pipes (PLP) that allow to transmit different services
`with specific capacity and robustness. The DVB-T2 specification provides an
`extended interleaving that increases robustness in both time and frequency
`domains.
`It also supports the concept of Rotated Constellations (RC) and
`includes an additional 256QAMconstellation.
`Standardization activities were also addressed on the development of mo-
`bile broadcasting systems, despite the lack of market and financing needed
`[13]. The handheld evolution of DVB-T2, Digital Video Broadcasting - Next
`Generation Handheld (DVB-NGH), is the state-of-the-art standard for DTT
`mobile communications, and includes some of the most advanced transmis-
`sion techniques to cope with adversities and characteristics of mobile chan-
`nels [14]. It was the first broadcasting system including the concept of one-
`dimensional Non-Uniform Constellation (NUC), for 64 and 256 orders. An-
`other relevant technique included in DVB-NGHwasthe use of Multiple-Input
`Multiple-Output (MIMO). The concept of MIMO is based on the use of sev-
`eral transmit and receive antennas to transmit different signals at the same
`
`17
`
`

`

`CHAPTER 1. INTRODUCTION
`
`
`
`
`
`
`
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`
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`
`2&
`
`Figure 1.1: Spectral efficiency of DVB-T, DVB-T2 and ATSC 3.0 specifications compared to
`the Shannon capacity limit, for AWGN channel.
`
`SNR (dB)
`
`time. The transmission of two or more streams in parallel permits to increase
`transmission capacity, but also robustness.
`The use of new digital standards along with moreefficient video coding
`arises as an opportunity to introduce two new features simultaneously, guar-
`anteeing an efficient use of the remaining spectrum. The state-of-the-art ter-
`restrial broadcasting standard, ATSC - Third Generation (ATSC 3.0), tries to
`solve this problem. It focuses on shortening the gap to Shannon limit through
`more efficient constellations and very-low coding rates, the aggregation of mul-
`tiple RF channels in which is known as Channel Bonding (CB), or the combined
`provision of fixed and mobile services, among others [15]. Fig. 1.1 shows the
`performanceachieved with ATSC 3.0 in terms of spectralefficiency (bit /s/Hz)
`vs. SNR (dB), for AWGN channel. ATSC 3.0 is also compared with someofits
`antecessors, i.e. DVB-T and DVB-T2. ATSC 3.0 includes some of the newest
`techniques developed in broadcasting such as MIMOor Layered Division Mul-
`tiplexing (LDM)[16]. LDM enables the efficient provision of mobile and fixed
`services by superposing two independent signals with different power levels in
`a single RF channel. With ATSC 3.0, it is also possible to split service data
`across two RF channels, so that peak data rate can be doubled. ATSC 3.0 also
`includes two-dimensional (2D) NUCsfrom 16 to 256 constellation symbols, and
`1D-NUCfor new high-orders such as 1024NUC (or LkKNUC) and 4096NUC(or
`AkNUC) [17].
`
`18
`
`

`

`1.2 Preliminaries
`
`1.2 Preliminaries
`
`The problem of designing a system that operates close to the unconstrained
`Shannon theoretical limit has been one of the most important and challenging
`problemsin information/communication theory [11]. As reference [18] states,
`one straightforward answer to the question of how to efficiently transmit more
`than one bit per symbol is a Coded Modulation (CM) scheme, where the chan-
`nel encoder is combined with a modulator and several bits are mapped into a
`symbol. What is not straightforward is how to configure a system that oper-
`ates close to the Shannon capacity limit, but with low complexity. In [19], the
`idea of jointly design the channel encoder and modulator was firstly proposed,
`which inspired several CM schemes suc