Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica, 2019
Bruno Alexandre
Amaro Santos
Plataforma de Simula¸
c˜
ao para Sistemas 5G
Simulation Platform for 5G Systems
Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica, 2019
Bruno Alexandre
Amaro Santos
Plataforma de Simula¸
c˜
ao para Sistemas 5G
Simulation Platform for 5G Systems
Disserta¸c˜ao apresentada `a Universidade de Aveiro para cumprimento dos re-quesitos necess´arios `a obten¸c˜ao do grau de Mestre em Engenharia Eletr´onica e Telecomunica¸c˜oes, realizada sob a orienta¸c˜ao cient´ıfica do Doutor Pedro Miguel da Silva Cabral, Professor Auxiliar do Departamento de Eletr´onica, Telecomunica¸c˜oes e Inform´atica da Universidade de Aveiro e sob a coori-enta¸c˜ao do Doutor Ad˜ao Paulo Soares da Silva, Professor Auxiliar do De-partamento de Eletr´onica, Telecomunica¸c˜oes e Inform´atica da Universidade de Aveiro.
O j´uri / The jury
Presidente / President Professor Doutor Ant´onio Lu´ıs Jesus Teixeira
Professor Associado com Agrega¸c˜ao da Universidade de Aveiro
Arguente Principal / Main Examiner
Professor Doutor Rui Miguel Henriques Dias Morgado Dinis
Professor Associado com Agrega¸c˜ao da Universidade Nova de Lisboa
Orientador / Advisor Professor Doutor Pedro Miguel da Silva Cabral
Agradecimentos / Acknowledgements
Em primeiro lugar quero agradecer `a minha fam´ılia por todo o apoio e carinho que me deram, especialmente aos meus pais e av´os que sempre me apoiaram em qualquer circunstˆancia. Agrade¸co tamb´em a todos os professores que me acompanharam durante o meu percurso acad´emico pelo conhecimento que me transmitiram, sobretudo ao meu orientador professor Pedro Cabral e coorientador professor Ad˜ao Silva pelos aconselhamentos na realiza¸c˜ao desta disserta¸c˜ao.
`
A Universidade de Aveiro e ao Instituto de Telecomunica¸c˜oes por garan-tirem todas as condi¸c˜oes e ferramentas de trabalho necess´arias, assim como a todos os membros destas institui¸c˜oes que me apoiaram, principalmente ao Diogo Barros e Joana Pereira pela ajuda e orienta¸c˜ao nos trabalhos realizados em laborat´orio.
Agrade¸co `a minha namorada e a cada um dos meus incr´ıveis amigos que levarei comigo no cora¸c˜ao por todas as experiencias vividas e pelo apoio incondicional demonstrado ao longo destes anos.
Palavras Chave Comunica¸c˜oes sem fios, 5G, OFDM, Amplificador de Potˆencia, R´ adio-frequˆencia
Resumo Gra¸cas aos avan¸cos tecnol´ogicos na ´area das comunica¸c˜oes, nunca foi t˜ao r´apido e simples comunicar com pessoas em qualquer parte do mundo, bem como usufruir de uma in´umera variedade de servi¸cos, apenas utilizando um telem´ovel com acesso `a internet. Mas o aumento do n´umero de utilizadores e funcionalidades exigem o desenvolvimento de sistemas de comunica¸c˜ao sem fios cada vez mais exigentes, com a necessidade constante de aumentar a velocidade e eficiˆencia na transferˆencia de informa¸c˜ao.
Atualmente est´a a surgir uma gera¸c˜ao de sistemas de comunica¸c˜ao sem fios que revolucionar´a a forma como partilharemos informa¸c˜ao, prometendo um aumento significativo nas taxas de transmiss˜ao de dados. Esta gera¸c˜ao apresenta-se como 5G.
De forma a otimizar o aproveitamento da largura de banda, os atuais sis-temas 4G j´a utilizam uma t´ecnica de modula¸c˜ao de sinal denominada de Orthogonal Frequency Division Multiplexing (OFDM) e os futuros sistemas 5G tamb´em far˜ao uso desta t´ecnica. Apesar de ter um ´optimo desempenho ao n´ıvel de aproveitamento espectral, a modula¸c˜ao OFDM apresenta eleva-dos valores na raz˜ao entre a potˆencia de pico e a potˆencia m´edia (PAPR) do sinal, o que pode levar os amplificadores de potˆencia a operar numa zona n˜ao linear. Comprometendo a eficiˆencia energ´etica do sistema e au-mentando a probabilidade de erro na transmiss˜ao de informa¸c˜ao.
Para avaliar o desempenho de sistemas de comunica¸c˜ao sem fios face aos efeitos provocadas pela amplifica¸c˜ao de sinais OFDM, foi desenvolvida uma plataforma de simula¸c˜ao. Nesta plataforma s˜ao incluidas medi¸c˜oes labora-toriais realizadas a dois tipos de amplificadores para diversos ambientes de funcionamento t´ıpicos dos sistemas 5G. De forma a entender como difer-entes caracter´ısticas de sistemas influenciam de forma diferente o seu de-sempenho, s˜ao testados sinais com duas larguras de banda distintas para trˆes t´ecnicas de modula¸c˜ao digital de sinal. Os testes realizados consideram tamb´em os efeitos do canal sem-fios assim como esquemas de sistemas com multiplas antenas.
Keywords Wireless Communication, 5G, OFDM, Power Amplifier, Radio Frequency
Abstract Thanks to the technological advances in communications, it has never been so fast and simple to communicate with people anywhere in the world, as well as enjoying a variety of services, only by using a mobile phone with internet access. But the increasing number of users and features requires an ever-demanding design of wireless communication systems, with the con-stant need to increase speed and efficiency in information transmission. A new generation of wireless communication systems that will revolutionize the way we share information is currently emerging, promising a significant increase in transmission rates. This generation presents itself as 5G. In order to optimize the bandwidth usage, current 4G systems already use a signal modulation technique called OFDM, and future 5G systems will also make use of this technique. Despite having optimum spectral performance, OFDM modulation has a high signal Peak-to-Average Power Ratio (PAPR), which can cause the power amplifiers to operate in a non-linear zone. This may compromise the electrical efficiency of the system and increase the probability of error in information transmission.
To evaluate the performance of wireless communication systems against the distortions caused by the amplification of OFDM signals, a simulation platform was developed. In this platform are included laboratory measure-ments for two types of amplifiers in different operating environmeasure-ments that are typical in 5G systems. In order to understand how different system characteristics differently affect its performance, signals with two distinct bandwidths are tested for three digital signal modulation techniques. The tests also consider the effects of the wireless channel as well as schemes of multi-antenna systems.
Contents
Contents i
List of Figures iii
List of Tables v
Acronyms vii
1 Introduction 1
1.1 Evolution of Mobile Communication Systems . . . 1
1.2 Upcoming 5G Technology . . . 3
1.3 Motivation . . . 5
1.4 Objectives . . . 6
1.5 Structure . . . 6
2 Wireless Communication Systems 7 2.1 Introduction . . . 7
2.2 Wireless Propagation Channel . . . 8
2.2.1 AWGN Channel . . . 9
2.2.2 Fading Channel . . . 10
2.2.3 MIMO Channel . . . 13
2.3 Multiple Antenna Systems . . . 14
2.3.1 Spatial Diversity . . . 14
3 Orthogonal Frequency-Division Multiplexing 19 3.1 OFDM Principles . . . 19
3.1.1 Multicarrier modulation and OFDM . . . 19
3.1.2 Cyclic Prefix . . . 23
3.1.3 Peak-to-Average Power Ratio in OFDM Signals . . . 23
3.2 OFDM System Model . . . 24
4 Radio Frequency Front-End 27 4.1 Mixers . . . 28
4.2 Low Noise Amplifiers . . . 29
4.3 Power Amplifier Characteristics and Non-linearity Effects . . . 29
4.3.1 Intermodulation Distortion . . . 31
4.3.2 AM-AM/AM-PM Distortion . . . 33
5 Simulation Platform 37
5.1 Introduction . . . 37
5.2 Radio Frequency Test Equipment and Laboratory Setup . . . 37
5.3 Digital Signal Processing . . . 40
5.3.1 SISO Simulation System . . . 40
5.3.2 MIMO Simulation System . . . 45
6 Test Results 47 6.1 Introduction . . . 47
6.2 Power Amplifier Modulated Signal Test . . . 48
6.3 Power Amplifier Effect in Signal Constellation . . . 58
6.4 SISO Simulation . . . 62
6.5 MIMO Simulation . . . 65
7 Conclusions 67 7.1 Discussion and Conclusions . . . 67
7.2 Future Work . . . 68
List of Figures
Figure 1.1 The trends of the mobile evolution [6]. . . 3
Figure 2.1 Block diagram of a wireless communication system. . . 7
Figure 2.2 The main mechanisms that impact signal propagation. . . 8
Figure 2.3 BER for different modulation schemes in a AWGN channel. . . 10
Figure 2.4 Large scale fading and small scale fading effects on signal. . . 11
Figure 2.5 Flat fading channel and frequency selective fading channel. . . 12
Figure 2.6 BER for different modulation schemes in Rayleigh and AWGN fading channel. . . 13
Figure 2.7 MIMO antenna scheme [14]. . . 14
Figure 2.8 Alamouti 2x1 scheme. . . 15
Figure 2.9 MIMO antenna scheme for Alamouti 2x2. . . 16
Figure 2.10 Alamouti 2x1 and 2x2 BER performance compared to a SISO system. 17 Figure 3.1 Comparison of SCM and MCM: frequency spectra of transmitted signals (a) and frequency spectra of received signals (b) [24]. . . 20
Figure 3.2 Savings in bandwidth: conventional multicarrier technique (a) and or-thogonal multicarrier modulation technique (b) [23]. . . 21
Figure 3.3 Results of the FFT and IFFT operation for different waveforms. . . . 21
Figure 3.4 Maintaining the orthogonality of the sub-carriers [20]. . . 22
Figure 3.5 OFDM signal with CP. . . 23
Figure 3.6 PAPR in OFDM signals. . . 24
Figure 3.7 OFDM transmitter and receiver. . . 25
Figure 4.1 Block diagram of a OFDM communication system [35]. . . 28
Figure 4.2 Ideal mixer symbol and its input and output signals plotted in time and frequency domain. . . 29
Figure 4.3 Current mode amplifier architecture. . . 30
Figure 4.4 Typical block scheme of a standard Doherty PA [40]. . . 31
Figure 4.5 Generation of intermodulation components in a two-tone test [39]. . . 32
Figure 4.6 RF PA non-linear gain [41]. . . 32
Figure 4.7 Non-linear model of the PA. . . 33
Figure 4.8 PAM-AM and AM-PM distortions. . . 34
Figure 4.9 Long term memory effect and short term memory effect [42]. . . 35
Figure 5.1 RF measuring equipment. . . 38
Figure 5.2 Complete setup used for measuring the PA. . . 39
Figure 5.3 Measurement system used in the laboratory. . . 39
Figure 5.5 Symbol constellations of different modulation schemes [21]. . . 42
Figure 5.6 Channel effect and added Noise. . . 44
Figure 5.7 Alamouti simulation flowchart. . . 46
Figure 6.1 Photos of the amplifiers tested in the measurement platform. . . 49
Figure 6.2 Simulated impact of the OFDM modulated signal in Class B PA for 1MHZ bandwidth. . . 50
Figure 6.3 Simulated impact of the OFDM modulated signal in Class B PA for 10MHZ bandwidth. . . 51
Figure 6.4 Measured impact of the OFDM modulated signal in Class B PA. . . . 52
Figure 6.5 Simulated impact of the OFDM modulated signal in Doherty PA for 1MHZ bandwidth. . . 54
Figure 6.6 Simulated impact of the OFDM modulated signal in Doherty PA for 10MHZ bandwidth. . . 55
Figure 6.7 Measured impact of the OFDM modulated signal in Doherty PA. . . . 56
Figure 6.8 Constellation of OFDM modulated signals amplified by the Class B PA. 59 Figure 6.9 Constellation of OFDM modulated signals amplified by the Doherty PA. 60 Figure 6.10 EVM [47]. . . 61
Figure 6.11 Constellation of OFDM modulated signals amplified by the Class B PA. 63 Figure 6.12 Constellation of OFDM modulated signals amplified by the Doherty PA. 64 Figure 6.13 BER curves for Alamouti schemes with QPSK modulated signal with 1M Hz bandwidth. . . 65
List of Tables
Table 2.1 Encoding and transmission sequence for the two-branch transmit
diver-sity scheme [34]. . . 15
Table 5.1 Signal space characteristics of QPSK. . . 41
Table 5.2 ETU delay profile. . . 44
Table 6.1 Simulated characteristics of the Class B PA. . . 53
Table 6.2 Measured characteristics of the Class B PA. . . 53
Table 6.3 Simulated characteristics of the Doherty PA. . . 57
Table 6.4 Measured characteristics of the Doherty PA. . . 57
Acronyms
3GPP 3rd Generation Partnership Project. ADC Analog to Digital Converter.
ADS Advance Design Systems.
AMPS Advanced Mobile Phone Service. AWGN Additive White Gaussian Noise. BER Bit Error Rate.
.
CDMA Code Division Multiple Access. CIR Channel Impulse Response.
CP Cyclic Prefix.
CSI Channel State Information. DC Direct Current.
DFT Discrete Fourier Transform. DL DownLink.
DPA Driver Power Amplifier. DPD Digital Pre-Distortion. DSP Digital Signal Processing. DUT Device Under Test.
eMBB Enhanced Mobile BroadBand. ETU Extended Typical Urban. EVM Error Vector Magnitude.
FDM Frequency Division Multiplexing. FDMA Frequency Division Multiple Access. FFT Fast Fourier Transform.
GPIB General Purpose Interface Bus. GSM Group Special Mobile.
ICI Inter Carrier Interference.
IDFT Inverse Discrete Fourier Transform. IF Intermediate Frequency.
IFFT Inverse Fast Fourier Transform. IMD Intermodulation Distortion. IoT Internet of Things.
ISI InterSymbol Interference.
ITU International Telecommunication Union. LNA Low Noise Amplifier.
LO Local Oscillator. LoS Line of Sight.
LTE Long-Term Evolution. MCM Multi Carrier Modulation.
MIMO Multiple-Input Multiple-Output. MISO Multiple-Input Single-Output.
mMTC Massive Machine-Type Communication. mmWave Millimeter Wave.
NAMTS Nippon Advanced Mobile Telephone Service. NMTS Nordic Mobile Telephone System.
NR New Radio.
PA Power Amplifier.
PAE Power Added Efficiency.
PAPR Peak-to-Average Power Ratio. PDC Personal Digital Cellular. PDF Probability Density Function. PSK Phase Shift Keying.
QAM Quadrature Amplitude Modulation. QPSK Quadrature Phase Shift Keying. RF Radio Frequency.
RMS Root Mean Square. S/P Serial-to-Parallel.
SCM Single Carrier Modulation. SISO Single-Input Single-Output. SMS Short Term Message.
SNR Signal-to-Noise power Ratio. STBC Space-Time Block Codes. STC Space-Time Code.
TDM Time Division Multiplexing. TDMA Time Division Multiple Access. UL UpLink.
UMTS Universal Mobile Telecommunications Service. URLLC Ultra-Reliable and Low-Latency Communication. VSA Virtual Signal Analyzer.
VSG Virtual Signal Generator. ZFC Zero-Force Combining.
Chapter 1
Introduction
Wireless communications were introduced in the 19th century with the appearance of analog radio systems. Since the emergence of this technology, progress has been made in terms of the variety of services, reliability of connection and transmission rate. Over the years, mobile communication systems evolved from an expensive technology for few individuals to systems used by most of the world population nowadays.
1.1
Evolution of Mobile Communication Systems
The first generation of wireless communication services emerged around the 80s and was implemented with analog cellular systems. Several analog radio systems were developed in different countries, and despite the various common features of these systems, each one was designed according to the environment and availability of Radio Frequency (RF) bands. These first generation systems allowed voice communication and were based on analog frequency modulation techniques, like Frequency Division Multiple Access (FDMA) [1][2].
In 1978 the Nippon Advanced Mobile Telephone Service (NAMTS), in Japan, becomes the first system to be operational. Three years later, the Nordic Mobile Telephone System (NMTS) also appears in Scandinavia and was specially characterized for its good radio cov-erage, making it suitable for rural areas. Only a couple of years later arises in the United States the Advanced Mobile Phone Service (AMPS), which had higher capacity than NMTS but offered a lower radio coverage range [1].
The 2G arises in the 90s with the motivation to obtain improved compatibility between re-gions, achieve enhanced voice quality and introduce Short Term Message (SMS) text messages. Unlike for the 1G, the second generation standardization committees have opted worldwide for digital systems, since digital transmission techniques operate better in high-interference environments and offer a higher capacity potential [2]. Other great benefits are the continu-ous performance enhancement, decreasing costs and decreasing power consumption in digital techniques.
Two channel access methods were selected for the 2G systems, the Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA). These two methodologies use the frequency spectrum more efficiently than the traditional dedicated fixed frequency trans-mitting systems [2]. TDMA is a multiplexing technology in which communication channels for each users are allocated by time slots on a carrier frequency. For the CDMA, several
trans-mitters can send information simultaneously over the same communication channel, since it does not assign a specific frequency or time slot for each user [3].
The 2G offered three different possible standards, the Group Special Mobile (GSM) for Europe and international applications, the IS-54 for North America and a third system for Japan, the Personal Digital Cellular (PDC). While IS-54 was implemented in order to in-corporate the AMPS from 1G, the European and Japanese systems were designed for new dedicated frequency bands and only partially overlapped the old bands [2].
Considering the limitations of radio spectrum available and the large increase in the num-ber of users, the focus of the 3G of mobile systems has been on the organization of the network frame and transmission designs to provide users with a flawless service, intended for voice, multimedia, text and internet access [2]. The 3rd Generation Partnership Project (3GPP) branded Universal Mobile Telecommunications Service (UMTS) and CDMA2000 as the main standards for the 3G. UMTS was first accessible in 2001 and used primarily in Europe, Japan and other regions that already had GSM infrastructures. One year later the CDMA2000 system is implemented and used specially in North America and South Korea [2]. With the emergence of more internet based services, the natural next step was to also move those services to the mobile devices. Therefore, the 4G technology is driven by the development of new and faster services for mobile devices. Long-Term Evolution (LTE), approved in 2008, is the marketed standard for the 4G, and is based on OFDM as the method for digital signal modulation [4].
Since the first LTE release, several improvements have been made and the major step was the LTE-Advanced, implemented in the LTE release 10 in 2011 [4]. LTE-Advanced defines a wider bandwidth through aggregation of multiple carries and progressed the use of advanced antenna techniques in both UpLink (UL) and DownLink (DL). The best way to increase capacity is to add more bandwidth and since it was important to keep compatibility with the older releases, the LTE-Advanced is provided through aggregation of the carriers from release 8 and 9 [5].
While the aggregation of carriers increase the system capacity, the use of multiple an-tennas techniques, are used in LTE-Advanced to improve the channel quality or enhance the transmission rate [5]. Figure 1.1 shows the trends of the mobile evolution over time. Recently, a significant increase in the number of LTE-Advanced systems has been seen, however, even more 5G users are expected in the forthcoming years. Additionally, it is also anticipated that GSM systems will become obsolete.
Figure 1.1: The trends of the mobile evolution [6].
1.2
Upcoming 5G Technology
Compared with the older generations, 4G has a much larger data bandwidth and a faster broadband internet, offering a lower latency service. But a new generation with even better characteristics began to be discussed around 2012, with the expectation to be commercially available around 2020, the 5G [7][8].
With all the ongoing evolution of LTE, it will be able to support a wide range of compe-tencies envisioned for the 5G. Although, there are expectations for the future generation that it cannot met. So, the need for more capable systems gave rise to the development of a new radio-access technology, known as New Radio (NR). The first version of the NR, 3GPP NR Release 15, was completed in June 2018 with the prospect of forward compatibility. Mean-ing that NR will be developed in such a manner that any future releases will be backwards compatible to its initial release(s) [8].
Mobile broadband will continue to be important and drive the need for higher system capacity, higher data rates and better coverage, but the 5G is much wider than that. Three distinctive classes of use cases are commonly referred in 5G:
• Enhanced Mobile BroadBand (eMBB): This use case category is the extension of the mobile broadband connectivity scenario of the current mobile telecommunication services, considering personal connectivity that includes access to multimedia content, services and data. The eMBB is achieved by providing the high data rates that are
required to support future multimedia services and the increasing volume of data gen-erated by these services [9].
• Massive Machine-Type Communication (mMTC): The growth of the Internet of Things (IoT) is reflected on the increasing number of wireless connected devices present in our daily lives, being expected that it will soon exceed the number of devices for sharing human-generated data. The focus of the mMTC is to provide connectivity to a massive number of systems, which are assumed to transmit sporadically a low amount of traffic [9].
• Ultra-Reliable and Low-Latency Communication (URLLC): The rigorous re-quirements on both latency and reliability are the distinctive features of this use case category, which targets mainly machine-type communications. The intended applica-tions for this use case include wireless control of industrial manufacturing and produc-tion processes, remote medical surgery, remotely driven vehicles and power distribuproduc-tion automation [9].
The transmitted waveforms defined for 5G NR are similar to those used in LTE, as they are based on OFDM modulation, although, 3GPP has decided that NR will support a much larger frequency range of operation, from below 1GHz up to 52.6GHz. The operation at this Millimeter Wave (mmWave) frequencies offer the possibility for a very large amount of spectrum and wider transmission bandwidths, enabling very high traffic capacity and data transmission rates. However, higher frequency values are also associated with higher radio channel attenuation, restraining the network coverage, which can be partly compensated by multiantenna transmission/reception techniques [12].
Multiantenna techniques are important in LTE, but in NR they have a more fundamental role in the system design. The extension of the spectrum, which also includes the mmWave frequencies, has led to a design of NR systems that achieve sufficient signal coverage by means of multiantenna schemes implementation. At high frequencies, the spectral efficiency does not require such a concern since there is plenty of spectrum available, instead, obtaining coverage becomes the main challenge as higher transmission losses may occur due to higher attenuations. Specially when considering outdoor-to-indoor scenarios, as the penetration loss goes up substantially with frequency. In mmWave systems, the transmission loss is mainly caused by the free space loss, since the effective antenna area is proportional to the wavelength of the signal and large bandwidths cause increased thermal noise power at the receivers [9]. However, additional environmental loss factors also have a considerable impact, such as atmospheric gases attenuation or precipitation attenuation [10].
The advances in active array antenna technologies have made possible to digitally control over a large number of antenna elements, referred to as massive Input Multiple-Output (MIMO). As the frequency is increased, more antenna elements can be accommodated by an antenna array with a certain dimension, since the individual antenna elements become smaller. As a result of this technological advances, also a new framework for acquiring the Channel State Information (CSI) has been developed in order to provide more efficient system designs capable to adapt to several use cases and introduce new features in future releases of NR [9].
1.3
Motivation
The wireless communications field has shown remarkable developments in the past two decades that have altered the way individuals interact and view the world. We live in an increasingly digitalized and interconnected society where the ability to share and receive in-formation worldwide becomes easy and immediate, becoming fewer and fewer the barriers to communication. The technological advances tend towards something much bigger than indi-viduals sharing messages over the internet, they have allowed interaction between indiindi-viduals and everyday devices or things. The IoT expands the idea of a digital society that connects the physical and the digital world, reflected on the increasing interactions between people and things by means of digital technology.
Communication systems must be able to exchange data at rates that keep up with the ever-increasing requirements for information sharing. Therefore, efficient RF signal modula-tions capable of transmitting large amounts of information while saving the electromagnetic spectrum are required. OFDM as been a robust multi-carrier modulation technique used to achieve high rate transmission, but it becomes a problem for communication hardware due to its high PAPR. In order to handle the sporadic high peaks in the signal, the Power Amplifiers (PAs) of the communicating equipment must operate at a power level significantly higher than the average power of the signal. As a consequence, their average efficiency decreases from the desirable levels.
The issue of the high PAPR in OFDM signals not only undermines the energy efficiency of the wireless communication systems, but also affects the quality of the link by placing the PA in a non-linear operating region, since it only has linear behaviors for low signal amplitudes. The effects of the non-linearity of the PA distort the amplified signal, impairing the ability to decode the correct information by the receiver. Although some of these effects can be reduced by applying certain design procedures, there are non-linear PA characteristics hard to predict, especially the long term memory effects.
It is of great interest to simulate the behavior of any system prior to its manufacture, providing a theoretical prediction of how the developed device will behave and allowing the correction, if necessary, of some aspects according to the result of its simulation. However, in the case of systems that include components with non-linear behaviors, simulations are not so accurate and may even mislead the system designer about the actual behavior of the developed system. To avoid inaccurate conclusions in the simulation process, it should be performed measurements to components that are difficult to predict mathematically, making the system simulation results more reliable.
1.4
Objectives
Electronic design automation softwares have made the life much easier for the RF system designers, because of the graphical environment that includes tools for every step of the design and specially for their ability to simulate the behavior of the circuit. Although simulating the performance of the circuit is quite practical, it should not be expected that the produced circuit shows the same behavior as in the simulation.
The objective of this dissertation is to create a platform to simulate the performance of complete 5G systems, from the transmitter to receiver while considering the wireless channel. Although most of the system is simulated, it is also integrated in the simulation platform measurements carried out in the laboratory. Since the non-linear characteristics of the PAs generate variations in the signal that cannot be predicted in simulation, laboratory measure-ments performed on real PAs are integrated in the platform to draw more realistic conclusions. The simulation platform developed in this dissertation follows the logic of the one developed in [49], the tested signals have the same characteristics and the channel effects are simulated following the same procedure. However, as stated above, the data obtained by laboratory measurements is also considered.
1.5
Structure
In this first chapter is provided information about the history of the technological evolution in the field of wireless communications services. Also in chapter 1, the motivations and objectives of the dissertation are explained so that the reader is aware of the main topics covered in the text and the importance of the work that was done.
To better understand how wireless communication systems process and share informa-tion, it is explained in chapter 2 the main building blocks of these systems. Multi-antenna techniques are also addressed.
The OFDM signal modulation used in the modern wireless systems is explained in chapter 3.
In chapter 4 are presented several circuits used in the transmitter and receiver front-ends, with a more detailed approach to the PAs.
Chapter 5 describes how the entire simulation platform for 5G systems was developed. Initially, the type of equipment needed to carry out the measurements in the laboratory is discussed, following an explanation of how the measuring system was set up. After clarifying how the measurements are performed, it is described how the collected data is used in the simulation, where the several algorithms used for handling this information are explained.
The results from the measurements are presented in chapter 6. In the first two section are observed some characteristics of the tested PAs and in the following sections, the results of the wireless system simulations are presented.
Finally, chapter 7 concludes with a discussion of the results and suggestions for future work.
Chapter 2
Wireless Communication Systems
2.1
Introduction
A simplistic representation of a wireless communication system is shown in figure 2.1, including its three main elements, a transmitter, a communication channel and a receiver.
Figure 2.1: Block diagram of a wireless communication system.
The communication begins on the transmitter baseband circuit, where the data to be transmitted, which is a binary code sequence, is generated and modulated using a suitable modulation technique so that the signal can be sent by the antenna(s). Then, the modulated signal is multiplexed with other signals using certain techniques, like Time Division Multiplex-ing (TDM) or Frequency Division MultiplexMultiplex-ing (FDM), to share the designated bandwidth before being up-coverted by the front-end circuit for transmission. After the transmission of the signal, it propagates over the channel, i.e. open space, which is very unpredictable and highly variable. The receiver then collects the corrupted signals with the antenna(s) and decodes the data through a set of blocks that perform the inverse tasks of the transmitter.
With regard to wireless communications, the information is transmitted in RF signals and in the case of the systems studied in this dissertation, the transmitted signals are in the microwave range. Because of their high frequency spectrum, microwaves can carry a large numbers of channels, furthermore, they also have short wavelengths, dictating the size of the antennas that can be used to transmit such waveforms [11].
This chapter discusses some types of channels that characterize wireless signal propaga-tion, as well as techniques that exploit spatial diversity provided by MIMO transmission and reception.
2.2
Wireless Propagation Channel
In mobile communications it is more likely that there is no guarantee of Line of Sight (LoS) between the communicating devices. Since communication systems are usually operated in areas surrounded by obstacles, as a signal travels through a wireless channel, it experiences multipath propagation due to random channel effects caused by the obstacles that it en-counters. The three main mechanisms that affect wireless signals by generating multipath propagation are depicted in figure 2.2 and can be described as:
• Reflection: Occurs when a transmitted electromagnetic wave bumps into a smooth sur-face with very large dimensions compared to the RF signal wavelength and bounces off. Additionally, there is normally some signal power loss as a result of the electromagnetic radiation absorption [13].
• Diffraction: Is the bending and spreading of waves around an obstacle, which oc-curs when the propagation path is obstructed by a dense body with large dimensions compared to the signal wavelength [13].
• Scattering: Occurs when an electromagnetic wave hits on either a large rough surface or any surface whose dimensions are on the order of the signal wavelength or less. This causes the signal energy to be spread out in all directions. In urban environments, typical signal obstructions that yield scattering include lampposts, street signs and vegetation [13].
Figure 2.2: The main mechanisms that impact signal propagation.
Because of these phenomena, the received signal results from the infinite sum of atten-uated, delayed and phase shifted replicas of the transmitted signal. The constructive and destructive combination of the transmitted signal replicas is usually addressed as multipath fading.
In this section are discussed three different types of channels. Primarily, the Additive White Gaussian Noise (AWGN) channel is presented, in which a transmitted signal is only affected by statistically independent Gaussian noise samples, and next several characteristics
2.2.1 AWGN Channel
A Single-Input Single-Output (SISO) wireless transmission channel not affected by multi-path fading, multi-path loss or shadowing, is considered an AWGN channel, whose only perturbation is the white Gaussian noise. The input/output relation in this channel is presented in expres-sion (2.1), where y(t) is the channel output and x(t) represents the channel input to which is added the noise n(t) [14].
y(t) = x(t) + n(t) (2.1)
The Signal-to-Noise power Ratio (SNR), given by (2.2), is commonly used to compare the level of a signal to the level of undesired noise. It is defined as the ratio of the received signal power, Pr, to the power of the noise that affects it, N . The noise presents a uniform
power spectral density of N0/2 and is considered in the total bandwidth of the signal, 2B,
generating a total noise power of N = N0
2 ∗ 2B = N0B.
The SNR is usually expressed in terms of the signal energy per bit, Eb, with Tbrepresenting
the bit time [14].
SN R = Pr N = Pr N0B = Eb N0BTb (2.2) For system performance evaluation it is essential to study the bit error probability, Pe,b.
In M-ary signals, like those used in the studies of this dissertation, the Pe,b is directly related
to the number of symbols in the signal constellation, since a greater number of M symbols implies that the points on the constellation are closer to each other. Expression (2.3) shows how Pe,b depends on the symbol error probability, Pe,s, and the M number of symbols of the
the signal constellation.
Pe,b≈
Pe,s
log2(M ) (2.3)
For a Quadrature Amplitude Modulation (QAM) modulation, the Pe,b given by (2.4)
is obtain from the statistical Q(x) function when considering the SNR per symbol, SN Rs.
Q(x) is defined as (2.5), which is the complement, with respect to unity, of the cumulative distribution function corresponding to the variable x. The variable x is a normalized Gaussian random variable with zero mean and unit variance [15].
Pe,b= Q( p SN Rs) (2.4) Q(x) = √1 2π Z ∞ x e−(t2/2)dt (2.5)
For a generic approach to M-ary signals with a square signal constellation, it is assumed the relation in expression (2.6) [16].
Pe,b≈ 4 log2(M ) 1 −√1 M Q s 3Eblog2(M ) (M − 1)N0 ! (2.6)
From expressions (2.6) it is concluded that, when transmitted in a AWGN channel, the error probability in M-QAM techniques decays exponentially with Eb/N0. The relation
be-tween the Bit Error Rate (BER) and Eb/N0 in a AWGN channel is shown in figure 2.3 for
the three modulation techniques tested in the context of this dissertation.
Figure 2.3: BER for different modulation schemes in a AWGN channel.
2.2.2 Fading Channel
As radio waves propagate through wireless channels, they experience effects like multipath or shadowing, causing some part of the sent signals to go through a constructive interference or to suffer from a destructive interference and be attenuated, even possibly extinguished. The wireless network should then be designed in a way to minimize the multipath fading effect, since the reflected delayed waves may produce InterSymbol Interference (ISI), causing a significant degradation of the network performance [17].
The fading effects on a wireless propagation channel can be classified into two main types, the large scale fading and small scale fading. The large scale fading describes the signal level after traveling over a large distance, in which the transmitted electromagnetic waves experience attenuations due to signal propagation and diffraction around large objects, such as hills, forests or buildings. In this case, as the received signal power fluctuates due to the obstructions in the path, the receiver is often said to be shadowed. On the other hand, the small scale fading is used to describe the received signal level after it comes across obstacles near the receiver, from a distance of several wavelengths to fractions of wavelengths. The worst case of small scale fading is when the LoS path is blocked and a large number of multiple reflective paths are considered. Under these circumstances the channel is called a Rayleigh channel. In such channel, the received signal is statistically described by a Rayleigh Probability Density Function (PDF) to simulate the smaller amplitude fluctuations [13]. Note figure 2.4 that shows the different variations of the transmitted signal power for both large and small scale fading.
Figure 2.4: Large scale fading and small scale fading effects on signal.
Channel fading effects can also be examined in terms of temporal variance, distinguishing between fast fading and slow fading. This distinction relates to the coherence time of the channel, Tc, which measures the period of time over which the channel impulse response is
considered to be not variant. This channel feature is also related to the channel Doppler spread, fd, by Tc ≈ f1d. The term fast fading is used to describe channels with Tc < Ts,
where Ts is the time duration of a transmitted symbol. This type of fading describes a
condition where the time duration in which the channel behaves in a correlated manner is short compared to the time duration of a symbol. Thus, several variations in the fading characteristics of the channel are expected over the symbol transmission time, causing the baseband pulse to be distorted, often resulting in an irreducible error rate [15][13].
A wireless communication channel is said to have slow fading if Tc> Ts. In this case the
time duration in which the channel behaves in a correlated manner is long compared to the time duration of a transmission symbol. Therefore, it can be expected that in a slow fading channel, the channel state remains unchanged during the symbol transmission time [13].
When regarding the characteristics of a fading channel, it is essential to understand its fre-quency selectivity. A channel can be considered to have a flat fading or a frefre-quency selective fading effect depending on the relation between its coherence bandwidth and the transmitted signal bandwidth. The coherence bandwidth of a fading channel, fc, is a statistical measure
of the range of frequencies over which the channel affects the spectral components of a trans-mitted signal with approximately equal gain and linear phase. Meaning that the spectral components of a signal in that range are affected in a similar manner. If the total spectrum of a transmitted signal is affected similarly by the channel, the fading is said to be frequency flat. This is the case for narrowband systems, in which the transmitted signal bandwidth, ∆f , is smaller than the coherence bandwidth of the channel, fc > ∆f . At the same time, if the
spectral components of the transmitted signal are affected by different amplitude gains and phase shifts, the fading is said to be frequency selective. This applies to wideband systems in which the bandwidth of the transmitted signal is bigger than the coherence bandwidth of the channel, fc < ∆f [15][13]. Figure 2.5 shows how a signal with a certain bandwidth, ∆f , is
this figure is noted that while the channel response along the signal bandwidth is practically constant for a flat fading channel, there is a significant variation in the channel response along the signal bandwidth on the frequency selective fading channel.
Figure 2.5: Flat fading channel and frequency selective fading channel.
In narrowband systems, the received carrier amplitude is modulated by a random variable α that represents the fading amplitude of the channel affecting the transmitted signal. This fading amplitude has a mean square value Ω = α2and PDF p
α(α) dependent on the nature of
the propagation environment. Since the instantaneous received signal power is modulated by α2, the instantaneous SNR per symbol is γ = α2E
s/N0, where Es represents the energy per
symbol. From this results an average SNR per symbol of γ = ΩEs/N0. The PDF of the SNR
per symbol, in (2.7), is then obtained by introducing a change of variables in the expression for the fading PDF of α [15].
pγ(γ) =
pα(pΩγ/γ)
2pγγ/Ω (2.7)
Depending on the nature of the radio propagation environment, there are different models describing the statistical behavior of the multipath fading envelope. As stated above, the Rayleigh distribution is frequently used to model multipath fading with no direct LoS path. In this channel model the fading amplitude α is distributed according to (2.8) [15].
pα(α) =
2α Ωe
−α2
Ω, α ≥ 0 (2.8)
Considering expression (2.7), the instantaneous SNR per symbol of the Rayleigh channel is distributed according to an exponential distribution given by (2.9) [15].
pγ(γ) =
1 e−
γ
As a result of the Rayleigh fading, each of the signaling schemes performance under AWGN interference, plotted in figure 2.3, now exhibit a BER performance that takes the form of inverse linear functions [13]. Figure 2.6 compares the BER performance between the AWGN channel and the Rayleigh channel.
Figure 2.6: BER for different modulation schemes in Rayleigh and AWGN fading channel.
2.2.3 MIMO Channel
A MIMO antenna system composed of T transmit and R receive antennas is illustrated in figure 2.7. In this scheme, as a transmitting antenna sends information, each of the receiving antennas will receive the sent signals affected by different path attenuation. This system is then considered to be affected by a hij impulse response between the jth(j = 1, 2, ..., T )
transmitting antenna and the ith(i = 1, 2, ..., R) receiving antenna, whereby the propagation channel can be represented by the matrix H in (2.10) [18].
The MIMO scheme is as a key 5G technology that can drastically enhance the performance of wireless networks. When using multiple antenna schemes, additional degrees of freedom in the spatial domain are introduced, greatly enhancing the multiplexing and diversity perfor-mance of such systems, allowing many users to be served more effectively without requiring more time and/or frequency resources. With abundance of antennas, the inherent effects of the fading channel can be attenuated, resulting in a more robust system [19].
H = h11 h12 . . . h1T h21 h22 . . . h2T .. . ... . .. ... hR1 hR2 . . . hRT (2.10)
Figure 2.7: MIMO antenna scheme [14].
2.3
Multiple Antenna Systems
In a flat fading wireless communication system equipped with multiple antennas, like the one in figure 2.7, the yi(i = 0, 1, . . . , R) received signals can be determined from the discrete
time model representation of a MIMO system in (2.11). Here xj(j = 0, . . . , T ) denotes the
transmitted signals by T transmitting antennas, nirepresents the Gaussian white noise vector
added in the R antennas and hij symbolize the R∗T channel gains applied on the independent
signaling paths [14]. y1 .. . yR = h11 . . . h1T .. . . .. ... hr1 . . . hRT x1 .. . xT + n1 .. . nR (2.11)
The communication systems can then benefit from the usage of multiple antennas in order to improve de performance through spatial diversity or to increase the data transmission rate through multiplexing.
2.3.1 Spatial Diversity
Diversity is a technique that provides wireless link improvement by transmitting multiple versions of the same signal over different channels, based on the fact that in each channel are experienced different levels of fading. Frequency diversity can be achieved if the same signal is transmitted on different carrier frequencies, separated with at least the coherence bandwidth of the channel, fc. In addition, a system can also make use of time diversity if
a signal is transmitted repeatedly with regularly time intervals, with a separation between the transmit times greater than the coherence time of the channel, Tc. For MIMO systems,
the existence of multiple antennas allows the usage of different propagation spatial paths as a mean for spatial diversity, without requiring the usage of more spectrum or transmission time to improve the wireless link. In spatial diversity, also called antenna diversity, channels with different characteristics are reached due to the distance between the emitting antennas [32].
signals are encoded across the spatial and time domain simultaneously. The Space-Time Block Codes (STBC) are the simplest types of STCs and the most well-known is the Alamouti code, projected by Siavash Alamouti in 1998 for a system with two transmit antennas and one receiver antenna [33].
Alamouti 2x1 Technique
The Alamouti 2x1 technique is applied to a Multiple-Input Single-Output (MISO) scheme with two transmission antennas, in which is assumed that the receiver has perfect knowledge of the CSI. On the transmitter side, two encoded symbols, s1 and s2, are sent following the
encoding map in table 2.1. In the first time instance t, the symbols s1 and s2are transmitted
by Antenna1 and Antenna2, respectively, while during the second time instance t + T , the negative conjugate of the second symbol, i.e. −s∗2, is sent from the first antenna and the conjugate of the first symbol, i.e. s∗1, is transmitted by the second antenna. The signals are then collected by the receiver that combines them to estimate the sent information [34].
Antenna1 Antenna2
time t s1 s2
time t + T −s∗2 s∗1
Table 2.1: Encoding and transmission sequence for the two-branch transmit diversity scheme [34].
A complete Alamouti scheme is shown in figure 2.8, in which is considered that the channel is modeled by a complex multiplicative distortion h11 for Antenna1 and h12 for Antenna2.
Note the combiner block that, based on the CSI, combines the signals received in the two time instances to estimate the ˆs1 and ˆs2 symbols.
Figure 2.8: Alamouti 2x1 scheme.
The received signals affected by channel distortion and white Gaussian noise in both instances are expressed in (2.12), wherein √1
2 represents the power constraint in order to
normalize the power per symbol to one. (
y1(t) = √12h11(t)s1+ √12h21(t)s2+ n1(t)
y1(t + T ) = −√12h11(t + T )s∗2+√12h21(t + T )s∗1+ n1(t + T )
In order to estimate the data, the received signals are combined using the following com-bining expressions, ( ˆs1= √12h∗11(t + T )y1(t) +√12h21(t)y1∗(t + T ) ˆs2= √12h∗21(t + T )y1(t) +√12h11(t)y1∗(t + T ) (2.13) Alamouti 2x2 Technique
In order to further decrease the probability of errors in the transmission, it is possible to apply the Alamouti technique to MIMO systems. In theory, the error probability decreases for systems with more antennas because there are new channels to consider, as note in figure 2.9, where is represented the h21and h22 channel functions for the second receiving antenna.
Figure 2.9: MIMO antenna scheme for Alamouti 2x2.
As already mentioned, the signal received by antenna 1 are given by (2.12), while the received signals at antenna 2 for both time instances are expressed in (2.14), in which are considered two new channel complex multiplicative distortions, h12 and h22.
(
y2(t) = √12h12(t)s1+√12h22(t)s2+ n2(t)
y2(t + T ) = −√12h12(t + T )s∗2+ √12h22(t + T )s∗1+ n2(t + T ).
(2.14) Given the received signals at each antenna, in (2.14), the data received by antenna 1 and antenna 2 can be estimated with (2.15) and (2.16) respectively.
( ˆs1,1= √12h∗11(t + T )y1(t) + √12h21(t)y1∗(t + T ) ˆs1,2= √12h∗21(t + T )y1(t) + √12h11(t)y1∗(t + T ) (2.15) ( ˆs2,1= √12h∗12(t + T )y2(t) + √12h22(t)y2∗(t + T ) ˆs2,2= √12h∗22(t + T )y2(t) + √12h12(t)y2∗(t + T ) (2.16) The estimated final symbols are then obtained by the following sum,
(
ˆs1 =ˆs1,1+ˆs2,1
ˆs2 =ˆs1,2+ˆs2,2
compared to the system with only one antenna on the transmitter, the Alamouti encoding schemes have lower BER values, showing how antenna diversity is effective in reducing the adverse effect of the fading channel.
Chapter 3
Orthogonal Frequency-Division
Multiplexing
OFDM is an attractive multiplexing technique for wireless digital communications widely used in modern telecommunication systems, which has become specially popular for its adop-tion in the DL of LTE. This technique combines the M-QAM modulaadop-tion scheme with or-thogonal frequency modulation, enabling to accommodate high data rate links over wireless channels characterized by severe multipath fading. The main drawback of OFDM, however, is the high amplitude fluctuations of the modulated waveforms.
The high PAPR in the OFDM signals is one of the main challenges for systems that make use of this modulation, since the large peaks introduce a degradation in the performance of the system when the signal is amplified by a non-linear PA. To reduce this non-linear effects, highly linear amplifiers are required, which normally implies low efficiency and great power dissipation, which is particularly an issue due to limited power resources on battery-powered devices, justifying why this method was not adopted for the UL in LTE [22]
3.1
OFDM Principles
The main features and implementation specifications regarding OFDM signals are ex-plained in this section, where the main advantages and disadvantages of using this type of modulation are also mentioned.
3.1.1 Multicarrier modulation and OFDM
One of the main handicaps presented by Single Carrier Modulation (SCM) is the fact that impulse noise or fade can cause the entire link to fail. On a Multi Carrier Modulation (MCM) system, only a portion of the carriers will be affected, and even error-correction coding can be used to restore the flawed carriers [23]. Figure 3.1 shows how the fading channel, with a H(f ; t) transfer function, affects the transmitted signal in both SCM and MCM methods, where BSCM and BM CM represent the SCM and MCM bandwidths respectively.
Figure 3.1: Comparison of SCM and MCM: frequency spectra of transmitted signals (a) and frequency spectra of received signals (b) [24].
The concept of multiple carrier multiplexing was developed in the 1950s and was firstly used in analog military communication, but this method has attracted attention as a mean of increasing the bandwidth of recent commercial digital communications systems [25]. In a classical multi carrier technique, the bandwidth of the signal is divided into several non-overlapping sub-channels to ensure that these do not corrupt the information of each other, however, it also leads to an inefficient use of the available spectrum.
OFDM follows the MCM philosophy, where a serial data stream is split up into a set of sub-streams and each is modulated on a separate single carrier. Unlike in the classical MCM, an OFDM signal is modulated to have part of its sub-channels bandwidths overlapping, allowing to save bandwidth as shown in figure 3.2. In order to accomplish such particularity without causing interference between sub-channels, the OFDM signal is generated based on digital technology, with the use of Inverse Discrete Fourier Transform (IDFT) on the transmitter and Discrete Fourier Transform (DFT) on the receiver, ensuring that the sub-carriers are mathematically orthogonal.
Figure 3.2: Savings in bandwidth: conventional multicarrier technique (a) and orthogonal multicarrier modulation technique (b) [23].
The DFT can be calculated from the efficient algorithm Fast Fourier Transform (FFT), which moves the signal from time to frequency domain representation, while the Inverse Fast Fourier Transform (IFFT) algorithm does the opposite operation. As seen in figure 3.3, for a sinusoidal waveform the FFT output will have a peak at the corresponding wave frequency and zero output elsewhere. On the other hand, as square waves contain a wide range of harmonics, its Fourier transform is composed of impulses in every harmonic in the Fourier series expansion, then the frequency domain output contains peaks at multiple frequencies. Usually, square waves are used as time references, but because of their wide range of harmonics, certain circuits use sine waves for timing reference to avoid noise or errors that can be caused by the harmonic components. The FFT and IFFT operations can be carried out back and forth without losing any of the original information [20].
Due to the specific structure of OFDM and its sub-carrier spacing ∆f , obtained from (3.1), the IFFT allows a low-complexity implementation of the OFDM signal. Assuming the sampling rate fs as a multiple of the sub-carrier spacing, expression (3.2), the total number
of sub-carriers N should be chosen so that the sampling theorem is sufficiently fulfilled. Considering Nc∆f as the nominal bandwidth of the OFDM symbol with Ncdata sub-carriers,
this implies that N should exceed Ncwith a sufficient margin. In addition to the data intended
to be transmitted, the signal must also include pilot sub-carriers, used for synchronization purposes between the receiver and transmitter, and some unused sub-carriers as guard carriers or null sub-carriers against interference from adjacent channels [26].
∆f = 1 TOF DM (3.1) fs= 1 Ts = N ∆f (3.2)
The IFFT size can then be selected as N = 1024, corresponding to a sampling rate fs= N ∆f = 15.36M Hz, where ∆f = 15kHz is the sub-carrier spacing [4].
Because the sub-carriers are orthogonal, their center frequencies are defined with such a difference in the frequency domain, that the remaining sub-carriers have zero value at the sampling instant of the desired sub-carrier, as in figure 3.4 [27].
A general set of orthogonal waveforms is given by
Ψk(t) = ( 1 √ Tse jωkt , 0 ≤ t ≤ T s, ωk= ω0+ kωs, k = 0, 1, . . . , Nc− 1 0 , otherwise (3.3)
In (3.3) the fk = ω2πk is the sub-carrier frequency and f0 = ω2π0 is the lowest frequency used
with k = 0. For a certain bandwidth, BW, the spacing between neighboring sub-carriers is ∆f = ωs
2π = BW
Nc , and since the waveform Ψk(t) is restricted to the time window [0, Ts], the
intercarrier spacing must also satisfy ∆f =T1
s =
R
Nc, for a R symbols per second rate [27].
3.1.2 Cyclic Prefix
As the signal propagates through a time-dispersive channel, it can experience ISI and, in OFDM, it also makes the ortogonality of the sub-carriers to be lost, resulting in Inter Carrier Interference (ICI) as addressed by Peled and Ruiz in 1980 [28].
It is possible to avoid ICI by adding to the transmitting signal a guard interval longer than the Channel Impulse Response (CIR). Instead of using blank guard intervals, it can be inserted a cyclic extension of the OFDM symbol as a prefix, as shown in figure 3.5. The Cyclic Prefix (CP) converts the original linear convolution into a cyclic one, ensuring the orthogonality among sub-channels [29]. The only disadvantage of the added CP is the minor loss of effective transmit power, since extra information with no useful data has to be sent [23].
Figure 3.5: OFDM signal with CP.
3.1.3 Peak-to-Average Power Ratio in OFDM Signals
The major drawback in using OFDM signals on the transmitter side is the high PAPR. The characteristic power peaks of OFDM signals, which appear when the sinusoidal signals of the sub-carriers are added constructively, requires using larger and expensive linear power amplifiers. In order to maintain an acceptable level of linearity it is needed to operate the PA with a large backoff, but since the peaks occur irregularly and infrequently, the power amplifiers will be operating inefficiently. Figure 3.6 shows a generic waveform of a OFDM signal, where is clear the difference between the average power level of the signal and its maximum power level.
To better understand the concept of PAPR, denote xn(n = 0, . . . , N − 1) as the data block
of the signal with N symbols, where N also represents the number of sub-carriers used for OFDM modulation. The duration of each symbol from xnis T and it modulates one of a set
of sub-carriers, fn(n = 0, 1, . . . , N − 1). As stated above, the N sub-carriers are chosen to be
orthogonal and the complex envelope of the transmitted OFDM signal is the given by (3.4).
x(t) = √1 N N −1 X n=0 xnej2πfnt, 0 ≤ t < N T. (3.4)
As can be seen in (3.5), the PAPR is the peak amplitude of the signal squared divided by the Root Mean Square (RMS) value squared, representing the average signal power. This
Figure 3.6: PAPR in OFDM signals.
expression can be rewritten considering the OFDM signal envelope in (3.4), resulting on the PAPR expression for OFDM signals in (3.6) [30].
P AP R = |xpeak| 2 x2 rms (3.5) P AP R = max0≤t<N T |x(t)| 2 1/N T ·RN T 0 |x(t)|2dt (3.6)
3.2
OFDM System Model
Considering Nc symbols to be transmitted, the OFDM signal production initializes with
the application of the IFFT to orthogonally modulate the Nc data constellation points in
the sub-channels. At the input of the IFFT block, N points are considered and the output samples are a set of N orthogonal sub-channels [23]. It should be noted that the ratio N/Nc,
which could be considered as the over-sampling of the time-discrete OFDM signal, is typically a non-integer number. Following the IFFT is a Parallel-to-Serial (P/S) conversion, and after it the CP can be introduced as a guard interval. At this point the signal is prepared to be amplified and sent over the communication channel.
At the receiver, the CP of the received signal is removed and it follows to a Serial-to-Parallel (S/P) conversion. The FFT is then used for each sub-carrier to convert the received signal back to frequency domain, and by proper channel estimation and equalization, the original transmitted spectrum is found. A P/S conversion is then performed to prepare the received symbols for demodulation. The generic base-band discrete-time block diagram of the OFDM transmitter and receiver depicting the described procedure is shown in figure 3.7 [31].
Chapter 4
Radio Frequency Front-End
This section addresses some of the main RF circuits used in wireless communication systems, with a focus on the type of circuits that were tested in the laboratory within the scope of this dissertation. The analysis of these circuits will be an important complement to what has already been stated about the structure of wireless communication systems, since they play a crucial role in modulation and demodulation of the baseband signal to RF radiation.
In wireless communication systems, the RF front-end is defined as the circuit between the antenna(s) and the digital baseband system. On the receiver front-end are included filters, Low Noise Amplifiers (LNAs) and mixers used for down-converting the signals received by the antenna(s) into signals suitable for the input of the baseband Analog to Digital Converters (ADCs). Contrariwise, the transmitter front-end circuit is responsible for up-converting the baseband signal, where filters and mixers are also used and the amplification of the signal before transmission is carried out by the PA. In this case, the non-linearity of the PA is a primary concern. Overall, the RF analog front-end can be considered the most critical part of the communicating system in determining, within limits, the digital BER performance and SNR potential of the system. Depending on the type of application intended for the wireless communication system, the design of the front-end circuits requires trade-off considerations between overall system performance, power consumption and size [35].
The block diagram in figure 4.1 shows the OFDM communication system in which are included the blocks from figure 3.7, as well as a simplistic representation of the front-end circuits used on the receiver and transmitter. It should be noted in figure 4.1 that the input and output signals of the digital signal processing system are sampled to discrete-time, since the front-end circuits deal with the continuous-time domain modulation or demodulation of these signals.
Figure 4.1: Block diagram of a OFDM communication system [35].
4.1
Mixers
The frequency mixers, or simply mixers, are fundamental components in any radio front-end, since they are employed to shift electromagnetic signals from one frequency range to another while preserving, as much as possible, its phase and amplitude characteristics. This operation is essential for passing the baseband signal to RF or vice versa. In terms of circuit design, the mixer can be based on different non-linear semiconductor devices, including diodes and field-effect transistors. However, as diode mixers are capable to operate without Direct Current (DC) bias, this type of mixers have been prevalent in many wireless systems [38].
Figure 4.2 shows an ideal mixer with three signal connections, as well as its input and output signals plotted in both time and frequency domain. The mixer receives the RF signal intended to be shifted in frequency and mixes it with a signal generated by the Local Oscillator (LO) of the system. The LO is typically driven with either a continuous sinusoidal wave signal or a square wave with a certain frequency f2. This signal works as a reference that when
mixed with the RF signal, with frequency f1, results an Intermediate Frequency (IF) signal at
the output, consisting of the sum and difference of the mixed frequencies, f1± f2. Then, the
IF signal can be filtered to select the sum, f1+ f2, or difference, f1− f2, frequency. Although,
if the output port of the mixer is terminated with a conventional bandpass or lowpass filter, part of the IF signal is reflected to the mixer, generating Intermodulation Distortion (IMD). While the LO can only be an input port, the RF and IF ports can be interchanged between the second input or output depending upon the application in which the mixer is being used [38][37].
If the RF signal is digitally encoded, two mixers are needed, one for the in-phase channel and another for a quadrature channel, as in figure 4.1 [38]. Since the in-phase and quadrature components of a signal are 90 degrees out of phase, the signal generated by the LO of the system is fed to a quadrature power splitter to produce two signals that differ in phase by 90 degrees. These signals are then applied to the LO ports of two identical mixers. On the transmitter side, the IF mixers input ports are fed by the I and Q baseband inputs respectively. The output RF signals are then summed together, resulting in a V(t) I/Q modulated signal
becomes the second input of the mixer.
(a) Ideal mixer symbol [38]. (b) Input and output signals of the mixer plotted at different do-mains.
Figure 4.2: Ideal mixer symbol and its input and output signals plotted in time and frequency domain.
4.2
Low Noise Amplifiers
A common measure to understand how well a receiver performs is by its sensitivity level. The sensitivity of a receiver is mainly dictated by the ability of the LNA to amplify the received signal while maintaining an acceptable SNR level. Since the received signal has typically low power levels, its amplification is required. However, an amplifier will increase the power of both the signal and the noise present at its input. With the aggravating that in this process the amplifier itself also introduces some additional noise. Therefore, it is crucial to use LNAs designed to minimize the additional noise, which can be achieved by choosing low noise components and operating points. Ideally, an LNA should provide sufficient gain to render low level signals delivered to the front-end components, while also being able to handle high level signals without adding excessive distortion [38].
4.3
Power Amplifier Characteristics and Non-linearity Effects
Every wireless system as to include in its output stage a RF PA to amplify the signal that will be sent by each transmitting antenna. Different wireless systems have significant differences in terms of RF output power, frequency bands, efficiency requirements and costs. Thus, a wireless system designer has to select a suiting PA to get the best trade-off between cost and desired performance. For cellular base station applications is generally used the current mode PA scheme, the generic structure of a current mode RF PA is presented in figure 4.3.Figure 4.3: Current mode amplifier architecture.
When comparing different types of PAs, current mode classes with specific characteristics can be set by changing the bias point of the transistor. The Class A is the most linear one, however, it is the least efficient class since it drains power even when no signal is being amplified. This class is only used when very high linearity amplifications are required. The Class B is more efficient than the Class A, since the bias point is at the threshold voltage of the device, meaning that the transistor is in the cut-off region if there is no input signal and starts conducting when there is a positive signal variation. It is possible to bias the transistor of the PA in a certain point between Class A and Class B, leading to what is called the Class AB. The Class C amplifier is biased in a point below the threshold voltage of the transistor, becoming the most efficient of the previous classes. Despite its good efficiency performance, it is also the most non-linear class, which makes it unsuitable for amplitude modulation applications, since variations in signal amplitude intensify its non-linear behavior [39].
On the other hand, the Doherty PA consists of two amplifiers connected by an impedance inverter network. This type of amplifiers is used to increase the signal excursion, which works just like a traditional single ended PA for lower power input signals, called carrier PA. For higher power input signals, as the carrier PA starts to compress, an extra power is added from the second amplifier called peaking PA. This scheme leads to an improvement of the average efficiency when compared with the traditional single ended amplifiers [40].
Figure 4.4 shows a typical block scheme of a Doherty PA, where the impedance inverter at the output of the carrier is necessary to guarantee the correct load modulation, and the phase difference that it introduces is recovered at the input by inserting a delay line [40]. In the context of this dissertation two different types of power amplifiers were measured, a Class B PA and a Doherty PA.
Figure 4.4: Typical block scheme of a standard Doherty PA [40].
Since cell phones are battery-powered systems, low operating currents and high power efficiency are critical requirements, as current consumption will have a significant impact on battery life. The more efficient the PA, the longer can be the talk time or the internet access. However, not only the power efficiency of the PA has to be considered when developing a wireless system, but also its non-linear effects, since the output of the amplifier will not be exactly a scaled copy of the input signal when the amplifier works out of its linear region. The main cause of the non-linearity is the active device, the transistor, which is approximately linear only for weak signals.
The non-linear behavior of the PA is a significant problem in communication systems that use OFDM. It has been explained in chapter 3 how OFDM is a spectrally efficient modulation, but it inevitably causes high PAPR. Leading the PA to a great sensitivity to distortion derived from the signal amplitude peaks outside the linear zone of the transistor [36]. The following presents the main non-linear characteristics of PAs and their consequent effects.
4.3.1 Intermodulation Distortion
When multiple carriers with different frequencies are applied to the input of a non-linear PA, energy is transmitted to frequencies that are not harmonics of these carrier frequencies. This phenomenon is called IMD and severely limits the performance of the communication systems.
To better understand how IMD occurs, let’s consider that the amplifier output voltage, vo, consists of an infinite series of non-linear products which are added on to the linear gain,
as expressed in (4.1). The vi represents the input signal voltage while the an(n = 1, 2, 3, . . . )
are experimentally determined coefficients that characterize the amplifier.
vo = a1vi+ a2vi2+ a3vi3+ . . . (4.1)
If the input signal consists of two in-band signals with equal amplitude, whose spacing is much smaller than the RF carrier frequency, as in equation (4.2), the output voltage is then obtain from expression (4.3).
