Construction of RF-link budget template for transceiver modelling

Department of Science and Technology

Institutionen för teknik och naturvetenskap

Linköping University

Linköpings universitet

LiU-ITN-TEK-A--19/051--SE

Construction of RF-link budget

template for transceiver

modelling

David Frykskog

Hjalmar Jonsson

LiU-ITN-TEK-A--19/051--SE

Construction of RF-link budget

template for transceiver

modelling

Examensarbete utfört i Elektroteknik

vid Tekniska högskolan vid

Linköpings universitet

David Frykskog

Hjalmar Jonsson

Handledare Anna Lombardi

Examinator Adriana Serban

Upphovsrätt

Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare –

under en längre tid från publiceringsdatum under förutsättning att inga

extra-ordinära omständigheter uppstår.

Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner,

skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för

ickekommersiell forskning och för undervisning. Överföring av upphovsrätten

vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av

dokumentet kräver upphovsmannens medgivande. För att garantera äktheten,

säkerheten och tillgängligheten finns det lösningar av teknisk och administrativ

art.

Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i

den omfattning som god sed kräver vid användning av dokumentet på ovan

beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan

form eller i sådant sammanhang som är kränkande för upphovsmannens litterära

eller konstnärliga anseende eller egenart.

För ytterligare information om Linköping University Electronic Press se

förlagets hemsida

http://www.ep.liu.se/

Copyright

The publishers will keep this document online on the Internet - or its possible

replacement - for a considerable time from the date of publication barring

exceptional circumstances.

The online availability of the document implies a permanent permission for

anyone to read, to download, to print out single copies for your own use and to

use it unchanged for any non-commercial research and educational purpose.

Subsequent transfers of copyright cannot revoke this permission. All other uses

of the document are conditional on the consent of the copyright owner. The

publisher has taken technical and administrative measures to assure authenticity,

security and accessibility.

According to intellectual property law the author has the right to be

mentioned when his/her work is accessed as described above and to be protected

against infringement.

For additional information about the Linköping University Electronic Press

and its procedures for publication and for assurance of document integrity,

please refer to its WWW home page:

http://www.ep.liu.se/

Abstract

This report presents the development of a simulation platform for radio receiver sys-tem design. The simulation platform is implemented in the AWR VSS environment. The report goes through the underlying theory of radio receivers and the methodology of im-plementing the receiver and receiver impairments in the simulation platform. The purpose of the project is to evaluate the AWR VSS environment for developing a simulation plat-form for RF budget analysis and to take advantage of built in functionality as well as its graphical interface. The project results in two simulation platform templates for receivers that uses different simulation types. It is demonstrated that these simulation platforms can have much of the specified requirements implemented. Some of the listed functional-ity requirements turns out difficult or impractical to implement however. It is concluded that VSS can be used for developing simulation platforms with the specified requirements. Some of the functionality that is not natively supported by the software is implemented using calculations post simulation and VBA scripting. These methods are proposed as a solution for adding functions to the template in future work.

Acknowledgments

This work would not be possible if not for the great support and guidance of our supervisors at Ericsson. With the help and expertise of our main supervisor, Peter Pääkkönen, our work was made substantially easier and the quality of the results would not be the same without him. We would also give out thanks to the head of our team at the analog design depart-ment, Jörgen Johansson, for pitching the idea of the master thesis and for helping us getting started with our work. We have been fortunate to have great support from many people at Ericsson, who has lent their time and knowledge to make sure we got all the help we needed to complete the work.

We have also had great support from our supervisors at Linköping University who has managed the administrative tasks and helped the work flow smoothly as well as made sure the project did not get out of hand.

Finally we would like to extend our gratitude to our families who has supported our efforts in all the ways possible. Thanks to their unrelenting support, we have managed to finish our electrical engineering education and this master thesis project.

Contents

1.5 State of the art . . . 4

2 Theory 5 2.1 Noise . . . 5

2.2 Non linearity in RF devices . . . 6

2.3 Selectivity . . . 8

2.4 Degradation . . . 9

2.5 Analog circuits in RF receivers . . . 9

2.6 Process variations . . . 12

2.7 Modulation . . . 12

2.8 I/Q Data . . . 13

2.9 Receiver architectures . . . 14

2.10 Digital Signal Processing . . . 17

3 Development 20 3.1 NI AWR Design Environment . . . 20

3.2 Receiver impairments . . . 21

3.3 Receiver model implementation . . . 21

3.4 Simulation platforms . . . 32

4 Results 34 4.1 Simulation platform results . . . 34

4.2 Budget simulations . . . 39

4.3 Time domain simulations . . . 43

5 Discussion 49 5.1 Results . . . 49

5.2 Method . . . 50

5.3 Improvements . . . 51

Nomenclature

In order of appearance

RF Radio Frequency FIR Finite Impulse Response RX Receiver/Receiving system IIR Infinite Impulse Response NI National Instruments EDA Electronic Design Automation AWR Applied Wave Research EM Electromagnetic

VSS Visual System Simulations API Application Programming Interface LNA Low Noise Amplifier ADS Advanced Design Systems

PA Power Amplifier BER Bit Error Rate

IP3 3:rd order Interception Point SNR Signal to Noise Ration

NF Noise Figure PID Product, Integrator, Derivative P1dB Compression Point RMS Root Mean Square

HDx x:th Harmonic Distortion DUT Device Under Test LO Local Oscillator

PLL Phase Locked Loop

ADC Analog to Digital Converter IM[N] Nth Intermodulation product

AGC Automatic Gain Control RFFE Radio Frequency Front End

DSP Digital Signal Processing VGA Variable Gain Amplifier PGA Programmable Gain Amplifier

LPF Low Pass Filter BPF Band Pass Filter BSF Band Stop Filter HPF High Pass Filter

IL Insertion Loss

DC Zero Hz Frequency Component IC Integrated Circuit

AM Amplitude Modulation FM Frequency Modulation PM Phase Modulation ASK Amplitude Shift Keying

FSK Frequency Shift Keying PSK Phase Shift Keying

QPSK Quadrature Phase Shift Keying QAM Quadrature Amplitude Modulation

IF Intermediate Frequency SDR Software Defined Radio DFT Discrete Fourier Transform

1

Introduction

Ericsson is a company which mainly focuses on the development of Radio Infrastructure for cellular, wireless connectivity. Their mobile solutions are installed all over the world in all environments and conditions. This requires high tolerances, high precision engineering and therefor high precision tools in the design phase. The push towards a more mobile society with 5G connectivity on the horizon calls for these tools to be more complex, precise and comprehensive than ever before.

1.1

Motivation

Current simulation platforms and tools used for transceiver link budget simulations in RF design at Ericsson are often Matlab or Microsoft Excel based. These custom tools are ver-satile and detailed, but complex and thus they require knowledge and competence in both these tools, and in specific transceiver systems. In the hands of an expert with knowledge in transceiver systems and the tools themselves, the tools are efficient. However for a person in the field with less experience in either area, the threshold to start using these tools is high.

The companies developing software for electronic design are with every edition of re-leases including more advanced solutions in their system simulation tools. This, along with a relatively low entry bar and a visual interface, makes for an intuitive and efficient work interface for system simulation. In addition, the system simulation tools can already include built in functionality similar to that of the tools in Matlab and Microsoft Excel. One major drawback of these software tools is that they might not include options and possibilities of customization for specific needs, such as specific impairments and functionalities. However, with the release of simulation tools such as Applied Wave Research (AWR) system Simu-lation tool Visual System SimuSimu-lations (VSS), these customization options have however been improved upon.

For engineers, ease of use, versatility and improved customization makes VSS competitive towards existing link budget simulation tools made in Matlab and Microsoft Excel.

1.2. Aim

1.2

Aim

The project aims to implement an RF link budget model in AWR VSS environment. Specifi-cally the RX and TX chains of a homodyne transceiver should be modelled for budget analysis purposes. The work includes analog and digital transceiver from antenna to the digital inter-face, but not any further. In comparison to present link budget tools in Matlab and Microsoft Excel, the tool should include frequency spectrum at each component (block) in VSS and RF budget parameters such as gain, noise figure and compression point.

In addition, the work aims to clarify the drawbacks of the present tools in Matlab and Mi-crosoft Excel regarding ease of use. The link budget tool in VSS should address these issues. As mentioned in Section 1.1, the tool should be targeted towards users, i.e., RF engineers not working with the present, more complex tools on a daily basis.

Functionality and parameters

The link budget tool should include the following functionality with associated parameters:

Analog blocks

Analog blocks include analog filters, amplifiers (LNA, PA), attenuators (including cable losses), mixers and passive components. They have a lot of different properties and parame-ters to be taken into account when making accurate models.

• Gain

• Noise Figure • IP3

• P1dB • HDx

IQ modulator and demodulator

• Spurious response • IQ-imbalance • LO leakage

• PLL including reference clock • DC offset • Reciprocal mixing • LO phase noise ADC • Sampling rate • Aliasing • Jitter

1.3. Research questions

Filters, channel filters

• Rejection • Decimation

Blockers and interfering signals

• Blocking • IM blocking

Other functionality

• Automatic Gain Control (AGC) • Gain calibration

Optional functionality

If the scope of the project allows for further modelling, the following proposals are to be investigated:

Statistical simulation

• Yield analysis

Digital blocks

• Channel filtering

• Ripple estimation and handling

1.3

Research questions

The simulation platform will be implemented in the VSS environment in AWR. When con-verting functionality and adding new ones to a new platform, the natural question to ask is what functionality can be implemented and what compromises have to be made in order to have these functions work in the new environment. The research questions that this project aims to answer are:

• What functionalities and parameters can be implemented in VSS using built in func-tionality and what have to be implemented using ad hoc means?

• What compromises needs to be made when implementing the different functionalities?

1.4

Delimitation

The scope of the project is limited to existing radio links. It aims to include functions already in use in excel-based budget tools with addition of VSS functionality such as time-domain. Contrasting the existing Excel-based link budget, the VSS tool will remain on system-level complexity.

The tool aims to be practical in the sense that user experience should be prioritized over complexity and customization. In a scientific sense, this feature is hard to define and to mea-sure. An example of a so called "practical" aspect of the tool could be simulation time. This limits the project to certain design choices based on this philosophy. If a model is considered accurate but slow, it might be discarded.

1.5. State of the art While VSS includes functionality to implement many of the listed requirements of the tool, it might not include others in the sense that they are either considered "impractical" or do not exist. An example of an impracticality is long simulation times. The limitations will be considered at the end of the project for evaluating VSS as a software for RF system level simulations and for implementing budget-like tools.

1.5

State of the art

Developing radio receivers and transmitters while optimizing all relevant circuit and system parameters is often pushing the limits of what is possible. High frequency transceivers are some of the most complex systems widley in use in the world today and require extreme precision in all steps of development.

Since the complexity and performance of radio systems increase, it is getting harder for legacy tools to meet the demands of engineers. Adding functionalities continuously over time eventually makes these tools slow and difficult to get to learn and to use. This pushes compa-nies to innovate and find alternative tools that have the ability to handle complex workloads in an easy to understand manner.

2

Theory

This chapter covers the theoretical background considered related to the functionality of the link budget tool in VSS. What we mean by functionality is the set of parameters listed in Section 1.2.

We will cover the baseline components of an RF front end (RFFE) receiver. These include amplifiers, filters and mixers. Other components and functionality covered in this section include Analog-Digital-Converter (ADC), Automatic Gain Control (AGC) and channel filter-ing. There are many other aspects of RF system modelling such as antenna characterization, DSP and the transmitting part of the system. But for the sake of narrowing the scope of the project, we will focus mainly on the analog front end, and theory related to the link budget tool.

We will also cover non-ideal aspects of RF system design. When designing and evaluating RF system performance and characteristics, we must take into consideration these aspects. These include noise and distortion, process variations and nonlinear behavior of RF compo-nents. It is worth noting that all non-ideal phenomena mentioned above are not necessarily independent. This will be clarified.

2.1

Noise

Noise can be described as a random process which is present in RF systems. Noise present in RF systems is introduced by the antenna from the external environment to the system, and is also generated internally by active components within the system itself.

One type of noise important to radio systems is thermal noise. It is caused by random motion of charges and is generated by anything lossy in radio systems. It can also be gen-erated outside the system e.g., in the atmosphere from thermally excited charges. Thermal noise can be characterized as frequency independent, noise, i.e., white Noise. When measuring noise power in the frequency domain, we can view the thermal noise as a constant power spectrum. across all measured frequencies due to this characteristic [11].

When measuring a signal in the time domain, we will observe the noise as a random process on top of the signal. If the noise power in the system is stronger than the power of the desired signal, we will not be able to observe the desired signal. Therefore, it is necessary to minimize the amount of noise generated by any RF system.

2.2. Non linearity in RF devices A simple example of a noise source is a resistor of value R in an environment with tem-perature T. Charges in the resistor are thermally excited and so we will be able to measure a voltage across it. Since thermal noise is a type of white noise, the mean voltage is 0. However, the root mean square value (rms) is non-zero and is given by (2.1).

Vn=

?

4kTBR (2.1)

where B is the system bandwidth and k is the Boltzmann constant. In a system where the resistor acts a noise source with impedance R, the available noise power Pn delivered to a

matched load can be derived with elementary circuit theory as in 2.2. Pn = Vn 2 2₁ R = V2 n 4R =kTB (2.2)

It can be seen that both the temperature and the system bandwidth play a crucial role when characterizing system noise power.

Noise figure

In RF systems, the increase of noise can be characterized by noise figure, which is the mea-surement of degradation in signal-to-noise ratio (SNR) from a point to another in a system (for example input and output). Noise figure is defined as [11]

F= S1/N1

S2/N2 (2.3)

where terms with notation 1 refers to port 1 and notation 2 to port 2. Single components often have noise figure specified in their data sheets in the units of decibels as F(dB) =10log10(F).

As systems consists of many components, independently produced and tested, in order to characterize a system by its total noise figure, Friis formula for noise can be used: [11]

F=F1+ F2´ 1

G1 +

F3´ 1

G1G2 +..., (2.4)

In order to minimize noise figure, we can derive from (2.4) that components with a large gain (G) such as amplifiers should be put close to the input.

2.2

Non linearity in RF devices

In many cases, system modelling can be approximately made assuming linear dependency between input and output signals. However, this linear assumption is never true and espe-cially not for RF devices and networks. This section covers the basics of nonlinear systems.

Given a nonlinear network, the output signal can be expressed as

so =a0+a1si+a2s2i +a3s3i..., (2.5)

where si is the input signal and so the output signal. Whereas for a linear network, the

output can simply be expressed as so = a0+a1si. A system with an input signal given by a

tone si=V0cos(ω0t)results in an output

so =a0+a1V0cos(ω0t) +a2V02cos2(ω0t) +a3V03cos3(ω0t) +...,

= (a0+1 2a2V 2 0) + (a1V0+3 4a3V 3 0)cos(ω0t) +1 2a2V 2 0cos(2ω0t) +1 4a3V 3 0cos(3ω0t) +..., (2.6)

From (2.6), it can be seen that harmonic components are present at the output of a nonlin-ear system, [11].

2.2. Non linearity in RF devices As signals in RF systems are rarely a single tone, it might be of interest to investigate the case when the input signal consists of many tones. Lets say si = V0(cos(ω1t) +cos(ω2t)),

then the output becomes

so =a0+a1V0(cos(ω1t) +cos(ω2t)) +a2V02(cos(ω1t) +cos(ω2t))2+a3V03(cos(ω1t) +cos(ω2t))3+...,

(2.7) The output spectrum will consist of harmonics given by

mω1+nω2 (2.8)

where m and n are arbitrary integers. These combinations are what is called intermodula-tion products [11].

Gain compression

Looking back at the output signal of an nonlinear system (2.6), we can derive the voltage gain of the fundamental frequency as:

Gω0 = (so/si)ω0 = a1V0+3₄a3V03 V0 =a1+ 3 4a3V 2 0 (2.9)

Since a3 is negative for amplifiers, it can be seen that the voltage gain decreases as the

amplitude of the input signal V0 increases. The gain becomes saturated as signal strength

increases at the fundamental frequency, hence the name gain compression. For nonlinear RF components, the gain compression is quantified as P1, usually in decibels. P1 is the point

where the gain has decreased 1 decibel with respect to the linear case, either referred to the input or the output [11]. Fig. 2.1 shows a typical nonlinear amplifier with output compression point at 15 dB.

Figure 2.1: Input versus output signal strength of a typical nonlinear amplifier simulated in VSS

2.3. Selectivity

Third-Order Interception Point

When the input signal consists of two tones, the output, is given by (2.7). It can be seen that third order intermodulation products are increasing with the cube of the input voltage. As the signal voltage is increased, the output power of the fundamental tone and the third order tones approach one another. The hypothetical point where these two lines cross is called P3

and is either referred to the input or the output. Solving the system of equations for the two lines, we find

P3=

2a3₁ 3a3

(2.10) where P3is referred to the output. This characteristic is of interest in RF systems since

these intermodulation products can cause distortion in the received signal This can in turn affect performance.

Fig. 2.2, shows the signal strength of fundamental tone (in blue) and the signal strength of the third order intermodulation tone (in pink). The intersection of the two dotted lines is referred to as P3. In contrast to the dotted lines, the solid lines take into account the nonlinear

contribution in both the fundamental tone (in blue) and the third order intermodulation tone (in pink).

Figure 2.2: VSS simulations of input versus output signal strength of a typical nonlinear amplifier. Fundamental tone (in blue) and third order intermodulation tone (in pink).

2.3

Selectivity

An RF receiver must provide certain requirements for desired performance. RF receivers have to be able to receive a signal in a specific channel within the band of operation, while rejecting signals and distortions from adjacent channels. This specific requirement is referred to as selectivity [11].

2.4. Degradation

2.4

Degradation

Degradation is a measurement of how well a radio channel performs with interference. The degradation can be calculated using (2.11).

Degradation[dB] = [POUT+I NT´ G+INT]´[POUT´ G] (2.11)

2.5

Analog circuits in RF receivers

Along the receiver chain, several types of amplifiers are used. They have all different pur-poses besides the main purpose of amplifying the signal.

Low Noise Amplifier

According to the Friis formula (2.4) it is important for the total noise figure of the system, to have high gain components close to the antenna of an RX lineup. The earlier the component lies in this chain, the more its noise figure contributes to the over all noise figure. The solution is the low noise amplifier or "LNA".

When designing an amplifier a, trade-off is made between the amplifiers gain and its noise figure. The LNA is an amplifier that is optimized for low noise instead of maximum gain, in order to keep the total noise figure of the lineup low.

For any two port amplifier, the noise figure can be described as in (2.12) [11]. F=Fmin+

RN

GS|YS´ Yopt|

2 _(2.12)

Where YS : Source admittance, Yopt : source admittance that results in the lowest

possi-ble noise figure, Fmin : Minimum noise Figure of transistor, RN : Equivalent noise figure of

transistor and GS: The real part of the source admittance [11].

Variable Gain Amplifier and Programmable Gain Amplifier

Variable Gain Amplifiers (VGA) and Programmable Gain Amplifiers (PGA) are as the names suggest, configurable amplifiers. Their purposes vary depending on design and application, but in receivers they can act as gain control and gain calibration. Gain control can be used to compensate for nonlinear behavior at high received power in order to improve dynamic range. Gain calibration can be used to compensate for process variations and temperature variations in components. These are examples of how VGA/PGA technologies were used in this project.

Filters

Analog passive filters are used to attenuate undesired frequency content of an incoming sig-nal, while retaining desired frequencies. There are four basic types of passive filters; Low-pass filters (LPF), bandLow-pass filters (BPF), bandstop filters (BSF) and high-Low-pass filters (HPF).

In RF applications, these types of filters can be implemented using a variety of techniques. These include lumped component filters, microstrip filters and cavity resonators.

Lumped component filters are implemented with lumped elements such as capacitors, inductors and resistors. Microstrip filters are implemented using microstrip transmission lines utilizing stubs and coupled lines as filtering elements [10].

Cavity filters are filters constructed from a cavity resonator i.e. a wave guide with its in-ternal dimensions tuned to create a standing wave for certain frequencies while frequencies outside the passband get attenuated by not being able to propagate through the cavity. Be-cause the filter is a cavity, it attenuates unwanted signals hard, which is why it is commonly

2.5. Analog circuits in RF receivers used as the bandpass filter at the antenna of RF systems to prevent interferance from reaching the components of the receiver. [17].

There are some parameters of importance when analyzing filters. Insertion loss (IL) de-scribes how much power that is lost between input and output. Ripple dede-scribes the flatness of the filter frequency response in the passband. Rejection describes how the filter attenuates frequencies in the stopband [10].

Mixers

The RF mixer is an essential component in any RF system. It is a two-input, one-output component that combines the two input signals by adding together their two frequency com-ponents as: ω1+ω2or ω1´ ω2. This can be done to either up- or down-convert a signal

depending weather the mixer is used in a transmitter or receiver. Since the mixer is a non-linear device, its characteristics can be described as a series of Taylor polynomials according to (2.7).

Up-conversion is used in the transmission link: Intermediate frequency (IF) signal is mixed with a signal from a Local Oscillator (LO) that acts as a carrier for the information contained in the IF signal. The mixed signal is then sent to the RF port. Given the two input signals

VLO(t) =cos(ωLOt) (2.13)

VIF(t) =cos(ωIFt) (2.14)

The resulting RF waveform from Up-conversion can be approximated as in (2.15). VRF(t) =

K

2[cos(ωLOt´ ωIFt) +cos(ωLOt+ωIFt)] (2.15) where K is the voltage conversion loss constant of the mixer.

At the receiver end, the RF and LO signals will act as input and IF the output. The output of the Down-converting mixer can be approximated as in (2.16).

VIF(t) =

K

2[cos(ωRFt´ ωLOt) +cos(ωRFt+ωLOt)] (2.16) A simple mixer can be implemented with a single diode. However, simple mixers have the general disadvantage of having a high LO- and RF-leakage. There are two types of balanced mixers designed to handle this problem; single and double balanced mixers.

The single balanced mixer is used to suppress the level of either the RF or LO input signal while the double balanced mixer is used to suppress both. The advantages of using a single balanced mixer is that it does not require high LO drive levels, it is also cheaper and less complex. The advantages of the double balanced mixer are that all ports are isolated, This preventing leakage, increases the linearity of the mixing and has a better spurious response since all even order products are suppressed. This is why the double balanced mixer is in wider use today than its single counterpart.

Mixers can be used to detect phase differences in the two input signals. Mixers however often display some non ideal functionality such as DC-offsets when used in this configuration [11].

Phase noise

Phase noise are random fluctuations in phase that are ever present in real signals. Since the phase noise is generated from the uncertainty of the phase of the signal, the effect it generates "propagates" outwards from the signal in the frequency spectrum as shown in Fig. 2.3 [8].

2.5. Analog circuits in RF receivers

Figure 2.3: Two systems using different oscillators showing the effect of phase noise Red: noisy oscillator, Blue: system with ideal oscillator [8].

ADC

ADC stands for Analog-to-digital converter and is the component responsible for sampling an analog signal and converting it into digital information. The basic idea of ADC functionality will be described and some of the implementation types will be discussed. The reader is referred to [7] for further theory regarding the implementation types of ADC’s.

Flash ADC

Flash ADC uses parallel connected comparators with different reference signals. The signal is sampled one symbol at a time. Each comparator outputs one bit. They are simple in their principle of operation and fast, but suffer from a large number of reference voltages and relatively high power consumption.

Pipeline ADC

Pipeline ADC architectures perform conversion using multiple cascaded stages of low-resolution ADCs. Each stage outputs the quantization error of the ADC, amplifies it and sends it to the input of the next stage. This operation is performed until the last stage which only consists of an ADC without quantization error output. It is slower than flash ADCs but power consumption is not as high.

Delta-Sigma ADC

Delta-Sigma ADC utilizes a feedback loop to force input and output to similar levels, while at the same time making use of low resolution (can be as low as 1 bit) ADC quantizers. The quantization error (noise) is added to the output signal and shaped with the help of an inte-grator within the delta-sigma loop. The inteinte-grator loop filter has the effect of a LPF for the input signal and HPF to the quantization noise. We can therefore filter out the quantization noise and keep the input signal.

AGC

AGC stands for Automatic Gain Control and is a control feature which is particularly used in RF systems. It is used to actively adjust power levels in a system. For receivers, it is often times used as automatic attenuation for keeping linearity and to extend dynamic range of the ADC. In real time, we cannot know what the received signal strength will be, and we have to take that uncertainty into account when designing a system.

2.6. Process variations In the case of a signal weak enough not to be detected, an AGC will not be able to do anything to improve performance. In the case of signal strong enough to either compress gain in the system (destroying linearity) or being outside the dynamic range of the ADC, we can actively adjust system gain to attenuate this signal.

Aspects of stability, noise figure and linearity all have to be taken into account for in order to design AGC systems. By attenuating the signal for example, we will inevitably increase noise figure as derived from (2.4).

There are many ways of implementing AGC functionality for receivers, but the one archi-tecture focused on in this project has similarities to [14].

2.6

Process variations

When fabricating physical components for any system, there will be slight deviations in their properties due to imperfections in the manufacturing and/or the materials being used. This is referred to as the process variation of the component. In high performance RF systems, the component parameters needs to be accurate to very high degree to ensure reliability of operation. When designing RF-systems, these variations need to be taken into account to make sure the variation can be compensated for or have narrow enough margin of error not to disturb the larger system.

E.g., a filter can have a process variation in the pass-band attenuation that can attenuate a signal more than what is acceptable. Moreover, the signal propagating through the filter can be affected by attenuation ripple. These effects must be predicted in the design phase through simulations that include parameters emulating process variations.

One way to compensate for variations in component properties is to do a "worst case analysis" where all the different factors that determine system’s performance are taken into consideration and are assumed to be at "worst case" scenario. Then one can evaluate and/or estimate the performance of the system at in a "worst case-scenario". This method is effec-tive but has the flaw of assuming all of these factors are independently affecting the system. This is a rough estimation for performance evaluation since these factors are not necessarily independent.

In order to determine a more accurate model of the absolute range of component per-formance variation, IC-manufacturers use statistical tools. One of these tools is the Monte Carlo analysis which can be used to determine the components statistical variation based on a function of random number generation [6].

2.7

Modulation

This section aims to introduce the reader to the basic theory of RF modulation.

Modulation of a signal is the process of varying certain properties of a periodic waveform (carrier signal) with another signal (modulating signal). A general way of thinking about this scheme is that the modulating signal contains information and the carrier signal controls where the radiated spectrum is located in frequency.

There are various ways to modulate a carrier wave using properties of waves. For a gen-eral signal in time

S(t) =Acos(ωt+φ) (2.17) There are 3 degrees of freedom to modulate the signal. Amplitude A, frequency ω and phase

φ. The analog modulation schemes are properly called Amplitude Modulation (AM), Frequency Modulation (FM) and Phase Modulation (PM).

Digital modulation schemes use the same degrees of freedom as analog modulation, but uses it to encode digital bits of information. The digital counterpart to the analog modulation

2.8. I/Q Data schemes AM, FM and PM are Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK) and Phase Shift Keying (PSK).

Using the basic principles of modulation presented in this section, more advanced meth-ods of modulation can be derived. For example, both amplitude and phase modulation can simultaneously be utilized in the same modulation technique. [11].

2.8

I/Q Data

I/Q data is a form of data representation using the so-called in-phase and quadrature-phase components of a sine wave. Using the trigonometric identity

cos(x+y) =cos(x)cos(y)´ sin(x)sin(y) (2.18) and applying it to a waveform

S(t) =Acos(ω0t+φ(t)) (2.19)

it gives

Acos(ω0t+φ(t)) =A[cos(φ(t))cos(ω0t)´ sin(φ(t))sin(ω0t)] (2.20)

where

SI(t) =Acos(φ(t))cos(ω0t) (2.21)

and

SQ(t) =´Asin(φ(t))sin(ω0t) (2.22)

are the in-phase and quadrature phase components of S(t).

For RF application this result can be applied for modulating a single waveform (2.19) with information contained in its I and Q components. It also means that the data stored in I and Q can be independently set and the resulting waveform is still in the form as in (2.19) [5].

In PSK, the most simple case is storing information with two states, where φ can store either binary 0 or binary 1. For this, the advantage of having information stored in I and Q components does not even need to be utilized if φ0and φ1are 0 and π respectively.

This idea can be expanded by introducing more states. QPSK (or Quadrature Phase Shift Keying) utilizes 4 states of φ while holding amplitude is constant. Each state can store two bits utilizing I/Q data. These states are φ11= π4, φ01= 3π4 , φ00 =´π4 and φ10 =´π4 [11].

Introducing amplitude as a degree of freedom to the modulation scheme, more than 2 bits per state as in QPSK can be utilized. An example of a I/Q modulation scheme using both amplitude and phase modulation is QAM (Quadrature Amplitude Modulation). QAM modulation has number of states on the form as in (2.23).

M=2n (2.23)

QAM modulation schemes are referred to by their number of states on the form M-QAM. For example, we have 4-QAM, 16-QAM, 32-QAM, 64-QAM and so on where the number of bits per states is

n=log2(M) (2.24)

By this result we can conclude that QPSK and 4-QAM are identical modulation schemes even though the theoretical background is different [11].

A graphical representation of the states encoded in I/Q data is the constellation diagram. A constellation diagram of QPSK states and 64-QAM states is presented in Fig. 2.4. X-axis data is the in phase component I, and the y-axis data the quadrature phase component Q.

2.9. Receiver architectures

Figure 2.4: Left: QPSK, Right: 64-QAM. Simulated in VSS

2.9

Receiver architectures

This section introduces two types of receiver architectures, i.e., the superheterodyne and ho-modyne receivers.

General receiver architecture

The types of receivers which this thesis covers consist of the following parts: Receiving an-tenna, filtering, amplification, down-conversion, sampling, channel filtering and decoding.

Superheterodyne Receiver

The main functionality which identifies superheterodyne receivers is the use of an interme-diate frequency (IF). Superheterodyne receivers might use more than one down-conversion to overcome problems due to LO stability at high frequency RF. After each down-conversion, image frequencies created by the mixing process and ideally intermodulation products are filtered out [7, 11]. A general schematic of a so called dual superheterodyne receiver, which uses two down-conversion stages is presented in Fig. 2.5 and Fig. 2.6

Talking about superheterodyne receivers, one issue that appears is so called image rejection. The problem is characterized by a trade off between image rejection and adjacent channel suppression. The problem of image rejection appears since the mixer outputs two dominant signals of frequencies|ω1´ ω2| and |ω1+ω2|. Since we have two cases where we can mix to

a desired IF, this means RF frequencies of distance 2ωIFin frequency will mix to the same IF

2.9. Receiver architectures

Figure 2.5: Superheterodyne receiver architecture [13]

Figure 2.6: Superheterodyne receiver architecture with I and Q data [13]

Homodyne Receiver

Homodyne, Direct-conversion or Zero-IF receivers directly convert the RF signal into baseband without any IF stage. Down-conversion is achieved with a quadrature down-converter split-ting up in-phase (I) and quadrature-phase (Q) components of a signal, which is then filtered and sampled. The reasons for quadrature down-conversion of the signal in I and Q com-ponents is to avoid so called "folding" from the negative frequencies and in case of phase modulated signals. In case of IQ modulated signals, the different sidebands of the RF signal contains different information and splitting up these into two different phases at demodula-tion avoids folding since the different sidebands fall on each side of baseband [12].

The main difference here from a super heterodyne receiver is that channel selection is achieved with a low pass filter instead of a bandpass filter since the signal is already down-converted to baseband. The LO signal is tuned to the incoming RF frequency for achieving zero-IF [7].

Homodyne receivers come with some technical challenges. LO leakage from the mixers result in so called self mixing which has the effect of adding a DC offset to the output of the mixer. IQ imbalance is also an issue when talking about homodyne receivers, although this is not necessarily unique to this type of receiver.

2.9. Receiver architectures

Figure 2.7: Homodyne receiver architecture [13]

Figure 2.8: Homodyne receiver architecture with I and Q data [13]

Discrete-Time Receivers

In certain applications, the use of direct RF sampling can be used to advantage. One sim-ple case is to enable multi standard radio on one single front end architecture. This leaves more design space for software defined radios (SDR), which have the possibility to increase versatility and reduce bill of materials since a lot of the functionality lies in integrated digital circuits. However, RF sampling of various kinds comes with a new set of challenges. Remov-ing analog RF devices from the design puts high performance requirements on samplRemov-ing and signal processing in order to compete with established and well understood technologies.

A few different RF sampling architectures which have been reported on over the years are presented here.

The so called analog processing receiver, uses analog filters and decimation instead of a mixer after the LNA in order for the ADC to sample the signal at a reasonable rate. Interfering signals and blocker signals are therefore not mixed down with LO harmonics as IM products, but instead aliased when sampled unless filtered. To tackle this problem, anti-aliasing filters can be implemented or the sample rate of the ADC must be increased. The first reported implemented receiver of this type was presented in [15].

Direct digitization receivers apply ADC functionality directly after LNA without adding any extra analog hardware. There are various types of these receivers depending on sampling method and of ADC [7].

2.10. Digital Signal Processing Another discrete-time receiver well worth mentioning but not discussed any further are Hybrid-filter bank receivers [16] which use the principle of decomposing the signal in the fre-quency domain using a bank of analog filters. There are various other kinds of discrete-time receivers and it is an active topic of research.

2.10

Digital Signal Processing

Digital signal processing deals with how the signal is handled from the ADC to the eventual recreation or storage of the analog information. The first step of any digital processing is the sampling of the analog signal.

Sampling

Sampling is the act of measuring a signal value at certain points in time. How often these measurements are done is called the "sampling rate" or "sampling frequency"[Fs]. How this

sampling frequency relates to the signal frequency being measured dictates if the information of the original signal will be preserved or not. Sampling frequency is derived through the time between measurements, i.e, samples. It is the measurement of how many samples per second that are performed.

The Nyqvist theorem states that any signal needs to be sampled for at least twice its fre-quency or simply put:

F_signal,maxă Fs

2 (2.25)

A signal that has a frequency higher than the Nyqvist frequency will naturally form a mirror signal on the opposite side of the Nyqvist frequency due to having the samples fall on the same places on the signal. The mirror signal will fall on Fs´ FSignalas shown in Fig 2.9.

This is also known as aliasing [9].

Figure 2.9: Signal mirroring (aliasing) around the Nyquist frequency [9]

Spectrum analysis

Spectrum analysis is a tool used when conducting measurements and analyzing signals. Fourier transformation of a signal decomposes it into its spectral components. In the case of signal in time, the transformed signal will be a function of frequency.

The discrete version of the Fourier Transform is called Discrete Fourier Transform (DFT) and is widely used to analyze sampled signals. The more samples analyzed, the higher the frequency resolution will be on the Fourier Transformed signal.

As signals are sampled, some things need to be taken into account for when performing spectrum analysis using DFT. A finite frequency means aliasing will occur when measuring frequencies above the Nyquist frequency.

2.10. Digital Signal Processing Another problem that arises with analyzing finite data is that we cannot assume first and last sample continuously connect with perfect periodicity. This gives rise to uncertainty in the frequency spectrum as the signals will appear to contain information far out from the center. One way to tackle this issue is to apply so called Window functions to the spectrum to mitigate some of these effects. Windowing does this by applying different functions in time domain that minimizes the amplitude of the discontinuos area of the signal that would other-wise cause high frequency spikes in the frequency domain. In Fig. 2.10 the difference between a spectrum with applied window and one without is presented where the difference can be seen clearly.

Figure 2.10: VSS simulation of a QPSK signal spectrum with Hanning window (Blue) and without window function (pink)

Digital filtering

In order to minimize the risk of aliasing, the signal cannot contain frequencies above the Nyquist frequency. This can be achieved by applying a low pass filter with a cut off frequency at the Nyquist limit i.e., an anti-aliasing filter. However, because there is no filter with an instant transition band, the cut off frequency needs to be slightly below the Nyquist limit in order to ensure that the stop band attenuates the higher frequencies enough for the aliasing to be negligible [2].

A filter that is especially well suited for this is the Finite Impulse Response or FIR-filter, which is a type of digital filter. The FIR filter is finite because it settles to zero in a finite time. An IIR (Infinite Impulse Response) filter however can decay towards zero for an infinite time. FIR filters are based on a N-number of parallel "taps" as seen in Fig. 2.11.

2.10. Digital Signal Processing

Figure 2.11: Transposed direct form FIR filter [2]

The mathematical expression of the output signal y given an input signal x through a FIR filter with filter coefficients h is given by (2.26).

y[n] =

N´1

ÿ

i=0

h[i]x[n´ i] (2.26) In order to reduce the processing required if the sample rate is a lot higher than the signal frequency, downsampling reduces the amount of samples that has to be processed. By ap-plying Half-band FIR filter and then downsampling (decimating) the signal, we can keep the information within the boundaries of the Nyquist frequency while minimizing effects from aliasing.

3

Development

This chapter presents the detailed approach to create a simulation platform with focus on the budget analysis simulation tool. After some consideration about the complexity of a communication system in terms in RF circuit design when non-idealities of the real-world components, process variations etc. must be considered, the receiver model is introduced along with the added functionality as required by project description. The way temperature-and frequency-dependency are implemented in the model is detailed. Models for simulat-ing the effect of LO phase noise, mixer M x N products and IQ-imbalance are described as well as models for gain calibration and automatic gain control. The receiver model includes also aspects of digital signal processing (DSP) for complete characterization of the receiver performance. Finally, the simulation platform and the developed templates for link budget simulations and time-domain simulation are presented in detail.

3.1

NI AWR Design Environment

In order to construct a simulation platform, a development platform is needed. One of the purposes of the project, as mentioned, is to examine how well National Instruments AWR Design Environment environment can handle such tasks. The NI AWR Design Environment is an Electronic Design Automation (EDA) software tool dedicated to RF/microwave system design [3]. The platform allows complex high-frequency systems to be modelled, simulated and verified. Software segments like Microwave Office and Visual System Simulator (VSS) are included. Microwave Office is dedicated to the RF/microwave circuit design on compo-nent level and allows nonlinear, frequency- and time-domain analysis, as well as electromag-netic (EM) analysis [4]. VSS is dedicated to system design based on behavioral models.

The advantage of the AWR development platform is that it allows customization of codes (scripts) for desired tasks and functionality. This customization is enabled by the application programming interface (API). The programming languages are popular ones, e.g., C++. This leads to AWR being able to get new functions implemented faster as well as having fewer persistent bugs. Their main competitor, Advanced Design System (ADS), Keysight Technolo-gies, uses their own proprietary language for coding. This has some advantages but put a limitation in the possible grade of customization.

3.2. Receiver impairments

Visual System Simulator

Visual System Simulator (VSS) is the system-level simulation environment included in the AWR software. VSS is dedicated to wireless communication and radar design. It supports measurements of system parameters for cascaded RF blocks, nonlinear behavior and spu-rious effects analysis, and measurement of metrics specific to communication applications, e.g., the Bit Error Rate (BER), [4]. It also supports signal processing in complex, real and dig-ital domain and a combination of these, [4]. As introduced in Chapter 1, the main goal of this project - a practical, user-friendly simulation platform for receiver budget analysis – will make use of the VSS tool due to the customization possibilities that the software allows more than other equivalent tools on the market.

3.2

Receiver impairments

Given the complexity of a communication system and of the signal processing performed within it, a series of non-idealities of the real-world component and design faults may affect the performance of the entire system. A part of these non-idealities were mentioned in Chap-ter 2. Their effects on the signals to be processed should be identified and predicted through simulations. A few of these effects are listed:

• Non-linearity: compression, intermodulation, spectral regrowth • LO-leakage: self-mixing, M x N harmonics

• Noise figure, not optimized: sensitivity, SNR at the output

• Process variations affecting all components in their electrical parameters, matching, etc. • Temperature effects etc.

3.3

Receiver model implementation

This section describes the implementation method of the receiver model and the different features listed in list of requirements in Section 1.2.

As shown in Chapter 1, the main goal of the project is to create a simulation platform with focus on the budget analysis simulation tool for wireless applications. This tool should be user friendly and easily adapted to the company requirements, e.g., allowing parameter simulations of the component models. Budget analysis is a powerful method frequently used in top-level system design. Through system budget simulations, linear and non-linear char-acteristics of the overall system can be determined as a function of the charchar-acteristics of the components in the chain. A typical design cycle includes:

• Receiver modelling as a chain of components (filters, LNA, mixers, etc) according to the actual receiver architecture

• First-hand estimation of the component characteristics that are in parameter form,(gain, compression, noise figure, etc.)

• Budget simulation on top-level, e.g., on receiver level

• Compare the simulation results to the required specifications of the system (receiver) • If specification not met, modify/optimize some components parameters and conduct

3.3. Receiver model implementation From this pseudo flow-chart the results of how important top-level simulations are in the design of complex, wireless systems. Since they comprise of a multitude of circuits operat-ing under different circumstances, e.g., signal levels, frequency etc., and performoperat-ing signal processing of high complexity, these system level simulations are necessary to make sure the system is working as intended.

Homodyne RX

The basic receiver lineup is based on a general homodyne receiver model presented in Fig. 2.8. A system diagram of the receiver lineup is presented in Fig. 3.1 with a few differences. Since VSS supports the use of complex signals on the form S(t) =I(t) +jQ(t)a single mixer acts as an IQ demodulator by itself with the right settings. Another difference is that there is no Variable Gain Amplifier (VGA) in Fig. 3.1. The reason for this design choice was that this feature was not assessed as "basic". This feature will be presented in the next lineup used for the project.

Figure 3.1: Basic homodyne receiver lineup

The receiver lineup implementation for this project however has a few more features in-cluded for a more detailed model. This lineup is constructed based on some of the specifica-tions of an existing 5G radio receiver and the implementation is presented in Fig. 3.2. It uses features such as temperature dependent parameters, losses and VGA’s.

3.3. Receiver model implementation

Figure 3.2: Homodyne receiver lineup with features

Temperature dependency

In order to approximate real system performance, the temperature dependency of certain component parameters needs to be taken into account and subsequently modelled. In a lot of cases, when temperature is static, component properties such as gain/loss behaves the same independent of temperature and can be modelled easily. However, when there is a system that will operate in a wide range of temperatures and the system itself is prone to self heating, the physical structure of the components causes the performance to drift.

In order to ensure correct performance and that the system meets regulatory and/or in-ternally set requirements, the temperature dependency needs to be taken into account.

Temperature variation is implemented with respect to a nominal temperature. The nomi-nal temperature is defined as the arithmetic average of the maximum and minimum temper-ature given in 3.1.

Tnom= Tmin+Tmax

2 (3.1)

where temperatures are defined as either Celsius or Kelvin. For a temperature varying parameter Q, the value is modelled as a function of T

Q(T) =Qnom+ T´ Tnom

Tmax´ TnomQtemp (3.2)

where Qtempis the user specified parameter for temperature variation. To give an example

of how this works, lets say we have an amplifier with nominal gain Gnom = 10 dB and a

temperature varying parameter Qtemp = ´1.2. At T = Tmax, G = 8.8 dB and at T = Tmin,

G=11.2 dB.

The gain/loss variation due to temperature can be compensated for by using a variable gain amplifier that can attenuate/amplify a signal depending on what temperature is in the system. This behaviour can be modelled in AWR by knowing the target gain for two different temperatures and interpolate the gain linearly between these two temperature values. The drift in gain/loss can thereby be compensated for in the model for static (time independent) simulations.

For time domain simulations, it is convenient to use a look up table of pre-measured gains at different temperatures while sweeping temperature values for the system.

3.3. Receiver model implementation

Frequency dependency

As well as in the case above with temperature, components tend to vary in performance depending on frequency. This is can be modelled in various ways in AWR depending on what simulations are being done and what components are being used. Frequency dependency can be modelled linearly, like temperature dependency is modelled in this project, but since temperature dependent data for the different components may be difficult to obtain, it is instead made part of the AGC system by taking the frequency dependent attenuation of the system blocks in S-parameter files. A number of points are placed on the S-parameter curves and then an interpolation is made between these. The power measurement for the AGC is made with regard to S21.

Frequency dependency could be implemented in this way for the entire lineup, but it would require a complete redesign of the lineup as well as lose a lot of what makes VSS simple to perform simulations in due to that the component properties would be complex to change and would have to be specified in non-intuitive ways.

The various filters implemented in the model are also a way of representing the frequency dependency of the system. Frequencies are attenuated differently depending on where on the spectrum they fall while still being propagated through the system.

Process variation

There are two types of process variations implemented. One for each of the simulation plat-forms respectively. This was done because of how the component parameters for the analog lineup are specified in the different platforms. The budget document can perform yield anal-ysis as part of VSS built in functionality, where the user can specify the type of distribution and how much the chosen component parameter should vary Fig. 3.3.

Figure 3.3: Process variation for Gain, P1dB and IP3 for a component in the analog lineup in the budget platform

When performing a yield analysis in VSS, a random number is generated in the specified interval and used as the parameter value. This can be done multiple times as a sweep, leading to a different approach being used in the time domain platform to reduce simulation time.

In the time domain platform, the process variation is specified alongside the other com-ponent values in the "Global Definitions"-folder as seen in Fig. 3.4.

3.3. Receiver model implementation Table 3.1: LO phase noise text file format

(,Hz) (,dB) 100 -80 1e3 -90 10e3 -95 100e3 -100 1e6 -125 10e6 -145

The process variation is then multiplied with a factor K_[...] indicating the maximum, min and normal case for the parameter. This is then added in turn to the component parameter in the analog lineup. This is suitable for the time domain platform since there are no sweeps involved and the most interesting cases of i.e. maximum, minimum or normal gain can be simulated with the manual input of the K_[...] factor. This can be seen in detail in Fig. 3.5.

Figure 3.5: Factor determining max, min and nominal case for process variation.

LO phase noise

Phase noise is modelled in AWR with a text file, a phase noise mask, attached to the LO input tone of the mixer. The text data file contains frequency offset-dBc/Hz data in a format as Table 3.1.

The text file is then applied to a tone source as phase noise as shown in Fig. 3.6.

Figure 3.6: LO phase noise

Mixer M x N products

The mixers inside the IQ modulator outputs a signal on the form (2.7). However, to model the specific behavior of the mixer, the user needs to specify the output levels (dBc) of the intermodulation products on the form (2.8), where m and n are indices in a resulting matrix. In AWR, the user needs to input this matrix in the mixer model as a text file. The AWR implementation of this feature is presented in Fig. 3.7. This example shows a matrix with indices 0x0 to 4x4.

3.3. Receiver model implementation

Figure 3.7: M x X text file as implemented in in the VSS mixer block

IQ imbalance

IQ imbalance models non ideal aspects of the IQ demodulator. It is implemented as a block, containing parameters DC offset, amplitude imbalance and phase imbalance. Units are volts, dB and radians.

Gain calibration

Gain calibration is used as to compensate for process variations and temperature variations in the system. It is also vital for the system to estimate its gain for DSP operations. It uses VGA blocks as variable attenuators. It is therefore important that the "Target gain" is not set to high since we only compensate by attenuation.

Two different gain calibration methods were implemented in AWR. The first model calcu-lates the sum of gain parameters from the RX lineups (3.3) and the total temperature varying parameter (3.4). Gnom= N ÿ n=1 Gn (3.3) Gtemp = N ÿ n=1 Gtempn (3.4)

Process variations are modelled by adding together the statistical gain variations in each element in the RX lineup as in (3.5).

Gprocess= N

ÿ

n=1

Gprocessn (3.5)

Adding together nominal gain, temperature varying gain and statistical gain variations we can determine the total gain by (3.6).

Gtot=Gnom+ T´ Tnom

Tmax´ TnomGtemp+Gprocess (3.6)

Gain calibration was then applied as an attenuation block attenuating the signal as in (3.7). Lcal=

#

Gtot´ GTarget, if Gtotą GTarget

0, otherwise (3.7)

3.3. Receiver model implementation

Figure 3.8: Gain calibration implementation using equations

The second implementation method of gain calibration uses a PID (Product, Integrator, Derivative) controlled gain calibration loop to calculates attenuation by gain compensation before the main simulation starts. The AWR implementation method is presented in Fig. 3.9.

Figure 3.9: Gain calibration implementation using PID controller

Receiver AGC models

Two types of AGC functions were implemented, one equation based feed forward model and one feedback model. The feedback model works exclusively for time domain simulations since it operates on previous samples while the feed forward model was designed to operate in any type of simulations utilizing equations and boundary conditions.

As discussed in Section 2.5, AGC is used to limit signal power going to the ADC utilize higher dynamic range for linear gain. As components exhibit nonlinear behavior to a higher degree with stronger signal power, AGC can be implemented to mitigate some of these ef-fects.

AGC functionality however comes with some inherent problems itself. Looking back at Friis formula for noise (see (2.4) ), we find attenuating the signal in the system increases noise

3.3. Receiver model implementation figure. In the moment we attenuate the signal, we also generate discontinuity in the data stream.

There are other problems that arise with control systems. Stability is one aspect that has to be taken into account since we rely on negative feedback with added delays.

VGA placement in the receiver lineup along with power measurement placement also matter for optimal control.

Feed forward AGC

The feed forward model was implemented using estimation of the output signal power given boundary conditions such as input signal power, frequency and linear gain. This model has the advantage of relieving computational power from simulation run-time but the disadvan-tage of guessing output conditions, giving room for inaccurate behavior. Simulation settings and equations for this model are presented if Fig. 3.10.

Figure 3.10: AGC Feed forward model The estimated output power is given by 3.8.

Pout=Pin+GTarget+H(fc) (3.8)

H(fc)is the filter response of the system at the center frequency input signal and GTarget

the target gain of the system from antenna to ADC. The power is given in RMS. The disad-vantage of this AGC model is that it is difficult to estimate RMS output power from multiple signals with different properties. This AGC does not include hysteresis functionality unlike the feedback AGC.

Feedback AGC

In order to model AGC functionality in the system for time-domain simulations, feedback control can be used instead of manual control.

The receiver feedback AGC model implemented in AWR utilized comparators with hys-teresis built in. In contrast to the regular logical comparator which outputs either True or False depending on if input passes the condition, the comparator with hysteresis utilizes two conditions, one condition for initial state and when the first condition was passed. The elec-trical analog to this behavior is the well known Schmitt trigger design which is described in [1]. The comparator with hysteresis implemented in AWR is presented in Fig. 3.11.

3.3. Receiver model implementation

Figure 3.11: Comparator with hysteresis implementation in AWR

For an AGC, we are interested in power measurement in order to control VGA’s. An AGC controller consisting of four comparators with different threshold levels was implemented in order to control a maximum of four VGA’s. This can be extended to any number of compara-tors. The AGC controller implemented in AWR is presented in Fig. 3.12.

3.3. Receiver model implementation

Figure 3.12: AGC Controller implementation in AWR

Power measurement is important for implementing AGC functionality. In the case for the AGC implementation for the project, the running average of N samples of the incoming RMS power is utilized, where N is user specified. Other user specified parameters for the AGC are hysteresis levels, delay between triggering VGA’s and a start delay for simulation purposes.

3.3. Receiver model implementation

Figure 3.13: Power measurement for AGC controller

ADC model

The Delta-Sigma (∆-Σ) ADC implemented and tested in VSS was a 2:nd order ∆-Σ as indi-cated by the number of integrators and/or the number of feedback loops in Fig. 3.14.

Figure 3.14: VSS schematic of the tested delta sigma ADC

This ADC model was not implemented due to the drastically increased simulation times when included in the receiver lineup for time domain simulations. It was also not compatible with budget simulations and was therefore not used for that platform either.

Digital interface

The digital interface was modelled using frequency transformation, frequency decimation along with FIR filters and a channel filter. A simple model of this is presented in Fig. 3.15.

3.4. Simulation platforms

Figure 3.15: Digital interface with FIR filters

FIR filter implementation

FIR filters were implemented with the help of coefficient based FIR filters. These coefficients were generated in Matlab with the help of built in FIR functions. The resulting coefficients were then exported to the AWR project.

3.4

Simulation platforms

In Section 1.2, the requirements for the simulation tool are presented. In order to cover these requirements, different simulation platforms in the form of system diagrams were imple-mented in AWR, where different aspects of the RF lineups were analyzed. For example, one document was created for analyzing budget parameter, while another had the purpose of analyzing AGC behavior. All these documents are presented below.

Impairment implementation for link budget simulation platform

In order to simulate blocking scenarios, i.e. when undesired signals are received by the sys-tem using budget simulations, functionality not native to VSS had to be implemented. One measurement interesting for this simulation type is degradation. To accurately model degra-dation of a channel in a system, as many receiver impairments as possible needs to be taken into account.

Increase in noise figure due to AGC attenuation can be implemented using available mea-surements built in to VSS. Estimation of phase noise can also be implemented with the help of built in measurements using the values in Table 3.1. IM3 signals generated from two signals can also be estimated using built in measurements of IP3. Undesired spurious response from the IQ demodulator was not as easily implemented however. With the help of scripting using VSS scripting editor, these mixer products can be estimated and taken into account for when measuring Degradation.

ADC impairments such as sampling, aliasing and jitter were not implemented in the link budget simulation platform since these parameters only makes sense in a time domain sim-ulation. The exception being jitter which could be implemented using some more advanced scripting.

Impairment implementation for the time domain simulation platform

Unlike the budget simulation, the resulting degradation can be calculated with the help of power measurements in each channel. The time domain engine takes all impairments into account listed in Section 1.2 except for ADC jitter since a simpler version of the ADC was implemented. This was due to simulation times being affected by the delta-sigma ADC.

3.4. Simulation platforms ADC sampling and aliasing are all taken into account for since finite sampling frequency and decimation are used.

4

Results

The results of the project are presented in this chapter. This includes implemented function-ality along with simulation examples from the AWR project documents.

4.1

Simulation platform results

Functionality

The AWR project ended up containing two different simulation platforms. The core function-ality includes: • Temperature dependency • Frequency dependency • Gain calibration • AGC • IQ imbalance

• User specified M x N mixer behavior • User specified phase noise

• Budget simulation platform

– Gain, Noise Figure, P1dB and IP3 measurements

– Blocking scenarios

– Yield analysis

• Time domain simulation platform

– Digital interface for channel filtering

– Blocking scenarios