Base station design for a wireless microsensor system

Base Station Design for a

Wireless Microsensor System

by

Andrew Yu Wang

Bachelor of Science in Electrical Engineering

University of Maryland, College Park, 1998

Submitted to the Department of Electrical Engineering and Computer

Science in partial fulfillment of the requirements for the degree of

Master of Science in Electrical Engineering and Computer Science

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOC

September 2000

Y MASSACHuSETTS INSTITUTE

OF TECHNOLOGY

OCT 2 3 2000

LIBRARIES

@

Massachusetts Institute of Technology 2000. All rights reserved.

Author .... ... ...

Department

lectrical

Certified by...

BARKER

Engineering and Computer Science

August 31, 2000

...

Charles G. Sodini, Ph.D.

Professor of Electrical Engineering

/

h'esjs:6upgvisor

Accepted by...

Arthur C. Smith, Ph.D.

Chairman, Department Committee on Graduate Students

Base Station Design for a

Wireless Microsensor System

by

Andrew Yu Wang

Submitted to the Department of Electrical Engineering and Computer Science on August 31, 2000, in partial fulfillment of the

requirements for the degree of

Master of Science in Electrical Engineering and Computer Science

Abstract

Wireless microsensor systems are used in a variety of civil and military applications with the objective of detection, classification and/or localization. The main design objective is to minimize the energy consumption of the microsensor node. The design issues involved are quite different from those faced by conventional wireless data and voice applications. In particular, the RF output power is small due to the short trans-mission distances, which make the microsensor transmitter electronics the dominant source of energy consumption.

The research presented in this thesis attempts to bring the circuit and system level issues together to analyze the transmitter energy consumption as a whole. Both the RF output power and the transmitter electronics power are considered, and the energy is minimized on the global level. Three strategies are found to reduce the energy consumption: 1) M-ary modulation, where noncoherent M-FSK is shown to be a good choice; 2) raising the RF output power to reduce the complexity of key transmitter components; 3) coding and diversity techniques. In addition, a digital-IF base station architecture is proposed to maximize design flexibility.

Thesis Supervisor: Charles G. Sodini, Ph.D. Title: Professor of Electrical Engineering

Acknowledgments

The completion of this thesis could not have been possible without the help and support of a number of people. I would like to thank all of my colleagues for providing technical assistance and all of my friends for providing warmth and laughs.

First and foremost, my whole-hearted gratitude goes to my advisor, Professor Charles Sodini, whose insight, guidance, and encouragement have led me this far. I wish the Red Sox will win a big one for you.

Special thanks goes to Snorre Kjesbu from ABB Group. His visits have answered so many of the questions we had regarding wireless microsensor systems.

Appreciation is extended to all my colleagues in the office. SeongHwan Cho's super-sharp intuition has helped me to look into the right issues. Aiman Shabra is extremely helpful whenever I am confused with my derivations. Kush Gulati is always there to argue about whether to have Indian or Chinese. Don Hitko has provided several good opportunities for me to vent my frustrations on tennis balls - hockey style. Dan McMahill's thesis proposal is simply a gold mine. Thanks goes to the rest of the crew who joke about me being the first who did not make a chip: Iliana Fujimori, Susan Dacy, Mark Peng, Pablo Acosta-Serafini, Mark Spaeth, and Ginger Wang.

Many thanks go to my friends who have made MIT a fun place to stay. In particular, Thit Minn has taught me numerous practical ideas in communications theory. His amazing memory directed us to many good restaurants in peculiar places. Irina Medvedev and Anne Pak have provided valuable suggestions on the first draft of this thesis. Mike Neely has always been there with me in the morning work-out, even when his shoulder was hurt. John Rodriguez has been a wonderful roommate, although his rolling pin created quite some confusion for visitors.

Finally, I would like to thank my mom and dad for always being there. Thank you for your support, and for allowing me to explore my own interests. This thesis is for you.

This work is sponsored, in part, by the National Science Foundation Graduate Fellowship, and by the ABB Group.

Contents

1 Introduction

1.1 Wireless Microsensor Systems . . . . 1.2 Design Objective and Approach . . . .

1.3 Thesis Focus . . . .

1.4 Thesis Outline . . . .

2 Base Station Design - System Level Issues

2.1 Transmitter Energy Minimization . . . . 2.2 Binary Versus Multi-level Modulation . . . .

2.2.1 a versus tstart: Using the Basic Assumptions

2.2.2 ce versus tstart: Large ton . . . .

2.2.3 a versus tatart: Large PRF ...

2.2.4 Observations . . . . 2.3 Reducing Transmitter Complexity . . . .

2.4 Reducing RF Output Power . . . .

2.5 Sum m ary . . . . 3 Base Station Design: Architectural Issues

3.1 Direct Conversion Receiver . . . .

3.2 Single-IF Conversion . . . . 3.3 Dual-IF Conversion . . . . 3.4 Digitizing the IF . . . . 3.5 Sum m ary . . . . 17 17 19 . . 20 . . 21 23 . . . . 23 . . . . 26 . . . . 29 . . . . 31 - . . . . .- . . . 32 . . . . 32 . . . . 33 . . . . 36 . . . . 37 39 39 42 43 45 47

4 Detection in White Gaussian Noise Channel

4.1 AW GN Channel ...

4.2 Optimal Detection Theory ... 4.2.1 Matched Filter Receiver ... 4.2.2 Correlator Receiver ... 4.2.3 Maximum Likelihood Receiver ....

4.3 Performance of the Optimal Receiver . . . .

4.4 Sub-optimal Detection . . . . 4.5 Classes of Modulation . . . .

4.5.1 On-Off Keying . . . . 4.5.2 Phase Shift Keying . . . . 4.5.3 Quadrature Amplitude Modulation . 4.5.4 Frequency Shift Keying . . . . 4.6 Sum m ary . . . .

5 Detection in Multipath Fading Channel 5.1 Large-scale Fading . . . .

5.1.1 General Description . . . .

5.1.2 Indoor Environment . . . .

5.2 Small-Scale Fading . . . .

5.2.1 Channel Characterization . . . .

5.2.2 Multipath Delay Spread and Coherent

5.2.3 Coherence Time and Doppler Spread

5.2.4 Frequency-nonselective Slowly-Fading

5.2.5 Rayleigh Channel Modeling . . . . .

5.3 Link Budget Analysis . . . .

5.3.1 Frequency Allocation . . . . 5.3.2 Link Budget . . . . 5.4 Mitigation Methods . . . . 5.5 Sum m ary . . . . Bandwidth Channel . 49 . . . . 50 . . . . 52 . . . . 52 . . . . 53 . . . . 54 . . . . 55 . . . . 60 . . . . 62 . . . . 62 . . . . 64 . . . . 67 . . . . 68 . . . . 72 75 . . . . 76 . . . . 76 . . . . 78 . . . . 79 . . . . 79 . . . . 80 . . . . 81 . . . . 82 . . . . 85 . . . . 85 . . . . 86 . . . . 86 . . . . 90 . . . . 91

6 Simulation Results 93

6.1 Simulation Tools ... . 93

6.2 Complex Envelope Representation . . . . 95

6.3 System Level Model . . . . 95

6.3.1 The Modulator Block . . . . 97

6.3.2 The Channel Block . . . . 97

6.3.3 The Demodulator Block . . . . 99

6.4 Simulation Results . . . . 100 6.4.1 M -PSK . . . . 101 6.4.2 F SK . . . . 101 7 Conclusions 107 7.1 Summary . . . . 107 7.2 Future Work . . . . 109

List of Figures

1-1 A wireless microsensor network . . . . 18

1-2 Top-level system design approach . . . . 21

2-1 A generalized transmitter architecture . . . . 24

2-2 tstart vs. ton for binary and 16-PSK . . . . 26

2-3 & VS. tstart using the basic assumptions . . . . 30

2-4 a VS. tstart large ton . . . . 32

2-5 a VS. tstart large PRF . . . . .- . . . . .. - . . . . . .. 33

2-6 BPSK BER degradation due to static carrier phase error . . . . 34

2-7 BER of noncoherent FSK with frequency error . . . . 35

3-1 Direct conversion receiver and the problem of self-mixing . . . . 40

3-2 Constellation due to phase and gain error . . . . 41

3-3 Single-IF conversion receiver . . . . 42

3-4 Image rejection vs. channel selectivity . . . . 44

3-5 Dual-IF conversion receiver . . . . 44

3-6 Digitization at the IF frequency . . . . 46

4-1 Simplified model of a digital communications system . . . . 50

4-2 The Additive White Gaussian Noise (AWGN) channel . . . . 51

4-3 Autocorrelation and power spectrum of white noise . . . . 51

4-4 Ideal linear demodulator . . . . 52

4-5 Maximizing the inner product . . . . 53

4-7 4-8 4-9 4-10 4-11 4-12 4-13 4-14 4-15 4-16 4-17 4-18 4-19 4-20 4-21 4-22 4-23 4-24 4-25

Maximum likelihood matched filter receiver . . . .

Maximum likelihood correlator receiver . . . .

Signal constellation of binary antipodal signaling . . . . . Error probability calculation based on nearest neighbors

Using Sinc function to perform pulse shaping . . . .

Spectra of raised-cosine filter with various roll-off factor .

M-ary noncoherent receiver . . . .

Signal constellation of on-off keying . . . . OOK noncoherent detection . . . . Signal constellations of BPSK, QPSK, and 8-PSK . . . .

BER curves for M-PSK . . . .

M-PSK Quadrature modulator . . . .

M-PSK Quadrature demodulator . . . .

M-QAM Constellation for M = 4, 16, 64 . . . .

Correlation between two Sinusoids separated by Af . . . M-FSK bit error rate versus Eb/NO . . . .

Direct VCO modulation of MSK signaling . . . .

MSK detection with frequency discriminator . . . .

SNR versus bandwidth efficiency at BER = 10-5 . . . .

5-1 Multipath propagation channel characterization . . . .

5-2 Response of a multipath channel to a narrow pulse . . . . 5-3 Multipath intensity profile and transform . . . .

5-4 Spaced-time correlation function and transform . . . .

5-5 Bit error rate in Rayleigh fading channel . . . . 5-6 Modeling of Rayleigh channel with Doppler spread . . . . 5-7 Link budget analysis for fading channels . . . . 5-8 Transmit power versus bandwidth efficiency in Rayleigh channel 5-9 Techniques for improving SNR in fading channel . . . . 6-1 SPW connects software simulation to hardware implementation

. . . . 54 . . . . 55 . . . . 57 . . . . 58 . . . . 59 . . . . 60 . . . . 61 . . . . 63 . . . . 63 . . . . 64 . . . . 65 . . . . 66 . . . . 66 . . . . 67 . . . . 69 . . . . 70 . . . . 71 . . . . 72 . . . . 73 76 79 80 82 84 85 87 89 91 94

6-2 6-3 6-4 6-5 6-6 6-7 6-8 6-9 6-10 6-11 6-12 6-13 6-14

Simulation model and block diagram . . . . . Basic modulator block diagram . . . .

AWGN Channel Block Diagram . . . .

Continuous versus discrete time representation Rayleigh channel for small Doppler spread 2-PSK BER degradation in AWGN channel 2-PSK BER degradation in Rayleigh channel 4-PSK BER degradation in AWGN channel 4-PSK BER degradation in Rayleigh channel 8-PSK BER degradation in AWGN channel 8-PSK BER degradation in Rayleigh Channel

of signals

Noncoherent MSK BER degradation in AWGN Channel Noncoherent MSK BER degradation in Rayleigh channel

. . . . . 96 . . . . . 97 . . . . . 97 . . . . . 98 . . . . . 99 . . . . . 103 . . . . . 103 . . . . . 104 . . . . . 104 . . . . . 105 . . . . . 105 . . . . . 106 . . . . . 106 A-1 BPSK/QPSK modulator test system . . . .

A-2 Unfiltered and filtered BPSK baseband signals . . . . A-3 Eye-diagram of BPSK signal with raised cosine filtering (a=1) .

A-4 FFT of unfiltered BPSK baseband signal . . . .

A-5 FFT of raised cosine filtered (a = 1) BPSK baseband signal . .

A-6 QPSK/MPSK test system . . . . A-7 Rayleigh channel based on two independent Gaussian generators

A-8 Rayleigh channel based on PMF generation . . . .

A-9 QPSK/MPSK demodulator block diagram [1] . . . .

A-10 GMSK modulator test system . . . .

A-11 GMSK I/Q channels waveforms - Quadrature modulator . . . .

A-12 GMSK magnitude/phase waveforms - FM modulator . . . .

A-13 GMSK (BT=0.5) coherent detection I-channel eye diagram . . .

A-14 GMSK (BT=0.5) coherent detection Q-channel eye diagram . .

A-15 GMSK (BT=0.3) coherent detection I-channel eye diagram . . .

A-16 GMSK (BT=0.3) coherent detection Q-channel eye diagram . .

112 113 113 114 114 115 116 116 117 118 119 119 120 120 121 121

A-17 Noncoherent MSK test system . . . . 122

A-18 MSK frequency discriminator demodulator . . . . 123

A-19 MSK frequency discriminator output waveforms . . . . 124

A-20 Frequency discriminator output eye diagram (BW=0.5/T) . . . . 124

List of Tables

1.1 Wireless microsensor system specifications . . . . 2.1 Comparison of RF output power and bandwidth occupancy 2.2 Summary of variables for Equations (2.4) and (2.5) . . . .

2.3

2.4

2.5 2.6

RF output energy versus transmitter energy . Energy savings based on Figure 2-3 . . . . Energy savings based on Figure 2-4 . . . . Energy savings versus transmitter complexity

20 27 28 29 31 31 36 47 67

3.1 DSP and ASIC/FPGA task allocation chart

4.1 Bandwidth efficiency of M-PSK signaling .

5.1 Summary of variables for Equation (5.1) . . . . 77

5.2 Summary of typical path loss exponent values . . . . 77

5.3 Summary of typical path loss data for indoor environment . . . . 78

5.4 FCC restrictions on U-NII Band . . . . 86

5.5 Assumptions used in the link budget analysis . . . . 88

5.6 Link budget analysis results . . . . 88 6.1 Summary of variables for Equation (6.4) . . . .

7.1 Energy minimization trade-offs . . . .

96 108

Chapter 1

Introduction

The wireless communications market has experienced an explosive growth in the past decade. There were over 160 million cellular phone handsets sold in 1998 [2]. The sales of mobile communications equipment and services for the European market was estimated to be 30 billion dollars in the same year [3]. In addition, other wireless ap-plications such as Wireless Local Area Networks (WLANs), Global Position Systems

(GPS), and Personal Communications Services (PCS) have grown as rapidly.

This rapid growth in the commercial market has generated a tremendous amount of research interest in radio frequency (RF) technology. In particular, as portable battery-powered devices become more ubiquitous, there is an ever increasing demand in low power and low cost design methodologies. At the Massachusetts Institute of Technology, the ultra low power radio project is a collaborative research effort whose goal is to investigate and develop novel circuit techniques and system architectures for wireless microsensor systems.

1.1

Wireless Microsensor Systems

Wireless microsensor systems are used in a variety of civil and military applications with the objective of detection, classification, and/or localization. Some examples include security monitoring, machine diagnosis, and chemical or biological detection. As shown in Figure 1-1, such a system is composed of numerous energy-constrained

sensor nodes and a much smaller number of high-powered base stations

[4].

The sensors collect data and send them to the base stations for processing.

o~ 00 0 0 00 00 00 00 0 >00 000 00 0 \f' 0 0l 0 sensor node 0) 000

Figure 1-1: A wireless microsensor network

The wireless microsensor system is an emerging market technology that is quite distinctive from both conventional voice and data applications. The following section discusses its unique features and how they affect design choices.

" High cell density - A wireless sensor network contains as many as several

thou-sand sensor nodes within a small area. Thus, they provide both extensive spatial coverage and significant fault tolerance. However, this imposes a challenge in the design of energy and bandwidth efficient multi-access schemes.

" Ad-hoc distribution - Spatial distribution is ad-hoc, and each sensor may have a very different transmit path. This means some sensors could have line-of-sight (LOS) transmission while others might be totally obstructed from the base station. This not only creates difficulty in estimating the transmit power but also increases the dynamic range of the received signal.

" Ease of deployment - Sensor nodes should require minimal installation and

virtually no maintenance. This implies that the protocols have to be simple as well as highly reconfigurable.

" Low mobility - Sensors are confined to a small area, so they are either static or

are restricted in mobility. This means that a slow fading environment with low Doppler spread is expected.

" Low data rate - The data rate is typically as low as a few kilobytes per second, and each data packet may contain only a few hundred bits. This favors a duty-cycled bursty transmission scheme where the transmitter is turned off most of the time.

" Low latency - Packets are required to arrive at the base station within a small time delay. This puts a restriction on the maximum delay of the bursty transmis-sion scheme. In addition, error correction protocols that require retransmistransmis-sion are clearly unfavored since they will increase delay.

" Short transmission distance - Typical transmission distance is tens of meters. The transmit energy is small enough that the sensor node electronics become the dominant source of energy consumption. As will be explained in Chapter 2, this characteristic plays a key role in our design approach.

" Asymmetric data link - Only one-way communication from the sensor to the

base station (uplink) is required. Base station to sensor communication (down-link) is used only for synchronization purposes.

" Volume constraint - The sensor is required to be compact, which imposes severe constraints on transmitter complexity.

1.2

Design Objective and Approach

The ultimate goal of the low power radio project is to maximize the battery life of the sensor nodes while complying with all the other requirements stated above. Sensor transmitter power consumption is the bottle-neck since the system lasts only as long as the sensors do. Table 1.1 shows detailed specifications for a system that monitors machine operations in a factory environment (provided by ABB Co.). This system is

chosen as a design example because it presents some very interesting design challenges and trade-offs. In particular, the battery life span of 5-10 years implies that the total transmitter power has to be kept in the milliwatt regime. At this time, no commercial solution is known to satisfy this requirement.

Cell density 200 - 300 in 5mx5m area

2000 - 3000 nodes in 100mx100m area

Range of link < 10m

Message rate average: 20 msgs/sec (msg = 2bytes) maximum: 100 msgs/sec

minimum: 2 msgs/sec

Error rate 10-6 after 5ms

and latency 10-9 after 10ms

10-12 after 15ms

Battery life 5-10 years

size _{one AA size battery}

Table 1.1: Wireless microsensor system specification for machine monitoring applica-tions

In order to achieve the above specifications, energy efficient solutions must be found at all levels of abstraction. Figure 1-2 shows the key design tasks in this collaborative project. On the system level, energy and bandwidth efficient multi-access protocols, multi-level modulation schemes, and coding/diversity techniques are considered. On the architecture level, novel transmitter and base station architectures are explored. On the circuit level, various low power, low noise, and high sensitivity circuitry are investigated.

1.3

Thesis Focus

The focus of this thesis includes the bold-lettered sections shown in Figure 1-2. The objective of this thesis is to explore base station receiver design methodologies that help the transmitter (i.e., the sensor) to achieve energy minimization. This can be accomplished on both the system and architecture levels. On the system level, various modulation schemes are studied and suitable modulation/demodulation techniques

WIRELESS MICROSENSOR SYSTEM

System: Architecture: Circuit:

-multi-access -transmitter -low power

-TDM/FDM/hybrid -overall planning -high sensitivity

-modulation -fast start-up FS

-OOK/PSK/FSK -receiver

-binary/M-ary -architecture choice

-coding/diversity -wideband ADC

-digital demodulator

Figure 1-2: Top-level system design approach

are suggested. On the architecture level, a wideband digital-IF receiver architecture is chosen based on an extensive study of various existing receiver architectures.

1.4

Thesis Outline

The remaining chapters of this thesis present further analysis and details of the project. Chapter 2 presents research results on modulation techniques with a focus on energy minimization. Chapter 3 develops a receiver architecture that is suitable for wireless microsensor systems. Chapter 4 analyzes various modulation schemes in additive white Gaussian noise (AWGN) channel. Chapter 5 introduces the multipath model, which is more appropriate for the wireless environment, and suggests remedies against fading loss. Chapter 6 details simulation approach and discusses the results. Chapter 7 summarizes the project and suggests areas of future work.

Chapter 2

Base Station Design

-

System

Level Issues

This chapter explains a unique base station design methodology we have developed, which we call the global energy minimization approach. The goal of the thesis, as mentioned in the introduction, is to explore base station design methodologies that help the transmitter to achieve energy minimization. Specifically, the global trans-mitter energy consumption equation is examined to find the most relevant system and architectural issues that affect the design of the base station. Since the base station has no limitation in power consumption or complexity, all design trade-offs are leveraged toward those that reduce the transmitter energy, which is taken as the main design criterion.

Much of the issues discussed in this chapter are built upon the results derived in later chapters. For readers who are not familiar with wireless communications concepts, Chapter 4 is a good place to start. The readers may return to this chapter after browsing through Chapters 4 and 5.

2.1

Transmitter Energy Minimization

Figure 2-1 shows a generalized transmitter architecture. The baseband modulator performs constellation mapping and spectral shaping. The baseband output signal

is modulated up to the carrier frequency, or RF, by the frequency synthesizer. This RF signal is then amplified by the power amplifier (PA) and transmitted through the antenna.

Baseband

Modulator P

Synthesized LO

Figure 2-1: A generalized transmitter architecture

The raw data rate for a microsensor system is low, typically a few kbits/s, so the transmitter employs a burst transmission scheme. The transmitter is on only for a short time during which the accumulated data is sent at a high rate. Based on bandwidth availability, the symbol rate is set at lMsymbols/s.

Since the transmitter is duty-cycled, average energy dissipation per cycle is used as a performance metric. This energy dissipation is given by

Et=t = Estart + Eon = Pstart - tstart + Pon - ton (2.1)

The total transmitter energy dissipation is composed of two components: Estart,

which is the energy dissipation during the start-up phase, and Eon, which is the

energy dissipation during the on-time (i.e., when the transmitter is sending data).

Pstart is the average power dissipation during the start-up phase, and tstart is the

time duration of the start-up phase. During tstart, all transmitter electronics are off

except the frequency synthesizer. The start-up phase is complete when the frequency synthesizer settles to the desired RF frequency. Therefore, Ptart is simply the average

power of the frequency synthesizer, PFS,

Pstart = PFS (2.2)

As shown in Figure 2-2, tstart is significant compared to t0n; thus, minimizing the

start-up time is a key to reducing total energy dissipation. An important research topic in the wireless microsensor system project is the design of a fast turn-on fre-quency synthesizer. It has been shown that by applying novel design techniques, the turn-on time of the frequency synthesizer can be kept below 10ps [4].

ton is the total on-time, and Pon is the average power dissipation during on-time.

It can be written as

Pon = PE + PRF (2.3)

where PE is the average on-time electronics power, and PRF is the RF output power.

In order to achieve minimum energy dissipation, Etot must be minimized as a whole. Clearly, transmitter design affects Ptart, tatart, and PE. The question that this thesis attempts to answer is: can the base station receiver design help to reduce any of these terms? The answer is yes, and it lies in the system-level issues. The following three strategies are found to affect the trade-offs between PE, PRF, and

ton-" Multi-level modulation decreases ton at the expense of increased PE and PRF.

Appropriate trade-offs can result in a reduced Eon.

" Increasing PRF may lower the performance requirements of certain critical

trans-mitter components, which in turn reduces PE. When transtrans-mitter electronics are

the dominant source of power dissipation, the savings in PE can offset the extra cost in

PRF-" Coding/diversity techniques reduce the RF output power. These techniques are

especially effective against fading loss in a multipath environment.

As expected, the trade-offs mentioned above are inter-connected, and the rela-tionships among them are complex. Traditionally, design issues on the circuit level

are separated from those on the system level. In this project, an attempt is made to tie all of the above strategies into a simplified but revealing relationship. The goal is to bridge the circuit issues to the system issues so that a global energy minimization solution may be found. The analyses in the following sections are tailored toward wireless sensor systems, but the same techniques apply to any RF system.

2.2

Binary Versus Multi-level Modulation

Based on the specifications shown in Chapter 1, the data rate for a machine moni-toring application is about 5.Okbits/s ( 2 bytes/mesg * 100 mesg/s + overhead). The

transmitter is turned on every 5ms (200 sensors per cell time-division multiplexed), and the transmission rate is lMsymbols/s. With these specifications, a comparison

of tstart and to, for binary modulation and 16-PSK is given in figure 2-2. Note that 16-PSK reduces to, by a factor of 4, which can potentially reduce Eon.

Binary Modulation 16-PSK

t t t

start on start on

(-Ous) (-25us) (-1Ous) (-6us)

Figure 2-2: tstart vs. to, for binary and 16-PSK

In general, M-ary modulation reduces ton by a factor of r =log2 M. The cost of

this reduction is an increase in PE and either PRF (in the case of PSK and QAM) or

bandwidth (in the case of FSK). Table 2.1 shows the RF output power and bandwidth occupation for various modulation schemes.

In Table 2.1, BW is the minimum bandwidth required to satisfy the Nyquist criterion. -y is the RF output power normalized to that of 2-PSK. The table shows how much extra RF output power is required for each modulation scheme as compared to 2-PSK.

Clearly, the modulation schemes are divided into two distinct classes: 1) M-PSK and M-QAM are bandwidth efficient modulation schemes whose applicability is

lim-Modulation r BW(MHz) PRF(mW)

I

2-PSK 1 1 .56 1 4-PSK 2 1 1.12 2.0 8-PSK 3 1 2.79 5.0 16-PSK 4 1 8.80 15.7 16-QAM 4 1 17.2 30.7 64-QAM 6 1 90.0 161 2-FSK* 1 1 2.24 4.0 4-FSK* 2 2 2.80 5.0 8-FSK* 3 4 3.30 5.9 16-FSK* 4 8 3.80 6.8 (*) noncoherent demodulation

Table 2.1: Comparison of RF output power and bandwidth occupancy for various modulation schemes

ited by the prohibitive increase in PRF; 2) M-FSK is a power efficient modulation scheme whose applicability is limited by its excessive demand on bandwidth.

In addition to the increase in PRF or bandwidth, M-ary modulation puts more

stringent demands on transmitter electronics performance. For M-PSK and M-QAM, the frequency synthesizer now has to contain a quadrature VCO, which increases its

power by a large proportion. Any distortion in the constellation causes more severe performance degradation. Quantization error in the D/A converter in the baseband modulator, phase noise of the VCO, and non-linearity of the power amplifier must all be reduced. For M-FSK, the frequency synthesizer must also have a wide tuning range, which increases the noise power in the loop bandwidth. Signal power must be increased correspondingly to maintain the same SNR.

In order to compare the overall effects mentioned above, Equation (2.1) is rewrit-ten in the form of (2.4) and (2.5). This was first proposed by SeongHwan Cho [4]. Em is written in terms of the variables used in EB for the purpose of easy comparison. The Greek alphabets represent the extra overhead energy, or the cost, required for M-ary modulation systems. The variables used are summarized in Table 2.2.

Em -- PFS tstart + (PB + OPFS + 7PRF) ' ton(r

EB: energy dissipation for transmitter using binary modulation

EM: energy dissipation for transmitter using M-ary modulation PFS: frequency synthesizer power for binary modulation

PB: _{transmitter electronics power (minus frequency synthesizer power)} for binary modulation

PRF: RF output power for binary modulation

tstart: time interval of start-up phase

to,: time interval when the transmitter is sending data

a: overhead in the transmitter electronics power (minus the frequency synthesizer power) when M-ary modulation is used.

#3:

overhead in the frequency synthesizer power when

M-ary modulation is used.

-y: overhead in the RF output power when M-ary modulation is used.

r:

#

_{of bits per symbol= log} 2 M

Table 2.2: Summary of variables for Equations (2.4) and (2.5)

In Equation (2.4), the total transmitter electronics power, PE, is written as

PE PFS + PB (2.6)

where PB includes all the transmitter electronics power, including the baseband mod-ulator, mixers, etc. except the frequency synthesizer power PFS. PFS is isolated because the frequency synthesizer is the dominant source of power dissipation. Un-like other typical RF applications, the power amplifier is not the dominant source of power dissipation in wireless microsensor systems due to short transmission distance. As shown in Table 2.3, the RF output energy as a fraction of the total transmitter energy dissipation is, indeed, quite small.

M-ary modulation is more energy efficient than binary modulation when EI

<

EB. Applying Equations (2.4) and (2.5) we arrive at a condition on the overhead energy a as follows: r'P

EF

/1 RF a < r + (1 - )tstart + (1- -)t on + (r--) PB ton t r PB (2.7) (2.5)

Modulation

I

EQut/Em 11 Modulation I EOut/Em

2-PSK 3.4% 2-FSK 12%

4-PSK 3.1% 4-FSK 7.3%

8-PSK 6.0% 8-FSK 7.2%

16-PSK 15% 16-FSK 7.1%

Table 2.3: RF output energy as a fraction of the total transmitter energy dissipation, assuming PFS = 10mW, PB = 2mW, a =2-3,

#

= 1.75, tstart = 10ps, and t,,,, = 25ps

The above equation states that in order for M-ary modulation to be more energy efficient, a has to be less than the quantity on the right hand side of Equation (2.7). This puts a cap on the complexity of the transmitter circuitry.

The difficulty in evaluating Equation (2.7) lies in that the variables a,

/3,

PFS, PB, and tstart are system parameters that depend on implementation details. At this time, there is no experimental data available for these variables. However, reasonable assumptions can be made to get good interpretations on Equation (2.7). Once experi-mental data is available, the equation can be evaluated easily. The basic assumptions

are: PFS = 10mW, PB = 2mW,

#

= 1.75, tstart = 10ps, and ton = 25ps. The

as-sumed PFS, PB, and tstart values are aggressive as compared to what are commercially available. These numbers are what we intend to achieve with our design.

2.2.1

a

versus tstart: Using the Basic Assumptions

Figure 2-3 plots a vs. tstart for various modulation schemes based on the above assumptions. Because 16-QAM is less efficient than 16-PSK in a Rayleigh channel, it is not included, and only 16-PSK is considered. 64-QAM is also excluded because it consumes too much RF output power for the moderate gain in bandwidth efficiency. In fact, the 64-QAM curve is below a = 0, which means that 64-QAM will consume

more energy than 2-PSK even if the 64-QAM transmitter electronics (everything except the frequency synthesizer) consume no power.

As shown in the figure, energy savings for M-ary modulation decrease as tstart increases. This makes intuitive sense because when tstart is long, the start-up energy

of the frequency synthesizer dominates, so the energy savings gained through the

reduction of to, are negligible. As tstart becomes shorter, the on-time energy

dissipa-tion becomes the dominant term, so reducing t0, through M-ary modulation achieves

significant energy savings. Therefore, reducing tstart not only decreases the start-up energy Estart but also helps M-ary modulation to reduce the on-time energy E,".

PFS=0 mW, P3 =2mW, 5=1.75, t =25ps 10 -- - - - - 6-PSK 16-FSK 8 . . .8 PSK :.. . . . .. ... 8-F$K\ 4 -4-PSK 2 . -.. -. -.-..-. .- 4-FSK 0 10 10 102 start

Figure 2-3: a vs. tstart using the basic assumptions

The second important observation is that M-FSK becomes more efficient than M-PSK at large M. M-FSK is not as energy-efficient at small M because noncoherent detection requires 6dB more RF power to achieve the same BER performance. For large M, the symbol SNR required for M-PSK grows very fast, which offsets the energy savings gained through reduction of ton. The symbol SNR required for M-FSK grows slowly, thereby making it very energy-efficient at large M. This makes M-FSK attractive since M-FSK already has the advantage of not requiring carrier synchronization.

Table 2.4 shows the energy savings achieved by M-ary modulation at tstart = 10ps for various a values. "/" means that M-ary modulation consumes more energy than 2-PSK at that particular a. Clearly, 16-FSK out performs all the other modulation

schemes. a 2 3 4 5 4-FSK

/

/

/

/

4-PSK

/

/

/

/

8-FSK 7.8% 3.8%

/

/

8-PSK 8.8% 4.8% .76%

/

16-PSK 12% 9.0% 6.0% 3.0% 16-FSK 20% 17% 13% 10%

Table 2.4: Energy savings based on Figure 2-3 (tstart= ₁ uPs, tstart = 25ps)

2.2.2 a versus tstart: Large ton

It is evident that the amount of energy savings depends on the ratio ton/tstart. The

larger this ratio is, the greater the savings. To verify this observation, Figure 2-4 shows the scenario when t,, is 100ps, which is 2-4 times greater than what was assumed previously. This happens if the amount of transmit data is increased. It is seen that the curves are shifted to the right as compared to Figure 2-3. This means that at any given a, energy savings become greater. As shown in Table 2.5, energy savings have increased by a factor of 2 or greater.

a 2 3 4 5 4-FSK

/

/

/

/

4-PSK 3.7%

/

/

/

8-FSK 26% 21% 16% 11% 8-PSK 27% 22% 18% 13% 16-PSK 31% 28% 24% 20% 16FSK 40% 37% 33% 29%

12

10

8

10

tstr (s)

Figure 2-4: a vs. tstart : large ton

2.2.3 a versus tstart: Large PRF

Now consider what happens when the RF output power has to be increased. This can be due to an increase in the transmitter-receiver distance, or that more RF power has to be added to combat multipath fading.

Figure 2-5 shows the case when PRF = 2.24mW, which is 4 times greater than

what was assumed previously. Clearly, M-ary modulation becomes out of favor. Only

16-FSK produces any significant savings at tstart = 10ps. This is because -y grows

faster than r, and when PRF is significant, the actual RF output power, PYPRF, is too

large even if to, is reduced by a factor of r.

2.2.4

Observations

M-ary modulation achieves the greatest energy savings when the ratio ton/tstart is

large and PRF is small (relative to PFS and/or PB). Since to, is usually determined

by the data rate, it is important to minimize tstart and PRF to make M-ary modulation

even more energy efficient.

PFS= 0mW, PB =2mW, s=1.75, t =100ps 16 FSK - ~~~165--PSK-8-.-S.K 8-FSK 4 PSK 4-FSK 4 2-01 100 10 2

PFS=1OmW, PB =2mW, PRF =2.24mW 0=1.75, to=25ps 12-10' ... 8+P.K.S. 8-FSP 4-PSK

Figure 2-5: a vs. ttari, : large PRF

It has also been shown that noncoherent M-FSK for M > 8 out performs M-PSK in terms of energy savings; the sacrifice, however, is bandwidth. For instance, 8-PSK uses 4 times as much bandwidth as M-PSK. This problem may be circumvented by careful planning of the spectrum. In the unlicensed band in the GHz regime, large bandwidth is available to make M-FSK a realistic option.

2.3

Reducing Transmitter Complexity

The transmitter electronics power, PE, can be lowered by reducing the performance requirements of critical transmitter components - for example, phase noise require-ment of the VCO and frequency offset error of the frequency synthesizer.

The phase noise of the VCO and the frequency offset error of the frequency syn-thesizer create two concerns. The immediate impact is degradation of performance in terms of bit error rate. A phase tracking error occurs due to phase noise, frequency error, and non-ideal frequency response of the phase-locked loop, in addition to I/Q mismatch created by quantization and gain errors. Figure 2-6 shows the effect of

4-PSK 4-FSK - 8-PSK - - 8-FSK - 16-PSK - - 16-FSK -7 -,- ... .... . 2 8 4 2 100 10, t,(Ps)

accumulated phase tracking error on the BER of binary PSK. Note that the BER performance worsens for large phase error.

BPSK BER Degradation Due to Static Carrier Phase Error

10 - : -I

0

E b/N (dB )

1. ..

Figure 2-6: BPSK BER degradation due to static carrier phase error

The second concern, which may be more serious, is that large phase error caused by phase noise and frequency error can potentially cause the carrier tracking loop to lose lock. This problem is exacerbated in a fading channel where carrier synchronization is usually a difficult task. Dan McMahill has studied the locking performance of coherent MSK and has shown that in MSK, the modulation index error has to be kept below 5% to achieve a reasonable RMS phase tracking error if an aggressive carrier tracking loop bandwidth of approximately 1% of the symbol rate is used

[51.

This is a very stringent restriction. For example, the Digital Enhanced Cordless

Telecommunications (DECT) standard specifies a 10% accuracy in modulation index, which is not adequate for use with coherent detection.

In light of the above observation, noncoherent detection provides an attractive alternative since it does not require carrier phase tracking. Figure 2-7 shows the effect of frequency error on noncoherent binary FSK. p is the normalized frequency error and is defined as p = feT, where

f,

is the actual frequency error, and T is the

symbol period.

10.

BER of noncoherent FSK with frequency error

... ...._{... ... ... ...: ... . ... ... ...} ... ... ... ... ... ... ... ... .... ... ... ... ... ... .... .. ... ... ... .. . ... ... .. ... .... ... ... ... . ... ... .: . ... . . ... .. ... .. . ... ... ... ... . ... ... ... p=0.2 ... .. ... ... ... ... ... ... ... ... _{.. ... .. ... .} ... .. ... .... . ... ...* , * -* .. ... ... ... ... ... .. ... ... .... ... ... ... . . ... ... .. ... ... ... ... .. ... ... .... ... .... ...- - - ... I ...: ....I ... ... ...: ... ... . ... ... ... ... ... ...I ... .... ... ... ... . ... . ... P= O .l ... ... ... .... ... . ... _... ...* ... ... ... . ... .. ... ... ... .. ... ... P = .... ... ... ... .... ... .... ... .... . ... ... ... _{... ...} ... ... ... 1 1 -.... ... ... .... ... - ... . ... ... .... ... ... . . .. . . ... 0 11 12 13 14 15 16 17 Eb/NQ (dB)

Figure 2-7: BER of noncoherent FSK with frequency error

As shown in Figures 2-6 and 2-7, performance degradation is not severe even for moderately large phase and frequency errors. It takes about 2dB of Eb/No to compensate for a phase error of 400 in PSK or for a frequency error of p = 0.1 in FSK, which corresponds to a 20% modulation index error for MSK.

This suggests that it is possible to reduce the transmitter energy consumption

by increasing the RF output power to compensate for more relaxed phase noise and

frequency error requirements. Specifically, Equation (2.5) is modified in the following way,

Em = (1 - 6)OPFS t tstart + (OZPB + (1 - 6)OPFS + (1 + IQT PRF) - ton /r (2.8)

where 6 is the reduction in the frequency synthesizer power due to relaxed phase noise and frequency error, and i represents the RF output power increase that compensates

10-1

10-2

the BER loss. The overall energy consumption is lowered if

6> NYPRFton (

- /PFS(tstart + ton (29

For a PRF increase of 2dB (s = _{58%), Table 2.6 shows energy savings of Equation} (2.8) over Equation (2.5) as a function of 6. Note that the energy savings do not

depend critically on the modulation level M.

1 5% 10% 115% 20% [25% 2-PSK 1.7% 4.8% 7.9% 11% 14% 2-FSK

/

0.42% 3.3% 6.2% 9.1% 4-FSK

/

3.0% 6.2% 9.3% 12% 4-PSK 1.9% 5.1% 8.4% 12% 15% 8-FSK

/

3.1% 6.2% 9.4% 12% 8-PSK 0.5% 3.6% 6.8% 9.9% 13% 16-PSK

/

/

2.3% 5.4% 8.4% 16-FSK 0.1% 3.4% 6.7% 9.9% 13%

Table 2.6: Energy savings when modulation power is increased to reduce transmitter complexity

2.4

Reducing RF Output Power

As shown in the last section, the RF output power is a small fraction of the total power consumption. It may seem that reducing the RF output power will not produce significant energy savings. However, there are two good reasons why the RF output power should be minimized. First, as shown previously, reducing the RF power will increase energy savings when ary modulation is employed. For PSK and

M-QAM, RF power increases dramatically for large M. This offsets the energy savings

gained through the reduction in to,.

The second reason is that at very low BER, which is what the wireless microsensor system requires, the RF output power becomes prohibitive without any coding and diversity techniques. For instance, in order to achieve an error rate on the order

of 10-9, Eb/No must be about 90dB for an uncoded system in a Rayleigh channel, while only 50dB is required to achieve an error rate of 10-5_{. Thus, coding, diversity,} and retransmission schemes must work together to keep the transmit power at the mW level. Effective coding, diversity, and retransmission schemes are currently being investigated.

2.5

Summary

Several useful results are presented in this section. Equation (2.5) is the global en-ergy equation that governs the total transmitter enen-ergy dissipation. Equation (2.7) can be used to determine whether M-ary modulation is more energy efficient than

binary modulation. Analysis shows that M-ary modulation achieves maximum

en-ergy savings for large ton/tstrt and small RF output power. In addition, for M > 8,

noncoherent M-FSK is more energy efficient than M-PSK. Equation (2.9) can be used to determine the effect of trading off higher RF output power for reduced transmitter complexity. These formulas are simple enough to provide a quick estimate of various design trade-offs. In addition, It has been shown that coding and diversity techniques have to be employed in order to keep the error rate at a negligible level (10-).

Chapter 3

Base Station Design: Architectural

Issues

This section shifts the focus of base station design from the system level to the archi-tectural level. The main concern here is high sensitivity and reconfigurability. High sensitivity reduces SNR loss as well as distortion, and reconfigurability allows more design freedom on the system level. The goal is to choose a receiver architecture that offers the best compromise between hardware complexity and system flexibility. So-lutions are proposed for both the RF front-end and the demodulator that follows. We begin the chapter by examining three architectures that are seen as viable solutions: direct conversion, single-IF conversion, and dual-IF conversion.

3.1

Direct Conversion Receiver

Direct conversion receiver is the focus of much research interest in recent years [6, 7, 81.

The main advantages for direct conversion receivers are higher level of integration and lower power dissipation. Although this architecture has existed since the 1920s, several technical challenges have put severe limitations on its performance at high RF. These challenges are being solved recently, and direct conversion is enjoying a revival. It has been the prevalent technology in paging applications. Now it is being implemented for high performance cellular applications as well.

Figure 3-1 shows the architecture of a direct conversion receiver. The RF signal is down-converted directly to baseband, hence the name direct conversion. This eliminates off-chip band-pass ceramic and SAW filters and thus, makes monolithic integration possible. LO Leakage AMP ILPF A/D --BAND ---- - --- _OLOI BASEBAND output BPF DEMOD Interferer AMP Leakage _L 0 RF LOI

Figure 3-1: Direct conversion receiver and the problem of self-mixing

Low part counts, low power, and high integration make direct conversion receivers attractive in portable applications. However, a big disadvantage is that they do not provide the level of performance that super-heterodyne receivers do. This is due to several draw backs, which are described below.

The most severe problem is due to self-mixing and consequent parasitic DC offset

[9]. As shown in Figure 3-1, self-mixing occurs due to either local oscillator (LO)

leakage or interferer leakage. Since isolation between LO port, input of mixer, and the LNA is not infinite, leakage occurs through capacitive and substrate coupling

[3]. It is also possible that the LO signal leaks to the antenna, is radiated, and is

then reflected back to create a time-varying self-mixing. Due to the large signal gain from the antenna to the ADC (typically 80-100dB), the DC offset can potentially saturate the ADC. In addition, for M-PSK and M-QAM, most of the signal power is

concentrated around DC; thus, the signal will be corrupted by the DC offset even if the ADC does not saturate. DC offset cancelation is a very challenging task. One technique that mitigates this problem is to encode the signal so that it contains little energy at DC. FSK is a popular modulation scheme for direct conversion receivers because its spectrum contains relatively little DC power [8].

Several other drawbacks of direct conversion receivers are rejection of out-of-channel interferer, I/Q mismatch, even-order distortion, and flicker noise. In di-rect conversion receivers, active low-pass filters are used in place of passive filters to provide better integration. However, since active filters exhibit much more severe noise-linearity-power trade-offs than their passive counterparts, rejection of out-of-channel interferer is more difficult. I/Q mismatch is caused by errors in the 90' phase shifter and any mismatches between the amplitudes of the I and

Q signals. Since I/Q

separation is done at the RF frequency, the signals are very sensitive to mismatches in the parasitics. This results in a distorted signal constellation and hence a higher error rate.

Distorted

Ideal o 0.-oA

Figure 3-2: Constellation due to phase and gain error

Even-order distortion and flicker noise are two more problems caused by circuitry non-idealities. The combined effect of all the drawbacks mentioned above makes it difficult for direct conversion receivers to achieve the kind of high-level performance heterodyne receivers have to offer.

3.2

Single-IF Conversion

Most receivers today employ the heterodyne architecture that translates the RF signal first to an intermediate frequency (IF) and then down-converts it to baseband. This reduces or avoids all of the disadvantages associated with the direct conversion receiver and thus improves system performance significantly. Two variations are commonly employed in today's transceivers. The first is single-IF conversion, and the second is dual-IF conversion.

As shown in Figure 3-3, the single-IF conversion receiver converts the desired signal from RF to IF through local oscillator LO1. Assuming the RF signal is a(t)-coS(wRFt), the signal appearing after the mixer is

=a(t)

a(t) - coS(wRFt) - cOS(WLolt) (tCOS(wRF - LO1)t + cOS(WRF + WLO1)t (3.1)

Thus, the baseband signal a(t) is frequency shifted to WIF WRF - WLO1 and WRF +

WLO1. A bandpass filter selects only the signal at IF, which is then down-converted

to baseband through the I/Q separation approach employed in a direct conversion receiver.

BAND IMAGE CHANNEL

SELECT LNA REJECT SELECT AMP

BPF BPF

XW

BPF duato r

demodulator

L1,0

0 (01F (0M CLOl IORF

Figure 3-3: Single-IF conversion receiver

image rejection and channel selectivity. Assuming that a signal is situated at WIM

WLOI - WIF before the mixer, the mixer translates this signal to

b(t) - COS(WIMt) - cos(wLt) (3.2)

b(t)

- 2 [COS(WIM - WLO1)t + COS(WIM + WLO1)t] (3.3)

b(t )

- 2 [COS(WIFt) + COS(WIM + WLO1) (3.4)

Thus, b(t) appears at the IF frequency as well. For this reason, the band at WIM is called the image of the RF signal. The image appears as interference to the desired signal and has to be reduced sufficiently through an image-reject filter.

To understand the trade-off between image rejection and channel selectivity, con-sider Figure 3-4 [9]. Clearly, image rejection improves as IF increases, since the image moves further away from the signal. However, the downside of high IF, or better im-age rejection, is reduced channel selectivity, since a high IF results in a much higher

Q

requirement on the channel select filter. The upper part of Figure 3-4 shows the high IF scenario, where the image is well rejected but the nearby interferer is not due to limited

Q.

The lower part of the figure shows the scenario for low IF, where the image is not adequately rejected, but the interferer is since channel selectivity is better. This conflict can be mitigated by adding an additional mixing stage.

3.3

Dual-IF Conversion

In dual-IF conversion receiver, a second mixer is added to down-convert the signal to a second IF. The first IF is high enough to provide good image rejection and improve the noise figure. A channel selection filter with modest

Q

requirement is placed at the first IF to provide a partial channel selection. The first IF is then converted to a low second IF, where precise channel selection can be achieved. Although the image problem also exists for the second IF, the frequency is low enough that the channel selection filter provides adequate rejection. Since the filters at each stage suppresses adjacent channel interference to some extent, the linearity requirement of

Image Reject Filter Image 2 IF

+

Channel Select Filter 0 IF

+

mi

Figure 3-4: Image rejection vs. channel selectivity

the following stages is relaxed proportionally [3].

BAND IMAGE CHANNEL CHANNEL

SELECT LNA REJECT SELECT SELECT AMP

BPFBPF BPF

XBPF

eto IFto

OLOI CL02

0 IF1 IM2 LO2 IFI IMI LOI RF

Figure 3-5: Dual-IF conversion receiver

Since dual-IF provides the best sensitivity and selectivity trade-off, most RF re-ceivers today employ this topology. However, the extra mixers and filters make dual-IF a low-integration and high-power-consumption approach. The SAW and ceramic filters used at IF are bulky, expensive, and can not be integrated into the silicon pro-cess. These drawbacks make RF designers seek alternatives for low power and high integration solutions.

Desired Channel ,

-Interferer

SIF

3.4

Digitizing the IF

As digital signal processing technology continues to improve, more and more tasks that were performed in the analog domain have been transfered into the digital do-main. There has been considerable research in digitizing the IF for radio receivers. This ranges from the ASIC based approach [10, 11] to the more audacious general-purpose-processor (i.e., a workstation) approach [12].

Digital-IF affords greater flexibility and higher performance in terms of attenua-tion and selectivity. For example, digital filters are not only less sensitive to compo-nent variation, but they are also more size and power efficient in applications requiring extremely linear phase, very high stop band attenuation, or very low pass band ripple

[13].

More importantly, digital implementation enables software control that can sup-port multiple modulation waveforms and multiple air interface standards on the same hardware platform. This is the idea behind software radio, which offers great flexi-bility and reconfiguraflexi-bility in terms of implementation.

Figure 3-6 shows a proposed architecture that is a good candidate for wireless microsensor systems. The RF front-end employs a dual-IF architecture to provide the best performance. It converts the band of interest to an IF at a few hundred MHz. This band is digitized by the wideband ADC, and then down-converted to baseband through a digital down-converter. Channel selection is performed at baseband, where the processing requirement is much less, and the signal is then demodulated. As mentioned before, a digital demodulator offers flexibility and is very conducive to the study of various demodulation and air-interface standards. In addition, it mitigates the sensitivity and selectivity trade-off since channel filtering can be made much more precise in the digital domain. However, digitization at the hundred MHz regime imposes serious technical challenges. The following shows why this is the case.

An ideal software radio would perform digitization directly at RF and implement all receiver functions in the digital domain to maximize reconfigurability. However, this is not feasible with today's technology due to limitations on ADC dynamic range