Welcome to
EQ2430/EQ2440
RF lecture
Per Zetterberg
School of Electrical Engineering
1
Objective of this lecture
•
•
Give an overview of radio communications.
Review
2
What is RF ?
• RF = Radio Frequency.
• For us: 2-6GHz.
3
What is the ”channel” ?
Propagation
channel
yt 
xt 
D/A
RX
TX
A/D
RX =receiver chain
TX = Transmitter
chain
Communication
channel
4
Transmitter chain (TX)
D/A
LPF
BPF
HPA
exp  j 2f TX t 
LPF = Low Pass Filter
BPF = Band Pass Filter
HPA = High Power Amplifier
=Mixer
exp  j 2f TX t 
=Local oscillator
5
Receiver chain (RX)
BPF
LNA
LPF
A/D
exp  j 2f RXt 
LPF = Low Pass Filter
BPF = Band Pass Filter
LNA =Low Noise Amplifier
=Mixer
exp  j 2f RXt =Local oscillator
6
Basic Channel Model
yt   exp  j 2ft hFILT t  hPR t  xt  t0 
Combined effect ot
low-pass and bandpass filters in TX and
RX.
Frequency offset
between TX and RX.
Unknown offset
between clocks at TX
and TX
Propagation
channel
7
Handling basic channel model
Discrete time:


yn  exp  j 2fTs n   hn n0 k I n  n0 
 k

TRAIN
TRAIN
I n : Known
1.
2.
3.
4.
Data
I n : Unkown
Sliding correlation.
Sliding correlation, several frequency offsets, FFT.
Several short correlations.
Self-correlation.
8
Inter-symbol interference
yn  I n  0.2I n1
4
1.5
3
1
2
0.5
1
0
0
-1
-0.5
-2
-1
-3
-1.5
-1.5
-4
-4
-1
-0.5
0
0.5
QPSK: No problem.
1
-3
-2
-1
0
1
2
3
4
1.5
16QAM: Blur.
9
Inter-symbol interference
sources
1. Radio propagation.
2. Narrow and sharp low-pass and band-pass filters !!!!!!
(narrow=narrow compared with the bandwidth of the desired signal)
3. Pulse-shaping, sampling offsets.
So why do we use these narrow filters ?
1. Limit spectrum of transmitted signal.
2. Improve adjacent channel
performance.
3. Reduce requirements on A/D
converters.
10
Ways to combat inter-symbol
interference
•
•
•
Interpolation between samples.
Equalizers (linear, decision feedback, viterbi, ...)
OFDM
11
Next problem
Power amplifier non-linearity
12
Power-Amplifier Non-linearity
13
Input/output power
14
AM/AM and AM/PM model
yt   SA  xt  exp  jSP  xt  exp  jxt 
AM/AM
AM: Amplitude Modulation
AM/PM
PM: Phase Modulation
15
Intuition AM/AM and AM/PM
model
•
•
•
•
•
•
Let’s say our communication signal has 1MHz
bandwidth.
The carrier frequency is 1GHz=1000MHz.
Then every symbol lasts 1000 cycles.
During one symbol the input signal can be seen as a
CW.
A CW which is sent through a non-linearity will always
appear at the output (together with harmonics), but
with a different amplitude and phase.
The AM/AM and AM/PM models are functions of this
phase offset.
16
Solid State Power Amplifier
Model: SSPA
FA   
A0
p

   2 p 
1    
  A0  


1
2p
:Output saturation level (unit dependent e.g. volt, dBm, LSB)
:Smoothness parameter.
LSB: Least significant bit.
17
Matlab function: SSPA.m
•
•
•
•
•
•
Available on course homepage.
Applies non-linearity to the input signal.
The parameter A0 is hardcoded inside the function.
The patameter A0 is referenced i units of LSB (leastsignificant bit) of the signal sent from the D/A
converter.
The smoothness parameter p is an input to the
function.
Three present values of p are proposed 1,10,100 (bad,
fair, good)
18
Amplifier non-linearity effects
Link 1
BS1
fc
MS1
Link 2
BS2
f c  5MHz
MS2
In-band disrtorion: Detoriation of own link.
Out-of-band distortion: Detoration of the others link.
19
In-band/out-of-band
In-band distortion
Out-of-band distortion
20
Example of in-band distortion
influence
4
4
3
3
2
2
1
1
0
0
-1
-1
-2
-2
-3
-3
-4
-4
-3
-2
-1
0
Without distortion
1
2
3
4
-4
-4
-3
-2
-1
0
1
2
3
4
With distortion
21
Next problem
Phase-noise
22
Phase-noise: Imperfect LO
BPF
LNA
LPF
A/D
LOt   exp  j 2f RXt   t 
This phase offset is a stocastic process
= phase noise.
23
Phase-Noise Spectrum
FFourier LO(t )  FFourier exp  j t 
24
Matlab-file: add_phase_noise.m
•Link on course homepage*
•Generates phase-noise from given phasenoise spectrum, and multiplies it to the
desired signal.
•The phase-noise spectrum is specified by
input parameters phase_noise_freq and
phase_noise_power.
•Three different ”pre-set” values given on
course homepage (bad, fair, good) given in
phase_noise_param.m.
*) The function is written by Alex Bar-Guy and is available on matlab central.
25
Example:
Influence of phase-noise
4
4
3
3
2
2
1
1
0
0
-1
-1
-2
-2
-3
-3
-4
-4
-3
-2
-1
0
1
Without phase-noise
2
3
4
-4
-4
-3
-2
-1
0
1
2
3
4
With phase-noise
26
How should you simulate ?
•
Start with basic channel model
You should be able to do this yourself.
•
•
Introduce AM/AM and AM/PM using SSPA.m.
Introduce phase-noise using add_phase_noise.m.
27
SNR and SINAD
SNR=
SINAD=
Signal power
Thermal noise power
Signal power
Distortion + Thermal noise
Often proportional to
transmitted power
Dominates at close distance.
28
SINAD and SNR versus range
90
SNR
SINAD
80
70
60
dB
50
40
30
20
10
0
0
100
200
300
600
500
400
Distance TX<->RX
700
800
900
1000
29
Estimating SNR and SINAD
5000
4500
Signal + noise
Signal + noise + distortion
Estimate thermal noise
power from part 1.
4000
Estimate signal power
and distortion power
from part 2 e.g. Using
training sequence.
3500
3000
2500
2000
1500
1000
500
0
1960 1980 2000 2020 2040 2060 2080 2100 2120 2140 2160
Part1: Before transmission:
Thermal noise only.
Part2: Signal present
X= S + N + E
30
Theory versus Reality
What theory ?
Generally: Basic channel model.
Present results versus SNR not SINAD
31
Voice Band Transmission
In
In
FM modulator
AM modulator
FM de-modulator
AM de-modulator
Out
Out
Power of output may be unrelated power of input.
Difficult to use previous slides in this scenario.
32
Wrap-up
•
•
•
•
•
•
•
Propagation channel versus communication channel
distinction.
Basic channel model.
Power amplifier distortion (AM/AM and AM/PM).
Phase-noise (in up-/down-converters)
Matlab functions
SINAD versus SNR
Voice-band transmission
33