1. Welcome to EQ2430/EQ2440 RF lecture
Per Zetterberg
School of Electrical Engineering

2. Objective of this lecture
Give an overview of radio communications.
Review

3. What is RF ?
RF = Radio Frequency.
For us: 2-6GHz.

4. What is the ”channel” ? TX RX Propagation channel
TX = Transmitter chain
RX =receiver chain
D/A
A/D
Communication channel

5. Transmitter chain (TX)
D/A
LPF
BPF
HPA
=Mixer
LPF = Low Pass Filter
BPF = Band Pass Filter
HPA = High Power Amplifier
=Local oscillator

6. Receiver chain (RX) A/D LPF BPF LNA LPF = Low Pass Filter
BPF = Band Pass Filter
LNA =Low Noise Amplifier
=Mixer
=Local oscillator

7. Basic Channel Model
Combined effect ot low-pass and band-pass filters in TX and RX.
Unknown offset between clocks at TX and TX
Frequency offset between TX and RX.
Propagation channel

8. Handling basic channel model
Discrete time:
TRAIN
Data
Sliding correlation.
Sliding correlation, several frequency offsets, FFT.
Several short correlations.
Self-correlation.

9. Inter-symbol interference
16QAM: Blur.
QPSK: No problem.

10. Inter-symbol interference sources
Radio propagation.
Narrow and sharp low-pass and band-pass filters !!!!!! (narrow=narrow compared with the bandwidth of the desired signal)
Pulse-shaping, sampling offsets.
So why do we use these narrow filters ?
Limit spectrum of transmitted signal.
Improve adjacent channel performance.
Reduce requirements on A/D converters.

11. Ways to combat inter-symbol interference
Interpolation between samples.
Equalizers (linear, decision feedback, viterbi, ...)
OFDM

12. Next problem
Power amplifier non-linearity

13. Power-Amplifier Non-linearity

14. Input/output power

15. AM/AM and AM/PM model AM/AM AM/PM
AM: Amplitude Modulation PM: Phase Modulation

16. 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.

17. Solid State Power Amplifier Model: SSPA
:Output saturation level (unit dependent e.g. volt, dBm, LSB)
:Smoothness parameter.
LSB: Least significant bit.

18. 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 (least-significant 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)

19. Amplifier non-linearity effects
Link 1
BS1
MS1
Link 2
BS2
MS2
In-band disrtorion: Detoriation of own link.
Out-of-band distortion: Detoration of the others link.

20. In-band/out-of-band
In-band distortion
Out-of-band distortion

21. Example of in-band distortion influence
Without distortion
With distortion

22. Next problem
Phase-noise

23. Phase-noise: Imperfect LO
BPF
LNA
LPF
A/D
This phase offset is a stocastic process = phase noise.

24. Phase-Noise Spectrum

25. Matlab-file: add_phase_noise.m
Link on course homepage
Generates phase-noise from given phase-noise 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.

26. Example: Influence of phase-noise
Without phase-noise
With phase-noise

27. 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.

28. Distortion + Thermal noise
SNR and SINAD
Signal power
SNR=
Thermal noise power
Signal power
SINAD=
Distortion + Thermal noise
Dominates at close distance.
Often proportional to transmitted power

29. SINAD and SNR versus range

30. Estimating SNR and SINAD
Estimate thermal noise power from part 1.
Estimate signal power and distortion power from part 2 e.g. Using training sequence.
Part1: Before transmission:
Thermal noise only.
Part2: Signal present
X= S + N + E

31. Theory versus Reality What theory ? Generally: Basic channel model.
Present results versus SNR not SINAD

32. Voice Band TransmissionIn
FM modulator
Out
FM de-modulator
Out In
AM modulator
AM de-modulator
Power of output may be unrelated power of input.
Difficult to use previous slides in this scenario.

33. 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