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Welcome to EQ2430/EQ2440 RF lecture. Per Zetterberg School of Electrical Engineering. Objective of this lecture. Give an overview of radio communications. Review. What is RF ?. RF = Radio Frequency. For us: 2-6GHz. What is the ”channel” ?. Propagation channel. TX. A/D. D/A. RX.
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Welcome toEQ2430/EQ2440 RF lecture Per Zetterberg School of Electrical Engineering
Objective of this lecture • Give an overview of radio communications. • Review
What is RF ? • RF = Radio Frequency. • For us: 2-6GHz.
What is the ”channel” ? Propagation channel TX A/D D/A RX RX =receiver chain TX = Transmitter chain Communication channel
Transmitter chain (TX) D/A LPF BPF HPA =Mixer LPF = Low Pass Filter BPF = Band Pass Filter HPA = High Power Amplifier =Local oscillator
Receiver chain (RX) BPF LNA LPF A/D LPF = Low Pass Filter BPF = Band Pass Filter LNA =Low Noise Amplifier =Mixer =Local oscillator
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
Handling basic channel model Discrete time: TRAIN TRAIN Data Sliding correlation. Sliding correlation, several frequency offsets, FFT. Several short correlations. Self-correlation.
Inter-symbol interference 16QAM: Blur. QPSK: No problem.
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.
Ways to combat inter-symbol interference Interpolation between samples. Equalizers (linear, decision feedback, viterbi, ...) OFDM
Next problem Power amplifier non-linearity
AM/AM and AM/PM model AM/AM AM/PM AM: Amplitude Modulation PM: Phase Modulation
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 differentamplitude and phase. The AM/AM and AM/PM models are functions of this phase offset.
Solid State Power Amplifier Model: SSPA :Output saturation level (unit dependent e.g. volt, dBm, LSB) :Smoothness parameter. LSB: Least significant bit.
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)
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.
In-band/out-of-band In-band distortion Out-of-band distortion
Example of in-band distortion influence Without distortion With distortion
Next problem Phase-noise
Phase-noise: Imperfect LO BPF LNA LPF A/D This phase offset is a stocastic process = phase noise.
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.
Example: Influence of phase-noise With phase-noise Without phase-noise
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.
SNR and SINAD Signal power SNR= Thermal noise power Signal power SINAD= Distortion + Thermal noise Dominates at close distance. Often proportional to transmitted power
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
Theory versus Reality What theory ? Generally: Basic channel model. Present results versus SNR not SINAD
Voice Band Transmission In Out FM modulator AM modulator FM de-modulator Out In AM de-modulator Power of output may be unrelated power of input. Difficult to use previous slides in this scenario.
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