Post on 16-Dec-2015
CELLULAR COMMUNICATIONS
3. DSP: A crash course
Signals
DC Signal
Unit Step Signal
Sinusoidal Signal
Stochastic Signal
Some Signal Arithmetic
Operational Symbols
Time Delay Operator
Vector Space of All Possible Signals
Shifted Unit Impulse (SUI) signals are basis for the signal vector space
Periodic Signals
Periodic Signals have another basis signal: sinusoids
Example: Building square wave from sinusoids
Fourier Series
Another version Fourier Series
Complex Representation
Parseval Relationship
Fourier Transform
Works for all analog signals (not necessary periodic)
Some properties
Discrete Fourier Transform (DFT) FT for discrete periodic signals
Frequency vs. Time Domain Representation
Power Spectral Density (PSD)
Linear Time-Invariant(LTI) Systems
Example of LTI
Unit Response of LTI
24
Convolution sum representation of LTI system
Mathematically
25
Graphically
Sum up all the responses for all K’s
Sinusoidal and Complex Exponential Sequences
LTI
h(n)
LTI
h(n)
njenx )(
k
knxkhny )()()(
k
knjekh )()(
jn
k
jk eekh )(
jnj eeH )(
Frequency Response
nje jnj eeH )()( jeH
eigenvalueeigenfunction
k
jkj ekheH )()(
Example: Bandpass filter
Nyquist Limit on Bandwidth
Find the highest data rate possible for a given bandwidth, B Binary data (two states) Zero noise on channel
1 0 1 0 0 0 1 0 1 1 0 1 00 0
Period = 1/B
• Nyquist: Max data rate is 2B (assuming two signal levels)• Two signal events per cycle
Example shown with bandfrom 0 Hz to B Hz (Bandwidth B)Maximum frequency is B Hz
Nyquist Limit on Bandwidth (general)
If each signal point can be more than two states, we can have a higher data rate M states gives log2M bits per signal point
10 00 11 00 00 00 11 01 10 10 01 00 0000 11
Period = 1/B
• General Nyquist: Max data rate is 2B log2M • M signal levels, 2 signals per cycle
4 signal levels:2 bits/signal
Practical Limits
Nyquist: Limit based on the number of signal levels and bandwidth Clever engineer: Use a huge number of signal levels
and transmit at an arbitrarily large data rate
• The enemy: Noise• As the number of signal levels grows, the
differences between levels becomes very small• Noise has an easier time corrupting bits
2 levels - better margins 4 levels - noise corrupts data
Characterizing Noise
Noise is only a problem when it corrupts data Important characteristic is its size relative
to the minimum signal information• Signal-to-Noise Ratio• SNR = signal power / noise power• SNR(dB) = 10 log10(S/N)
• Shannon’s Formula for maximum capacity in bps• C = B log2(1 + SNR)• Capacity can be increased by:
• Increasing Bandwidth• Increasing SNR (capacity is linear in SNR(dB)
)
Warning: Assumes uniform (white) noise!
SNR in linear form
Shannon meets Nyquist
From Nyquist: MBC 2log2From Shannon: )1(log2 SNRBC
Equating: )1(loglog2 22 SNRBMB )1(loglog2 22 SNRM
)1(loglog 22
2 22 SNRM SNRM 12
SNRM 1 12 MSNRor
M is the number of levelsneeded to meet Shannon Limit
SNR is the S/N ratio needed tosupport the M signal levels
Example: To support 16 levels (4 bits), we need a SNR of 255 (24 dB)
Example: To achieve Shannon limit with SNR of 30dB, we need 32 levels
)1(loglog 22
2 SNRM