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Cellular COMMUNICATIONS. MIDTERM REVIEW. Representing Oscillations. w is angular frequency Need two variables to represent a state Use a single 2D variable to represent a state as a vector (a phasor ). Wavelength and propagation velocity. Constructive and Destructive Interference. - PowerPoint PPT Presentation
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CELLULAR COMMUNICATIONSMIDTERM REVIEW
Representing Oscillations w is angular frequency Need two variables to represent a
state Use a single 2D variable to represent
a state as a vector (a phasor)
0( ) sin( )x t a wt
2 12 22wwT T f w f
w T
0( ) sin(2 ) sin( )tx t a ft a
0 0
2 2
( ) ( ( ), ( )) cos( ), sin( )
, arctan 2
x y
yx y t
x
r t r t r t a wt a wt
ra r r
r
Wavelength and propagation velocity
vvTf
Constructive and Destructive Interference
Doppler Effect
vT
vT Tu u
uf fv
When no relative motion
When moving @U
Fast fading: Multipath
ISI
Example
Example: Sawtooth
Frequency Domain X(k)=1/k
Ambiguity problem
Ambiguity in frequency domain
Nyquist sampling frequency Signal band Avoid aliasing Nyquist sampling frequency Maximum frequency without aliasing
[ : ] [ : ]a b c h c hf f f f f f
a s b s b af f f f f f
2s hf f
2s
hff
Time vs. Frequency Short pulse in time domain->wide spectrum
Power Spectral Density(PSD)
2( ) ( )PSD f X f
Example:1Hz+3Hz
Nonlinear Example: 1Hz+3Hz
f(x1+x2)!=f(x1)+f(x2)
SUI are a basis
Finite Impulse Response Filter
Impulse response
( ) ( 2) ( 1) ( )y n x n x n x n
( 1) 0(0) 1(1) 1(2) 1(3) 0
hhhhh
Convolution 0
( ) ( ) ( )m
c n x n m h m x h
Convolution in Frequency Domain x(t), y(t) are signals X(f), Y(f) are their spectrum What is the spectrum C(f) of Convolution theorem C=X*Y
(multiplication)
Convolution in the time domain===Multiplication in the frequency domain
c x y
Amplitude Modulation(AM) Change amplitude of the signal
according to information Simplest digital form is “on-off
keying”(telegraph Morse code)
Audio AM
Frequency Modulation
Phase Modulation
Another form of FM
Circular 16-QAM
Frequency Hopping
Example :DSSS with PN
Transmitter/Receiver should be able to generate same synchronized Pseudo Random Noise sequences
OFDM Select orthogonal carriers Reach maximum at different times Can pack close without much
interference More carriers within the same
bandwidth
Hierarchy of speech coders
-Law
Vector quantization Encode a segment of
sampled analog signal (e.g. L samples)
Use codebooks of n vectors Segment all possible
samples of dimension L into areas of equal probability
Very efficient at very low rates( R=0.5 bits per sample)
DPCM and prediction
Sub-band coding Human ear does not detect error at all
frequencies equally well
Human Vocal Tractdemo
Voice Generation Model
LPC
Mean Opinion Score Quality Rating
Codec MOS rating
Binary Symmetric Channel Transmission medium introduce errors Demodulator produces errors Model as a channel
Memoryless: probability of error is independent from one symbol to the next
Symmetric: any error is equally probable Binary Symmetric Channel (BSC)
Error Correcting Codes (ECC) Redundancy added to information
Encode message of k bits with n (n>k) bits Example: Systematic Encoding
Redundant symbols are appending to information symbols to obtain a coded sequence
Codeword
Error correction vs. Error Detection Error-detection
Detect that received sequence contains an error Request retransmission ARQ: Automatic Repeat Request/Query (HSDPA)
Error-correction Detect that received sequence contains an error Correct the error Forward Error Correction
“A Code allows correction of up to p errors and detection up to q (q>p) errors”
Block Codes vs. Convolution Codes Block Codes
Encode information block by block Each block encoded independently Encoding/Decoding is a memoryless
operation Convolutional Codes
Next symbol depend on a history of inputs/outputs
Linear Codes Linear combination of valid codewords is
also a codeword Code distance is a minimum among all
nonzero codeword weights (number of 1s) Linear space spanned by basis:
Syndrome
Syndrome depends only on error pattern Different errors=>different syndromes except
for the addition of codeword Can identify error patterns of weight w<=t
by looking at the syndrome One-to-one between syndromes and errors
w<=t
Convolution Codes
Decoding: Viterbi Algorithm Errors on the channel Find path with minimal total errors
Trellis Coded Modulation (TCM) Combined coding and modulation scheme Make most similar signals (phases) represent most different/distance codewords
Turbo Codes Use 2 convolutional codes on the same
data Feed data in different order to the
encoders