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CELLULAR COMMUNICATIONS MIDTERM REVIEW

Cellular COMMUNICATIONS

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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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Page 1: Cellular COMMUNICATIONS

CELLULAR COMMUNICATIONSMIDTERM REVIEW

Page 2: Cellular COMMUNICATIONS

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

Page 3: Cellular COMMUNICATIONS

Wavelength and propagation velocity

vvTf

Page 4: Cellular COMMUNICATIONS

Constructive and Destructive Interference

Page 5: Cellular COMMUNICATIONS

Doppler Effect

vT

vT Tu u

uf fv

When no relative motion

When moving @U

Page 6: Cellular COMMUNICATIONS

Fast fading: Multipath

Page 7: Cellular COMMUNICATIONS

ISI

Page 8: Cellular COMMUNICATIONS

Example

Page 9: Cellular COMMUNICATIONS

Example: Sawtooth

Frequency Domain X(k)=1/k

Page 10: Cellular COMMUNICATIONS

Ambiguity problem

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Ambiguity in frequency domain

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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

Page 13: Cellular COMMUNICATIONS

Time vs. Frequency Short pulse in time domain->wide spectrum

Page 14: Cellular COMMUNICATIONS

Power Spectral Density(PSD)

2( ) ( )PSD f X f

Page 15: Cellular COMMUNICATIONS

Example:1Hz+3Hz

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Nonlinear Example: 1Hz+3Hz

f(x1+x2)!=f(x1)+f(x2)

Page 17: Cellular COMMUNICATIONS

SUI are a basis

Page 18: Cellular COMMUNICATIONS

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

Page 19: Cellular COMMUNICATIONS

Convolution 0

( ) ( ) ( )m

c n x n m h m x h

Page 20: Cellular COMMUNICATIONS

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

Page 21: Cellular COMMUNICATIONS

Amplitude Modulation(AM) Change amplitude of the signal

according to information Simplest digital form is “on-off

keying”(telegraph Morse code)

Page 22: Cellular COMMUNICATIONS

Audio AM

Page 23: Cellular COMMUNICATIONS

Frequency Modulation

Page 24: Cellular COMMUNICATIONS

Phase Modulation

Another form of FM

Page 25: Cellular COMMUNICATIONS

Circular 16-QAM

Page 26: Cellular COMMUNICATIONS

Frequency Hopping

Page 27: Cellular COMMUNICATIONS

Example :DSSS with PN

Transmitter/Receiver should be able to generate same synchronized Pseudo Random Noise sequences

Page 28: Cellular COMMUNICATIONS

OFDM Select orthogonal carriers Reach maximum at different times Can pack close without much

interference More carriers within the same

bandwidth

Page 29: Cellular COMMUNICATIONS

Hierarchy of speech coders

Page 30: Cellular COMMUNICATIONS

-Law

Page 31: Cellular COMMUNICATIONS

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)

Page 32: Cellular COMMUNICATIONS

DPCM and prediction

Page 33: Cellular COMMUNICATIONS

Sub-band coding Human ear does not detect error at all

frequencies equally well

Page 34: Cellular COMMUNICATIONS

Human Vocal Tractdemo

Page 35: Cellular COMMUNICATIONS

Voice Generation Model

Page 36: Cellular COMMUNICATIONS

LPC

Page 37: Cellular COMMUNICATIONS

Mean Opinion Score Quality Rating

Page 38: Cellular COMMUNICATIONS

Codec MOS rating

Page 39: Cellular COMMUNICATIONS

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)

Page 40: Cellular COMMUNICATIONS

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

Page 41: Cellular COMMUNICATIONS

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”

Page 42: Cellular COMMUNICATIONS

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

Page 43: Cellular COMMUNICATIONS

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:

Page 44: Cellular COMMUNICATIONS

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

Page 45: Cellular COMMUNICATIONS

Convolution Codes

Page 46: Cellular COMMUNICATIONS

Decoding: Viterbi Algorithm Errors on the channel Find path with minimal total errors

Page 47: Cellular COMMUNICATIONS

Trellis Coded Modulation (TCM) Combined coding and modulation scheme Make most similar signals (phases) represent most different/distance codewords

Page 48: Cellular COMMUNICATIONS

Turbo Codes Use 2 convolutional codes on the same

data Feed data in different order to the

encoders