Physical Layer and Coding

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1 Physical Layer and Coding Muriel Médard Professor EECS

2 Overview A variety of physical media: copper, free space, optical fiber Unified way of addressing signals at the input and the output of these media: basic models Nyquist sampling theorem How we use the channels: modulation Modeling the net effect of channels: transition probabilities and errors Making up for channel errors: principles of coding theoretical limits

3 Signals Channel has a physical signal traversing a physical medium Electromagnetic signal Polarization of the wave TM TE Propagation of the wave

4 Signals What do we mean by frequency? We can express a signal by a superposition of sinusoids, each of them with its own frequency A continuous signal is bandlimited to a bandwidth W at carrier frequency f 0 if it can be expressed in terms of sinusoids in a range of frequencies of width W around a frequency f 0 If we multiply the signal by a frequency shift, then we operate around 0, the baseband representation passband baseband f 0 -W/2 f 0 f 0 +W/2 -W/2 0 W/2

5 Nyquist sampling theorem We can wholly reconstruct a signal from its periodic samples as long as those samples are within 1/W of each other Continuous signal x(t), discrete time signal is x[n] = x(n T) where T = 1/W x(t) 0 T 2T 3T 4T 5T 6T t

6 Modulation: creating signals How we generate signals, by varying amplitude, phase, frequency On-off keyed (OOK): send 1, 0, a special case of amplitude modulation Frequency-shift keyed (FSK), change frequencies: Phase-shift key (PSK): 2 PSK 4 PSK

7 Modulation Pulse position modulation (PPM) Combining phase and amplitude, for instance: PAM 5x5 symbol code for 100BASE-T2 PHY

8 Channels Canonical channel model: signal goes into channel, there is a non-additive effect and addition of noise N[n] Tx X[n] G G(X[n]) Y[n] Rx Very commonly, G is a multiplicative effect: Y[n] = g X[n] + N[n] Y[n] = g -1 X[n-1] + g 0 X[n] + N[n] INTERSYMBOL-INTERFERENCE (ISI)

9 Effect of the channel If we put in a certain string of input signals X[1], X[2],, then we have a certain probability of having a certain output string Y[1], Y[2], The net effect is what is the probability of having a certain output when a given input is given

10 Channel model The channel is described by an input alphabet, an output alphabet, a set of probabilities on the input alphabet and a set of channel transitions, for a memoryless channel (on every bit the channel behaves independently of other times): Tx Rx k m Channel with input alphabet of size k+1 and output alphabet of size m+1 p(y x) is transition probability, probability of getting output y for input x

11 How do we determine the probabilities? We want to get to p(x y) Use Bayes s rule: p(x y) = p( x, y) / p(y) Also p(x, y) = p(y x) p(x) p(y) = p(y x i ) p(x i ) i For the case where all the inputs have the same probability, then p(x y) = p(y, x) / (1/n p (y x i ) ) i = p(y x) / n p (y x) How can we try to make up for the effect of the channel?

12 When do we use codes Two different types of codes: source codes: compression channel codes: error-correction Source-channel separation theorem says the two can be done independently for a large family of channels stream Source encoder s Channel encoder x y Channel decoder Source decoder Modulator, channel, receiver, etc...

13 What is a reasonable way to construct codes? Suppose that errors occur independently from symbol to symbol: a reasonable way to code is to make codewords as dissimilar as possible Number of bits in red: Hamming distance x 1 = x 2 = If we received y = , we would map to the first codeword - we ll make this more precise later

14 What do we mean by constructing a code? Block code: map a block of bits to another, generally larger, block of bits (n, k) linear block code encodes n-bit message into k-bit code vector, rate of code is R = n/k In general, code maps a message from set M= 2 n onto a codeword of length k Every codeword is one-to-one correspondence with a message An error occurs in decoding if we map a received codeword to the wrong message or to more than one message

15 What do we mean by decoding? We choose most likely input to have yielded observed output - Maximum Likelihood Decoding Given that we observe the output vector y, the probability that x 1 was transmitted is p(x 1 y) and the probability that x 2 was transmitted is p(x 2 y) (let s look at 2 codewords) Then we pick x 1 when p(x 1 y) / p(x 2 y) > 1 or equivalently when ln(p(x 1 y) ) - ln(p(x 2 y) ) > 0 in terms of log likelihoods

16 Decoding example Consider the binary symmetric channel (BSC) 0 Tx 1-p Rx p 0 1 p 1 1-p The probabilities are: p(x 1 y) = (1-p) 13 p 13 no errors and 1 error p(x 1 y) = (1-p) 11 p 3 11 no errors and 3 errors The BER is p

17 Hamming distance and code capability Minimum Hamming Distance d min of a code is the minimum Hamming Distance between any two codewords A code can detect up to t errors iff d min t + 1 A code can correct up to t errors iff d min 2t + 1 A code can correct up to t errors and detect up to t > t errors iff d min 2t + 1 and d min t + t + 1 How does d min relate to r = k-n, the number of redundant bits? Singleton bound: r + 1 d min Example take strings of n bits and add a parity check code: r is 1 and d min is 2 These are bounds, most codes don t achieve these bounds

18 Linear codes: how do we build them? Code word x = s G where for (n,k) code G is a matrix with k rows and n columns Linear code because sums of codewords are codewords How can we find d min? The Hamming distance between two binary codewords is the same as the Hamming weight of their sum Thus, the minimum Hamming distance is the minimum weight of non-zero code vectors

19 Syndromes and parity-check For any code, it is always possible to find a systematic code, i.e. a code for which G = [I k x k P k x r ]by manipulating the rows using linear operations We define the parity-check matrix H to be: & P r x k # $ - I % k x k " We have x H = 0 Syndrome s = y H Define error sequence e = y + x Then s = e H Decoding is done by finding the minimum Hamming weight sequence that satisfies s = e H and decoding to the codeword y + z

20 Let s think in 3-D Hamming distance between codes is related to t, the number of errors to correct in a (n, k) linear block code Example: rate (3,1) repetition code: can correct at most 1 error and detect 2 errors

21 Further bounds Hamming bound: r Gilbert bound: there exists a code such that: " # $ $ % & " # $ % & ' ( = t 0 j j n log " # $ $ % & " # $ % & ' ( = 2t 0 j j n log r

22 Cyclic Codes Cyclic codes are codes with cyclic shift property in code words: if (a, b, c, d, e) is a codeword, so is (b, c, d, e, a) A generator matrix is then G = & g $ $ $ $ $ $ m 0. g... m.. g # g... $ %... g m... gm " Can we make use of the regular structure of this matrix to simplify things? 0

23 Polynomials on fields Galois field: a field with a finite number of elements We can define a polynomial over a finite field (Galois Field) GF(q) as f(d) = f n D n + f n-1 D n f 0 D is not a variable in the field, it is an indeterminate - think of the D-transform or the Z-transform For polynomials, addition and multiplication can be defined, they are commutative, associative, closure holds, an addition inverse is defined BUT NO MULTIPLICATIVE INVERSE Instead, we have the following theorem: let f(d) and g(d) be polynomials over GF(q) and let g(d) have degree at least 1. Then there exist unique polynomials h(d) and r(d) over GF(q) for which the degree of r(d) is less than that of g(d) and f(d) = g(d) h(d) + r(d) (Euclidean division algorithm)

24 Relation between polynomials and cyclic codes Represent a vector x = (x n-1 x 0 ) as x(d) = x n-1 D n x 0 We say that x(d) is a codeword when the coefficients for the letters of a codeword If we have a cyclic code, then it is the case that the remainder of dividing D x(d) by D n - 1 ( also called the remainder of D x(d) modulo D n - 1 ) is also a code word To see this: Dx(D) = x n-1 D n + + x 0 D = x n-1 (D n - 1) + + x 0 D + x n-1 Define g(d) to be the lowest degree monic polynomial which is a code word in a cyclic code define m to be its degree Because of linearity, any D j g(d) is also a codeword, so in general a(d) g(d) is a codeword Moreover all code words have this form

25 Implementing systematic cyclic codes In polynomial form, a systematic binary code is of the form: x(d) = D r t(d) - d(d) = D r t(d) + d(d) The check bits are d(d) and d(d) has degree less than r Let s take d(d) to be the remainder of D r t(d) / g(d) What about the syndrome? s(d) = x(d) / g(d) = D r t(d) / g(d) + d(d) / g(d) = 2 d(d) = 0 For some q(d), we have g(d) q(d) + d(d) = D r t(d) So g(d) q(d) = D r t(d) - d(d) = D r t(d) + d(d) which is our codeword x(d)

26 Implementing cyclic codes Divide by g(d) circuit D r s(d) g g g r-1... s 1 + s s r-1 + D r s(d)... Divide by g(d) + Encoder Circuits are easily implemented using shift registers to represent polynomials in D

27 Decoding Brute force: divide y(d) by g(d), from there get syndrome, check syndrome against syndrome table, generate n-bit error pattern, then add to y(d) to obtain corrected word However, we know that we can only correct a number t of errors Let s define the Meggitt set to be the set of error patterns such that e n-1 = 1 Use Meggitt s theorem: suppose that g(d) h(d) = D n - 1 and e(d) / g(d) = s(d) then [D b e(d) mod (D n - 1)]/g(D) = [D b s(d)]/g(d) So only need to store syndromes for error patterns in the Meggitt set

28 Different types of codes: Hamming codes Hamming codes are some of the few perfect codes Sphere of radius v: set of all sequences at distances v or less from a sequence A v-error-correcting sphere-packed code has the property that the spheres of size v around the code are non-overlapping and that every sequence is at most v+1 in distance from some code word A v-error-correcting sphere-packed code for which every sequence is at most v+1 in distance from some code word is perfect Hamming codes: rows of H are all the different nonzero sequences of length k-n Thus k = 2 k-n -1 For all Hamming codes, the minimum Hamming distance is 3, so they can correct 1 error or detect 2 The rate of a Hamming code is R = (2 r - r -1 )/(2 r -1 )

29 Cyclic Redundancy Check Codes (CRCs) Normally look only to see whether the syndrome is 0 or not, which is why they are error-detecting This is not a function of the CRC code, it s more of the way it s decoded Usually used in a concatenated fashion with an error correcting code g(d) = (x+1) p(d) where p(d) is a primitive polynomial, which means that it divides D 2r but no lower order polynomial of the form D v -1 Length k = 2 r-1-1 Usually used in shortened mode: for a systematic code, stuff with 0s and those 0s need not be transmitted but must be taken into account at the decoding

30 Convolutional codes Rather than map a block to a block, we code continuously Tend to be used in lower BER channels We can define the code as a set of states: for M memory elements in the encoder, there are 2 M states The trellis structure is as follows, say M=2 Decoding 11 We use a time between known states 01 Possible ways of getting from one state 00 to another are called the adversary paths 10 t t + 1 Trellis Viterbi algorithm is a means of applying dynamic programming to path selection

31 Beyond codes based on distance What is the best performance we can get with a coded system? Let us consider a channel with bandwidth W, with noise energy N per Hz, and with a maximum E on the mean transmitted Shannon s limit is that if we have no delay constraint and no complexity constrain on the coder and decoder, the best achievable error-free rate (capacity) is given by W 2 & E log$ 1 + % NW Bits per second A modulation to achieve this limit is a one that itself looks like noise and the codes are random # "

32 Capacity Recall the BSC 0 X p 1-p Y 0 1 p 1 1-p Capacity is: I(X; Y) = H(Y) - H(Y X) H is entropy H(Z) = - Σ p Z (z) log(p Z (z) ) Let s work it out...

33 Random codes The codes are random in that the codewords are selected at random from a large number of codewords The coding theorem gives results based on the fact that an error is unlikely because the for long enough codewords, two codewords are unlikely to be the close enough to be confused Codes have emerged which work on that principle Examples: Turbo Codes and Low Density Parity Check Codes

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