matlab awgn ber 不对,BER for coded AWGN channels

bercoding

BER for coded AWGN channels

Description

The bercoding function returns an upper bound or

approximation of the bit error rate (BER) for coherent BPSK or QPSK modulation over an

additive white Gaussian noise (AWGN) channel for a specified coding type, decoding

decision, code rate, and distance spectrum of the code. The results for binary PSK and

quadrature PSK modulation are the same. This function computes only modulation order 2

or 4 for M-ary PSK modulation. For more information, see Analytical Expressions Used in bercoding Function and Bit Error Rate Analysis App.

ber = bercoding(EbNo,'block',decision,N,K,dmin)

returns the upper bound of the BER for an

[N,K] binary block code for the

specified decoding decision type and minimum distance of the code.

ber = bercoding(EbNo,'Golay','hard',24)

returns the upper bound of the BER for an the extended (24, 12) Golay code using

hard-decision decoding and coherent BPSK modulation. In accordance with [3], the Golay coding upper bound assumes only the correction of 3-error

patterns. Even though correcting approximately 19% of 4-error patterns is

theoretically possible in practice, most decoders do not have this

capability.

ber = bercoding(EbNo,'Hamming','hard',N)

returns an approximation of the BER for a Hamming code using hard-decision

decoding and coherent BPSK modulation.

ber = bercoding(EbNo,'RS','hard',N,K)

returns an approximation of the BER for an

(N,K) Reed-Solomon code using

hard-decision decoding and coherent BPSK modulation.

Examples

Find Upper Bound of Theoretical BER for Block Code

Find the upper bound of the theoretical BER for a (23,12) block code.

Set the codeword length, message length, minimum distance, and Eb/N0 range in dB.

n = 23; % Codeword length

k = 12; % Message length

dmin = 7; % Minimum distance

EbNo = 1:10; % Eb/No range (dB)

Estimate the BER.

berBlk = bercoding(EbNo,'block','hard',n,k,dmin);

Plot the estimated BER.

berfit(EbNo,berBlk)

ylabel('Bit Error Probability')

title('BER Upper Bound vs. Eb/No with Best Curve Fit')

Estimate Coded BER Performance for 16-QAM in AWGN

Estimate the BER performance in an AWGN channel for a 16-QAM signal when encoded with a (15,11) Reed-Solomon code using hard-decision decoding.

Set the input Eb/No range and find the uncoded BER for 16-QAM.

ebno = (2:12)';

uncodedBER = berawgn(ebno,'qam',16);

Estimate the coded BER for a 16-QAM signal with a (15,11) Reed-Solomon code using hard-decision decoding.

codedBER = bercoding(ebno,'RS','hard',15,11,'qam',16);

Plot the estimated BER curves.

semilogy(ebno,[uncodedBER codedBER])

grid

title('Compare Uncoded and R-S Coded 16-QAM Transmission')

legend('Uncoded BER','Coded BER')

xlabel('Eb/No (dB)')

ylabel('BER')

Input Arguments

EbNo — Energy per bit to noise power spectral density ratio

scalar | vector

Energy per bit to noise power spectral density ratio in dB, specified as a

scalar or vector.

Data Types:single | double

coding — Source coding type

'conv' | 'block' | 'Hamming' | 'Golay' | 'RS'

Source coding type, specified as one of these options.

'conv' — The returned BER is an upper bound

of the BER for binary convolutional codes with coherent BPSK or

QPSK modulation.

'block' — The returned BER is an upper

bound of the BER for (N,

K) linear binary block codes with

coherent BPSK or QPSK modulation.

'Hamming' — The returned BER is an

approximation of the BER for a Hamming code using hard-decision

decoding and coherent BPSK or QPSK modulation.

'Golay' — The returned BER is an upper

bound of the BER for an extended (24,12) Golay code using

hard-decision decoding and coherent BPSK or QPSK modulation. In

accordance with [3], the Golay coding upper bound assumes the correction of

3-error patterns only. Even though correcting approximately 19%

of 4-error patterns is theoretically possible, in practice, most

decoders do not have this capability.

'RS' — The returned BER is an approximation

of the BER for an

(N,K) Reed-Solomon

code using hard-decision decoding and coherent BPSK

modulation.

Data Types:char | string

decision — Decoding decision type

'hard' | 'soft'

Decoding decision type, specified as one of these options.

'hard' — Use this option for hard-decision

decoding.

'soft' — Use this option for unquantized

soft-decision decoding. This option applies only when

coding is set to

'conv' or

'block'.

Data Types:char | string

coderate — Code rate of convolutional code

positive scalar

Code rate of the convolutional code, specified as a positive

scalar.

Dependencies

To enable this argument, set the coding argument

to 'conv'.

Data Types:double | single

dspec — Distance spectrum of code

structure

Distance spectrum of the code, specified as structure containing these

fields. To find distance spectra for sample codes, use the distspec function or see

[5] and [3].

dfree — Minimum free distance of code

positive scalar

Minimum free distance of the code, specified as a positive

scalar.

Data Types:double | single

weight — Weight spectrum of code

positive scalar

Weight spectrum of the code, specified as a positive

scalar.

Data Types:double | single

Dependencies

To enable this argument, set the coding argument

to 'conv'.

Data Types:struct

N — Codeword length

integer

Codeword length, specified as an integer of the form

2M–1, where

M is an integer in the range [3, 16]. For more

information, see N-K Pairs for Source Coding.

Example:15 or 2^4-1

Dependencies

To enable this argument, set the coding argument

to 'block', 'Hamming',

'Golay', or 'RS'.

K — Message length

positive integer

Message length, specified as a positive integer. For more information, see

N-K Pairs for Source Coding.

Example:5 specifies a Galois field array with five

elements.

Dependencies

To enable this argument, set the coding argument

to 'block' or 'RS'.

dmin — Minimum distance of code

positive scalar

Minimum distance of the code, specified as a positive scalar smaller than

or equal to (N - K + 1).

.

Dependencies

To enable this argument, set the coding argument

to 'block'.

Data Types:double | single

modulation — Modulation type

'psk' (default) | 'oqpsk' | 'dpsk' | 'pam' | 'qam' | 'fsk' | 'msk'

Modulation decision type, specified as 'psk',

'oqpsk', 'dpsk',

'pam', 'qam',

'fsk', or 'msk'. The default

modulation scheme is PSK modulation with a modulation order of 2

(specifically, nondifferential BPSK modulation).

Data Types:char | string

Output Arguments

ber — BER

scalar | vector

BER upper bound or approximation, returned as a scalar or vector. If the

EbNo input is a vector, ber is

a vector of the same size, and its elements correspond to the elements of

the

Eb/N0

vector.

Limitations

In general, the numerical accuracy for the output BER is limited to approximately two

significant digits. The numerical accuracy output by this function is limited by these restrictions.

Approximations in the analysis leading to the closed-form expressions used

by the function

Approximations related to the numerical implementation of the

expressions

More About

N-K Pairs for Source Coding

For block codes the codeword length (N) and

message length (K) pairs must comply with these

guidelines.

N and K must produce a

narrow-sense BCH code.

To generate the list of valid

(N,K) pairs and their

corresponding values of error-correction capability, run the command

bchnumerr(N).

Valid values for N have the form

2M–1, where

M is an integer in the range [3, 16]. The value of

N cannot exceed 65,535.

For Hamming codes, K is computed directly

from N.

For Reed-Solomon codes, N and

K must differ by an even integer. Valid values for

N have the form

2M–1, where M

is an integer in the range [3, 16]. The value of N cannot

exceed 65,535.

Alternatives

You can configure the Theoretical tab in the bercoding function.

References

[1] Proakis, John G. Digital Communications.

4th ed. New York: McGraw Hill, 2001.

[2] Frenger, P., P. Orten, and T. Ottosson. “Convolutional Codes with Optimum Distance Spectrum.” IEEE Communications Letters 3, no. 11 (November 1999): 317–19. https://doi/10.1109/4234.803468.

[3] Odenwalder, J. P. Error Control Coding

Handbook, Final Report, LINKABIT Corporation, San Diego, CA: 1976.

[4] Sklar, Bernard. Digital Communications: Fundamentals

and Applications. 2nd ed. Upper Saddle River, N.J: Prentice-Hall PTR,

2001.

[5] Ziemer, R. E., and R. L., Peterson.

Introduction to Digital Communication. 2nd ed. Prentice Hall,

2001.

Introduced before R2006a