Pairwise error probability

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Pairwise error probability is the error probability that for a transmitted signal (X) its corresponding but distorted version (\widehat{X}) will be received. This type of probability is called ″pair-wise error probability″ because the probability exists with a pair of signal vectors in a signal constellation.[1] It's mainly used in communication systems.[1]

Expansion of the definition[edit]

In general, the received signal is a distorted version of the transmitted signal. Thus, we introduce the symbol error probability, which is the probability P(e) that the demodulator will make a wrong estimation (\widehat{X}) of the transmitted symbol (X) based on the received symbol, which is defined as follows:

P(e) \triangleq \frac{1}{M} \sum_{x} \mathbb{P} (X \neq \widehat{X}|X)

where M is the size of signal constellation.

The pairwise error probability P(X \to \widehat{X}) is defined as the probability that, when X is transmitted, \widehat{X} is received.

P(e|X) can be expressed as the probability that at least one \widehat{X} \neq X is closer than X to Y.

Using the upper bound to the probability of a union of events, it can be written:

P(e|X)\le\sum_{\widehat{X}\neq X} P(X \to \widehat{X})

Finally:

P(e) = \tfrac{1}{M} \sum_{X \in S} P(e|X) \leq \tfrac{1}{M} \sum_{X \in S}\sum_{\widehat{X}\neq X} P(X \to \widehat{X})

Closed form computation[edit]

For the simple case of the additive white Gaussian noise (AWGN) channel:


Y = X + Z, Z_i \sim \mathcal{N}(0,\tfrac{N_0}{2} I_n)
\,\!

The PEP can be computed in closed form as follows:

\begin{align}
P(X \to \widehat{X}) & = \mathbb{P}(||Y-\widehat{X}||^2 <||Y-X||^2|X) \\
& = \mathbb{P}(||(X+Z)-\widehat{X}||^2 <||(X+Z)-X||^2) \\
& = \mathbb{P}(||(X - \widehat{X})+Z||^2 <||Z||^2) \\
& = \mathbb{P}(||X- \widehat{X}||^2 +||Z||^2 +2(Z,X-\widehat{X})<||Z||^2) \\
& = \mathbb{P}(||X- \widehat{X}||^2<2(Z,X-\widehat{X}) )\\
& = \mathbb{P}(||X- \widehat{X}||^2/2<(Z,X-\widehat{X}) )
\end{align}

(Z,X-\widehat{X}) is a Gaussian random variable with mean 0 and variance N_0||X- \widehat{X}||^2/2.

For a zero mean, variance \sigma^2=1 Gaussian random variable:

P(X > x) = Q(x) = \frac{1}{\sqrt{2\pi}} \int_{x}^{+\infty} e^-\tfrac{t^2}{2}dt

Hence,

\begin{align}
P(X \to \widehat{X}) & =Q \bigg(\tfrac{\tfrac{||X- \widehat{X}||^2}{2}}{\sqrt{\tfrac{N_0||X- \widehat{X}||^2}{2}}}\bigg)= Q \bigg(\tfrac{||X- \widehat{X}||^2}{2}.\sqrt{\tfrac{2}{N_0||X- \widehat{X}||^2}}\bigg) \\
& = Q \bigg(\tfrac{||X- \widehat{X}||}{\sqrt{2N_0}}\bigg)
\end{align}

See also[edit]

References[edit]

  1. ^ a b Stüber, Gordon L. Principles of mobile communication (3rd ed.). New York: Springer. p. 281. ISBN 1461403642. 

Further reading[edit]

  • Prasad, 5th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC '94) The Hague, the Netherlands, September 18–22, 1994 ; ICCC Regional Meeting on Wireless Computer Networks (WCN), the Hague, the Netherlands, September 21–23, 1994 ; edited by Jos H. Weber, Jens C. Arnbak, and Ramjee (1994). Wireless networks : catching the mobile future : proceedings. Amsterdam: IOS Press. pp. 564–575. ISBN 9051991932. 
  • Simon, Marvin K.; Alouini, Mohamed-Slim (2005). Digital Communication over Fading Channels (2. ed.). Hoboken: John Wiley & Sons. ISBN 0471715239.