The bit error rate or bit error ratio (BER) is the number of bit errors divided by the total number of transferred bits during a studied time interval. BER is a unitless performance measure, often expressed as a percentage.

The bit error probability p_e is the expectation value of the BER. The BER can be considered as an approximate estimate of the bit error probability. This estimate is accurate for a long time interval and a high number of bit errors.

Example

0 1 1 0 0 0 1 0 1 1,

and the following received bit sequence:

0 0 1 0 1 0 1 0 0 1,

The number of bit errors (the underlined bits) is in this case 3. The BER is 3 incorrect bits divided by 10 transferred bits, resulting in a BER of 0.3 or 30%.

Packet error rate

:$p\_p\; =\; 1\; -\; (1\; -\; p\_e)^N$,

assuming that the bit errors are independent of each other. For small bit error probabilities, this is approximately

:$p\_p\; \backslash approx\; p\_eN.$

Similar measurements can be carried out for the transmission of frames, blocks, or symbols.

Factors affecting the BER

The BER may be improved by choosing a strong signal strength (unless this causes cross-talk and more bit errors), by choosing a slow and robust modulation scheme or line coding scheme, and by applying channel coding schemes such as redundant forward error correction codes.

The transmission BER is the number of detected bits that are incorrect before error correction, divided by the total number of transferred bits (including redundant error codes). The information BER, approximately equal to the decoding error probability, is the number of decoded bits that remain incorrect after the error correction, divided by the total number of decoded bits (the useful information). Normally the transmission BER is larger than the information BER. The information BER is affected by the strength of the forward error correction code.

Analysis of the BER

Examples of such simple channel models are:

A worst case scenario is a completely random channel, where noise totally dominates over the useful signal. This results in a transmission BER of 50% (provided that a Bernoulli binary data source and a binary symmetrical channel are assumed, see below).

, QPSK, 8-PSK and 16-PSK, AWGN channel.]] with gray-coding operating in white noise.]]

In a noisy channel, the BER is often expressed as a function of the normalized carrier-to-noise ratio measure denoted Eb/N0, (energy per bit to noise power spectral density ratio), or Es/N0 (energy per modulation symbol to noise spectral density).

For example, in the case of QPSK modulation and AWGN channel, the BER as function of the Eb/N0 is given by: $BER=1/2erfc(Eb/N0/sqrt(2))$.

People usually plot the BER curves to describe the functionality of a digital communication system. In optical communication, BER(dB) vs. Received Power(dBm) is usually used; while in wireless communication, BER(dB) vs. SNR(dB) is used.

Measuring the bit error ratio helps people choose the appropriate forward error correction codes. Since most such codes correct only bit-flips, but not bit-insertions or bit-deletions, the Hamming distance metric is the appropriate way to measure the number of bit errors. Many FEC coders also continuously measure the current BER.

A more general way of measuring the number of bit errors is the Levenshtein distance. The Levenshtein distance measurement is more appropriate for measuring raw channel performance before frame synchronization, and when using error correction codes designed to correct bit-insertions and bit-deletions, such as Marker Codes and Watermark Codes.

Mathematical draft

$x\_1(t)\; =\; A\; +\; w(t)$ for a "1" and $x\_0(t)\; =\; -A\; +\; w(t)$ for a "0". Each of $x\_1(t)$ and $x\_0(t)$ has a period of $T$.

Knowing that the noise has a bilateral spectral density $\backslash frac\{N\_0\}\{2\}$,

$x\_1(t)$ is $\backslash mathcal\{N\}\backslash left(A,\backslash frac\{N\_0\}\{2T\}\backslash right)$

and $x\_0(t)$ is $\backslash mathcal\{N\}\backslash left(-A,\backslash frac\{N\_0\}\{2T\}\backslash right)$.

Returning to BER, we have the likelihood of a bit misinterpretation $p\_e\; =\; p(0|1)\; p\_1\; +\; p(1|0)\; p\_0$.

$p(1|0)\; =\; 0.5\backslash ,\; \backslash operatorname\{erfc\}\backslash left(\backslash frac\{A+\backslash lambda\}\{\backslash sqrt\{N\_o/T\}\}\backslash right)$ and $p(0|1)\; =\; 0.5\backslash ,\; \backslash operatorname\{erfc\}\backslash left(\backslash frac\{A-\backslash lambda\}\{\backslash sqrt\{N\_o/T\}\}\backslash right)$

where $\backslash lambda$ is the threshold of decision, set to 0 when $p\_1\; =\; p\_0\; =\; 0.5$.

We can use the average energy of the signal $E\; =\; A^2\; T$ to find the final expression :

$p\_e\; =\; 0.5\backslash ,\; \backslash operatorname\{erfc\}\backslash left(\backslash sqrt\{\backslash frac\{E\}\{N\_o\}\}\backslash right).$ ±§

Bit error rate test

A BERT typically consists of a test pattern generator and a receiver that can be set to the same pattern. They can be used in pairs, with one at either end of a transmission link, or singularly at one end with a loopback at the remote end. BERTs are typically stand-alone specialised instruments, but can be Personal Computer based. In use, the number of errors, if any, are counted and presented as a ratio such as 1 in 1,000,000, or 1 in 1e06.

Common types of BERT stress patterns

Bit error rate tester

The main building blocks of a BERT are:

See also

References

Category:Ratios Category:Data transmission Category:Network performance Category:Error measures