Why BER Plot for channel coding have sudden drop?
This curve contain BER versus SNR for Self-Concatenated Convolutional
Coding (SECCC) schemes in phase 1 for AF relaying and
Distributed Self-Concatenated Convolutional Coding scheme
using Iterative Decoding (DSECCC-ID)
REf.
[1]. M. F. U. Butt, S. X. Ng and L. Hanzo, “Self-concatenated code design and its application in power-efficient cooperative communications,” IEEE Communications Surveys & Tutorials, vol. 14, no. 3, pp. 858–883, 2012.
Why BER Plot for channel coding have sudden drop?
Dear Haji,
This behavior is observable for the error correcting codes since when the input bit error rate is smaller than the error correcting capability of method, the output bit error rate becomes theoretically zero. For example for the linear block error correcting code with code rate m/c where m is the message bits and C the code bits., the parity bits are P=C-M and the error correcting capability which is the number of bits that can be corrected is t=(P-1)/2. For example assuming P= 12-9= 3, t=1. Therefore the correctable bit error rate is 1/ 12 which is about 8x10^-2.This means that when the input bit rate is smaller equal this rate the output bit error rate will be very nearly zero as the errors will be corrected by the decoder.
Best wishes
Dear Haji,
This behavior is observable for the error correcting codes since when the input bit error rate is smaller than the error correcting capability of method, the output bit error rate becomes theoretically zero. For example for the linear block error correcting code with code rate m/c where m is the message bits and C the code bits., the parity bits are P=C-M and the error correcting capability which is the number of bits that can be corrected is t=(P-1)/2. For example assuming P= 12-9= 3, t=1. Therefore the correctable bit error rate is 1/ 12 which is about 8x10^-2.This means that when the input bit rate is smaller equal this rate the output bit error rate will be very nearly zero as the errors will be corrected by the decoder.
Best wishes
For convolutional codes there are three typical features you see in the plots (aka. waterfall curves):
- When the channel is poor, the code cannot correct the errors. This is the upper plateau. In fact, the code can even make it worse, which you see when you compare it to the channel without coding.
- The sudden drop is the effect of the decoder correcting errors. In this region the code is effective. Compared to uncoded performance the drop becomes much steeper. Think of it the other way: coming from the right (good channel) the coding system approaches a threshold where it struggles and finally fails to correct the errors. A better code would have an even more sudden transition---and closes to the channel capacity.
- The lower plateau on the right is called the error floor. Convolutional codes are ineffective to get to very low residual error probabilities. This is the reason why some communication systems have an inner code that is effective to get from a fairly good channel (after CC) to practically zero residual errors. Examples of these inner codes are algebraic codes, like RS or BCH.
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