Channel error rate optimization using Markov codes

• US 10,447,315 B2
• Filed: 08/15/2017
• Issued: 10/15/2019
• Est. Priority Date: 08/15/2016
• Status: Active Grant

• Current Assignee: Seagate Technology LLC (Seagate Technology Holdings Plc)
• Original Assignee: Seagate Technology LLC (Seagate Technology Holdings Plc)
• Inventors: Venkataramani, Raman
• Primary Examiner(s): Knapp, Justin R
• Application Number:
  US15/677,304
• Publication Number:
  US 20190058494A1
• Time in Patent Office: 791 Days
• Field of Search: None
• CPC Class Codes: H03M 13/4138 soft-output Viterbi algorithm based decoding, i.e. Viterbi decoding with weighted decisions
• CPC Class Codes: H03M 13/4146 soft-output Viterbi decoding according to Battail and Hagenauer in which the soft-output is determined using path metric differences along the maximum-likelihood path, i.e. "SOVA" decoding
• CPC Class Codes: H03M 13/6337 Error control coding in combination with channel estimation
• CPC Class Codes: H04L 1/002 Algorithms with memory of the previous states, e.g. Markovian models
• CPC Class Codes: H04L 1/0041 Arrangements at the transmitter end
• CPC Class Codes: H04L 1/0059 Convolutional codes
• CPC Class Codes: H04L 25/03 Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
• CPC Class Codes: H04L 25/067 providing soft decisions, i.e. decisions together with an estimate of reliability H04L25/068 and H04L25/069 take precedence; sequence estimation techniques H04L25/03178

Abstract

In one embodiment, a system provides for optimizing an error rate of data through a communication channel. The system includes a data generator operable to generate a training sequence as a Markov code, and to propagate the training sequence through the communication channel. The system also includes a Soft Output Viterbi Algorithm (SOVA) detector operable to estimate data values of the training sequence after propagation through the communication channel. The system also includes an optimizer operable to compare the estimated data values to the generated training sequence, to determine an error rate based on the comparison, and to change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

Claims

1. A system for optimizing an error rate of data through a communication channel, the system comprising:

a data generator operable to generate a training sequence as a Markov code, and to propagate the training sequence through the communication channel;

a Soft Output Viterbi Algorithm (SOVA) detector operable to estimate data values of the training sequence after propagation through the communication channel; and

an optimizer operable to compare the estimated data values to the generated training sequence, to determine an error rate based on the comparison, and to change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

2. The system of claim 1, wherein:

the channel is a storage media.

3. The system of claim 1, wherein:

the channel is memoryless.

4. The system of claim 1, wherein:

the channel is a channel with memory comprising intersymbol interference (ISI).

5. The system of claim 1, wherein:

the optimizer is further operable to configure subsequent data through the channel based on the determined error rate.

6. The system of claim 5, further comprising:

an encoder operable to configure the subsequent data with error correction coding.

7. The system of claim 6, wherein:

the encoder is further operable to configure the error correction coding based on the determined error rate.

8. A method of optimizing an error rate of data through a communication channel, the method comprising:

generating a training sequence as a Markov code;

propagating the training sequence through the communication channel;

estimating, with a Soft Output Viterbi Algorithm (SOVA) detector, data values of the training sequence after propagation through the communication channel;

comparing the estimated data values to the generated training sequence;

determining an error rate based on the comparison; and

changing the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

9. The method of claim 8, wherein:

the channel is a storage media.

10. The method of claim 8, wherein:

the channel is memoryless.

11. The system of claim 8, wherein:

the channel is a channel with memory comprising intersymbol interference (ISI).

12. The method of claim 8, further comprising:

configuring subsequent data through the channel based on the determined error rate.

13. The method of claim 12, further comprising:

configuring the subsequent data with error correction coding.

14. The method of claim 13, further comprising:

configuring the error correction coding based on the determined error rate.

15. A non-transitory computer readable medium comprising instructions that, when executed by a processor, are operable to direct the processor to optimize an error rate of data through a communication channel, the instructions further directing the processor to:

generate a training sequence as a Markov code source;

propagate the training sequence through the communication channel;

estimate, with a Soft Output Viterbi Algorithm (SOVA) detector, data values of the training sequence after propagation through the communication channel;

compare the estimated data values to the generated training sequence;

determine an error rate based on the comparison; and

change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

16. The computer readable medium of claim 15, wherein:

the channel is a storage media.

17. The computer readable medium of claim 15, wherein:

the channel is memoryless.

18. The computer readable medium of claim 15, wherein:

the channel is a channel with memory comprising intersymbol interference (ISI).

19. The computer readable medium of claim 15, further comprising instructions that direct the processor to:

configure subsequent data through the channel based on the determined error rate.

20. The computer readable medium of claim 19, further comprising instructions that direct the processor to:

configure the subsequent data with error correction coding; and

configure the error correction coding based on the determined error rate.