GFSK detector

Patent metadata

Field	Value
Publication number	US-10756935-B2
Application number	US-201916547886-A
Country	US
Kind code	B2
Filing date	Aug 22, 2019
Priority date	Aug 22, 2018
Publication date	Aug 25, 2020
Grant date	Aug 25, 2020

Abstract

A Gaussian frequency shift keying (GFSK) detector for decoding a GFSK signal. The detector includes: a multi-symbol detector and a Viterbi decoder. The multi-symbol detector is configured to: receive a series of samples representing a received GFSK modulated signal; and generate, for each set of samples representing an N-symbol sequence of the GFSK modulated signal, a plurality of soft decision values that indicate the probability that the N-symbol sequence is each possible N-symbol pattern, wherein N is an integer greater than or equal to two. The Viterbi decoder is configured to estimate each N-symbol sequence using a Viterbi decoding algorithm wherein the soft decision values for the N-symbol sequence are used as branch metrics in the Viterbi decoding algorithm.

First claim

What is claimed is:

1. A Gaussian frequency shift keying (GFSK) detector comprising:
a multi-symbol detector configured to:
receive a series of samples representing a received GFSK modulated signal, and
generate, for each set of samples representing an N-symbol sequence of the GFSK modulated signal, a plurality of soft decision values that indicate the probability that the N-symbol sequence is each possible N-symbol pattern, wherein N is an integer greater than or equal to two; and

a Viterbi decoder configured to estimate an M-symbol sequence of the GFSK modulated signal using a Viterbi decoding algorithm wherein the soft decision values for the N-symbol sequences of that M-symbol sequence are used as branch metrics in the Viterbi decoding algorithm, wherein M is an integer greater than N.

2. The GFSK detector of claim 1 , wherein the plurality of soft decision values comprises a soft decision value for each possible N-symbol pattern that indicates the probability that the N-symbol sequence is that N-symbol pattern.

3. The GFSK detector of claim 1 , wherein the Viterbi decoder is configured to estimate the M-symbol sequence by determining a maximum likelihood path through a trellis of valid N-symbol transitions.

4. The GFSK detector of claim 3 , wherein the trellis comprises a plurality of branches, each branch represents a valid path from an N-symbol pattern at a first time instance to an N-symbol pattern at a second time instance.

5. The GFSK detector of claim 4 , wherein:
the plurality of soft decision values comprises a soft decision value for each possible N-symbol pattern that indicates the probability that the N-symbol sequence is that N-symbol pattern; and
the Viterbi decoder is configured to, for each set of samples representing an N-symbol sequence, use the soft decision value for a particular N-symbol pattern as the branch metric for any branch that terminates in that particular N-symbol pattern.

6. The GFSK detector of claim 3 , wherein the Viterbi decoder is configured to, for each set of samples representing an N-symbol sequence, generate a path metric for each possible N-symbol pattern that indicates the likelihood that the most likely path through the trellis terminates in that N-symbol pattern based on the set of soft decision metrics for that set of samples.

7. The GFSK detector of claim 1 , wherein the series of samples comprises at least three samples per symbol, and the plurality of soft decision values comprises at least three sets of soft decisions values, each set of soft decision values indicating the likelihood that the N-symbol sequence is each possible N-symbol pattern based on a different one of the at least three samples of a symbol being a centre sample of the symbol.

8. The GFSK detector of claim 7 , wherein:
the Viterbi decoder is configured to estimate the M-symbol sequence by determining a maximum likelihood path through a trellis of valid N-symbol transitions;
the Viterbi decoder is configured to, for each set of samples representing an N-symbol sequence, generate a path metric for each possible N-symbol pattern that indicates the likelihood that the most likely path through the trellis terminates in that N-symbol pattern based on the set of soft decision metrics for that set of samples; and
the GFSK detector further comprises:
two additional Viterbi decoders, each Viterbi decoder configured to generate a path metric for each possible N-symbol pattern according to a Viterbi decoding algorithm based on one set of soft decision values; and
a timing adjustment module configured to generate a timing adjustment signal based on the path metrics generated by the Viterbi decoders to cause the timing of the sampling of the GFSK modulated signal to be adjusted.

9. The GFSK detector of claim 1 , wherein the multi-symbol detector is configured to generate the soft decision values by comparing the samples to a plurality of reference patterns that represent each possible N-symbol pattern.

10. The GFSK detector of claim 9 , wherein each reference pattern of the plurality of reference patterns are not channel corrected prior to comparing the reference patterns to the set of samples representing the N-symbol sequence.

11. The GFSK detector of claim 9 , wherein the multi-symbol detector comprises a plurality of correlators, each correlator is configured to correlate the samples with one of the plurality of reference patterns to generate a portion of the soft decision values.

12. The GFSK detector claim 11 , wherein each correlator is configured to, each sample period, correlate the most recent X samples to the reference pattern and generate a soft decision value based on the correlation, wherein X=K*N and K is a number of samples per symbol.

13. The GFSK detector of claim 9 , wherein each reference pattern represents expected frequency patterns for one of the possible N-symbol patterns.

14. The GFSK detector of claim 1 , wherein N=4.

15. The GFSK detector of claim 1 , wherein the GFSK modulated signal is a Bluetooth signal or a Bluetooth LE signal.

16. A GFSK receiver comprising the GFSK detector as set forth in claim 1 .

17. A method of decoding a received Gaussian frequency shift keying (GFSK) modulated signal at a GFSK receiver, the method comprising:
receiving a series of samples representing the received GFSK modulated signal;
generating, each symbol period of the GFSK modulated signal, a plurality of soft decision values that indicate the likelihood that a most recent N-symbol sequence of samples is each possible N-symbol pattern, wherein N is an integer greater than or equal to two; and
estimating an M-symbol sequence of the GFSK modulated signal using a Viterbi decoding algorithm wherein, each symbol period, at least a portion of the plurality of soft decision values are used as branch metrics of the Viterbi decoding algorithm, wherein M is an integer greater than N.

18. A GFSK detector configured to perform the method as set forth in claim 17 .

19. A non-transitory computer readable storage medium having stored thereon computer readable instructions that, when executed at a computer system, cause the computer system to perform the method as set forth in claim 17 .

20. A non-transitory computer readable storage medium having stored thereon a computer readable description of the GFSK detector as set forth in claim 1 that, when processed in an integrated circuit manufacturing system, causes the integrated circuit manufacturing system to manufacture an integrated circuit embodying the GFSK detector.

Assignees

• Imagination Tech Ltd

Inventors

• Murrin Paul
• Anderson Adrian John

Classifications

• H04L27/14 Primary

  Demodulator circuits; Receiver circuits · CPC title

• H03M13/256

  with trellis coding, e.g. with convolutional codes and TCM · CPC title

• H03M13/41

  using the Viterbi algorithm or Viterbi processors · CPC title

• H04L2027/0065

  Frequency error detectors (H04L2027/0067 takes precedence) · CPC title

• H04L1/0059

  Convolutional codes · CPC title