System and method for measuring channel quality information

• US 6,108,374 A
• Filed: 08/25/1997
• Issued: 08/22/2000
• Est. Priority Date: 08/25/1997
• Status: Expired due to Fees

• Current Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Ejzak, Richard P., Balachandran, Krishna, Nanda, Sanjiv, Kadaba, Srinivas R.
• Primary Examiner(s): Vo, Don N.
• Assistant Examiner(s): PHU, PHUONG M
• Application Number:
  US08/921,454
• Time in Patent Office: 1,093 Days
• Field of Search: 375/227, 375/221, 375/225, 375/222, 375/316, 375/377, 455/226.3
• US Class Current: 375/227
• CPC Class Codes: H04L 1/0009 by adapting the channel coding H04L1/1812 takes precedence
• CPC Class Codes: H04L 1/0015 characterised by the adaptation strategy
• CPC Class Codes: H04L 1/0026 Transmission of channel quality indication
• CPC Class Codes: H04L 1/0054 Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms
• CPC Class Codes: H04L 1/006 Trellis-coded modulation
• CPC Class Codes: H04L 1/12 by using return channel
• CPC Class Codes: H04L 1/20 using signal quality detector
• CPC Class Codes: H04W 36/302 due to low signal strength
• CPC Class Codes: H04W 72/54 based on quality criteria

Abstract

A system and method to measure channel quality in terms of signal to noise ratio for the transmission of coded signals over fading channels. A Viterbi decoder metric for the Maximum Likelihood path is used as a channel quality measure. This Euclidean distance metric is filtered in order to smooth out short term variations. The filtered or averaged metric is a reliable channel quality measure which remains consistent across different coded modulation schemes and at different mobile speeds. The filtered metric is mapped to the signal to noise ratio per symbol using a threshold based scheme. Use of this implicit signal to noise ratio estimate is used for the mobile assisted handoff and data rate adaptation in the transmitter.

Claims

1. A method for determining the signal to interference plus noise ratio, comprising the steps of:

establishing a set of path metrics corresponding to a set of signal to interference plus noise ratios;

receiving a digital signal;

determining a path metric for said digital signal;

establishing a set of signal to interference plus noise ratio values corresponding to a set of short term average of metric values, said short term average of metric values defined as M_i /∓

, wherein M_i is an average Euclidean decoder metric value and ∓

is the expectation value of a decoded path metric,

determining the decoded path metric from said received digital signal using a decoder, said decoded path metric defined as m_i,

averaging m_i to produce an average decoded path metric;

storing in a memory unit said average decoded path metric, said average decoded path metric defined as ∓

;

determining an estimated Euclidean distance metric; and

mapping said path metric to said corresponding signal to interference plus noise ratio in said set of signal to interference plus noise ratios.

2. The method of claim 1, wherein said digital signal is a coded signal.

3. The method of claim 1 wherein said digital signal is a trellis coded signal.

4. The method of claim 1 wherein the step of determining the estimated Euclidean distance metric is performed using the following equation:

space="preserve" listing-type="equation">M.sub.i =α

M.sub.i-1 +(1-α

)m.sub.i

where said estimated Euclidean distance metric is defined as M_i and α

is a predetermined filter coefficient which is greater than zero and less then 1.0.

5. The method of claim 4 including the steps of

determining a standard deviation of M_i ;

determining average metric thresholds defined as θ

_low and θ

_high based on the standard deviation of M_i ;

determining a value for M_i /μ

by dividing said value of M_i by said value of μ

;

mapping said value of M_i /μ

to a minimum value of said corresponding signal to interference plus noise ration if M_i /μ

is less than θ

_low ;

mapping said value of M_i /μ

to a maximum value of said corresponding signal to interference plus noise ratio if m/u is greater than θ

_high ; and

mapping said value of M_i /μ

to said corresponding signal to interference plus noise ratio.

6. The method of claim 1 wherein said decoder is a Viterbi decoder for a maximum likelihood path.

22. The system of claim 1 wherein the step of determining the estimated Euclidean distance metric is performed using the following equation

23. The system of claim 1 including the steps of

means for determining a standard deviation of M_i,

means for determining average metric thresholds defined as θ

_low and θ

_high based on the standard deviation of M_i,

means for determining a value for M_i /μ

by dividing said value of M_i by said value of μ

,

means for mapping said value of M_i /μ

to a minimum value of said corresponding signal to interference plus noise ration if M_i /μ

is less than θ

_low,

means for mapping said value of M_i /μ

to a maximum value of said corresponding signal to interference plus noise ratio if m/u is greater than θ

_high, and

means for mapping said value of M_i /μ

to said corresponding signal to interference plus noise ratio.

24. The system of claim 23 wherein said decoder is a Viterbi decoder for a maximum likelihood path.

7. A system for determining the signal to interference plus noise ratio, comprising:

means for establishing a set of path metrics corresponding to a set of signal to interference plus noise ratios;

means for receiving a digital signal;

means for determining a path metric for said digital signal;

means for establishing a set of signal to interference plus noise ratio values corresponding to a set of short tern average of metric values, said short term average of metric values defined as M_i /∓

, wherein M_i is an average Euclidean decoder metric value and ∓

is the expectation value of a decoded path metric;

means for determining the decoded path metric from said received digital signal using a decoder, said decoded path metric defined as m_i ;

means for smoothing m_i to produce an average decoded path metric;

means for storing in a memory unit said average decoded path metric, said average decoded path metric defined as ∓

,

means for determining an estimated Euclidean distance metric; and

means for mapping said path metric to said corresponding signal to interference plus noise ratio in said set of signal to interference plus noise ratios.

8. The system of claim 7, wherein said digital signal is a coded signal.

9. The system of claim 7, wherein said digital signal is a trellis coded signal.

10. The method of claim 8 wherein the means for determining the estimated Euclidean distance metric is performed using the following equation:

space="preserve" listing-type="equation">M.sub.i =α

M.sub.i-1 +(1-α

)m.sub.i

where said estimated Euclidean distance metric is defined as M_i and α

is a predetermined filter coefficient which is greater than zero and less then 1.0.

11. The system of claim 7 further including

means for determining a standard deviation of M_i ;

means for determining average metric thresholds defined as θ

_low and θ

_high based on the standard deviation of M_i ;

means for determining a value for M_i /μ

by dividing said value of M_i by said value of μ

;

means for mapping said value of M_i /μ

to a minimum value of said corresponding signal to interference plus noise ration if M_i /μ

is less than θ

_low ;

means for mapping said value of M_i /μ

to a maximum value of said corresponding signal to interference plus noise ratio if m/u is greater than θ

_high ; and

means for mapping said value of M_i /μ

to said corresponding signal to interference plus noise ratio.

12. The system of claim 8 wherein said decoder is a Viterbi decoder for a maximum likelihood path.

13. A method for determining the frame error rate, comprising the steps of:

establishing a set of path metrics corresponding to a set of frame error rates;

receiving a digital signal;

establishing a set of path metrics corresponding to a set of signal to interference plus noise ratios,

mapping the signal to interference plus noise ratios to the frame error rates,

determining a path metric for said digital signal;

establishing a set of signal to interference plus noise ratio values corresponding to a set of short term average of metric values, said short term average of metric values defined as M_i /∓

, wherein M_i is an average Euclidean decoder metric value and ∓

is the expectation value of a decoded path metric;

determining the decoded path metric from said received digital signal using a decoder, said decoded path metric defined as m_i ;

averaging m_i ;

storing in a memory unit said average decode oath metric, said average decoded path metric defined as ∓

;

determining an estimated Euclidean distance metric; and

mapping said path metric to said corresponding frame error rate in said set of frame error rates.

14. The method of claim 13, wherein said digital signal is a coded signal.

15. The method of claim 13, wherein said digital signal is a trellis coded signal.

16. The method of claim 15 wherein the step of determining the estimated Euclidean distance metric is performed using the following equation

17. The method of claim 16 further including the steps of

determining a standard deviation of M_i,

determining average metric thresholds defined as θ

_low and θ

_high based on the standard deviation of M_i,

determining a value for M_i /μ

by dividing said value of M_i by said value of μ

,

mapping said value of M_i /μ

to a minimum value of said corresponding signal to interference plus noise ration if M_i /μ

is less than θ

_low,

mapping said value of M_i /μ

to a maximum value of said corresponding signal to interference plus noise ratio if m/u is greater than θ

_high, and

mapping said value of M_i /μ

to said corresponding signal to interference plus noise ratio.

18. The method of claim 15 wherein said decoder is a Viterbi decoder for a maximum likelihood path.

19. A system for determining the frame error rate, comprising:

means for establishing a set of path metrics corresponding to a set of frame error rates;

means for establishing a set of path metrics corresponding to a set of signal to interference plus noise ratios, and

means for mapping the signal to interference plus noise ratios to the frame error rates,

means for receiving a digital signal;

means for determining a path metric for said digital signal;

means for establishing a set of signal to interference plus noise ratio values corresponding to a set of short term average of metric values, said short term average of metric values defined as M_i /∓

, wherein M_i is an average Euclidean decoder metric value and ∓

is the expectation value of a decoded path metric;

means for determining the decoded path metric from said received digital signal using a decoder, said decoded path metric defined as m_i ;

means for smoothing m_i ;

means for storing in a memory unit said average decoded path metric, said average decoded path metric defined as ∓

;

means for determining an estimated Euclidean distance metric; and

means for mapping said path metric to said corresponding frame error rate in said set of frame error rates.

20. The system of claim 19, wherein said digital signal is a coded signal.

21. The system of claim 19, wherein said digital signal is a trellis coded signal.

25. A method for determining the signal to interference plus noise ratio values from a channel quality metric, comprising the steps of:

receiving a digital signal consisting of a plurality of blocks of N symbols;

determining the channel quality metric for the digital signal;

determining an Euclidean distance metric, m_i, for the i^th block of the plurality of blocks;

determining a moving average metric M_i for the i^th block employing the following equation;

space="preserve" listing-type="equation">M.sub.i /=→

M.sub.i-1 =(1-/→

)m.sub.i

where →

is a predetermined coefficient which is greater than zero and less than 1.0.

establishing a rule for mapping the channel quality metric to the signal to interference plus noise ratio values; and

mapping the channel quality metric to the signal to interference plus noise ratio values using the rule.

26. The method of claim 25 wherein the step of determining the channel quality metric further includes the step of processing the digital signal to obtain estimates of the transmitted data and determining the channel quality metric from these estimates.

27. The method of claim 25 wherein each of the plurality of blocks of N symbols is derived from encoding and modulating a block of J information bits.

28. The method of claim 25 wherein the Euclidean distance metric, m_i, is a value produced by a minimum Euclidean distance decoder.

29. The method of claim 28 wherein the minimum Euclidean distance decoder is a Viterbi decoder.

30. The method of claim 25 wherein the step of determining the Euclidean distance metric m_i further includes the steps of

determining a estimated decoded sequence {a_k } of the digital signal using a decoder,

re-encoding the decoded sequence {a_k } to obtain an estimate symbol sequence {s_k } of the digital signal,

computing the Euclidean distance metric using the following equation ##EQU19## where {r_k }, k=1, 2, . . . , N is the received sequence and {α

_k }, k=1, 2, . . . , N denotes the estimated channel coefficients.

31. The method of claim 30 wherein the decoder is an exhaustive search decoder.

32. The method of claim 30 wherein the decoder is a reduced search decoder.

33. The method of claim 25 wherein the channel quality metric is the moving average metric.

34. The method of claim 25 wherein the channel quality metric is predicted from the moving average metric using a linear predictor.

35. The method of claim 34 further including the steps of generating an optimal linear predictor h_l,

storing the optimal linear predictor h_l into a memory unit;

and

determining future moving average metric M_i+D values, for the (i+D)^th time slot block, from the current value and (p-1) previous values of the moving average metric using the following relation ##EQU20## where p is the order of the optimal linear predictor h_l, l is index that starts at zero and ends at p-1, and D represents the future time slot index.

36. The method of claim 25 wherein the step of establishing the mapping from the channel quality metric to the signal to interference plus noise ratio further includes the steps of

choosing thresholds defined as θ

_low and θ

_high such that θ

_low <

1 and θ

_high >

1, and

generating a table of target metric values μ

_n in descending order of signal to interference plus noise for a range of interest using the following rule;

space="preserve" listing-type="equation">μ

.sub.n =(N*E.sub.s)/(10.sup.(0.1*SINR).sub.n for n=1, 2 . . . K

where N is the number of symbols per block, E_S is the energy per symbol, SINR_n, is the n^th signal to interference plus noise value in the table, and K is the size of the table.

37. The method of claim 36 wherein the target metric values μ

_n are the expected values of the respective channel quality metrics for each SINR_n.

38. The method of claim 36 wherein the thresholds θ

_low and θ

_high are chosen based on the standard deviation of M_i.

39. The method of claim 36 wherein the step of mapping the channel quality metric to the signal to interference plus noise ratio values using the rule further includes the steps of

determining values M_i /μ

₁ by dividing said value of M_i by said value of μ

_n, n=1, 2, . . . , K from pre-determined table of target metric values,

mapping said value of M_i to a maximum value of said corresponding signal to interference plus noise ration if M_i /μ

₁ is less than θ

_low,

mapping said value of M_i to a minimum value of said corresponding signal to interference plus noise ratio if M_i /μ

₁ is greater than θ

_high, and

mapping said value of M_i to said corresponding signal to interference plus noise ratio SINR_n for n such that θ

_low <

M_i /μ

_n <

θ

_high.