EP0467522A2
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EP
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Maximum likelihood sequence detector
Assignee: Nokia Mobile Phones UK Ltd
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Filing: 1991-06-04
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Granted: —
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Expires: 2011-06-04
Abstract
Abstract A maximum likelihood detector using the Viterbi algorithm for estimating a sequence of data bits received over a communication channel. Depending on the constraint length (C), a plurality of different states is associated with the transmitted bits (e.g. 16 if C=4). The detector comprises various data sources (13, 14, 17, 23, 24, 28) relating respectively to state transition probabilities (branch metrics, previous partial path metrics) and observed values of the received bits. Means are provided for calculating the partial path metrics for each state using values from said data sources. The calculating means comprise a common adder/accumulator (1) for performing repeated additive arithmetic operations and for storing the cumulative result thereof. Multiplexing means (10-15) are also provided for selectively coupling the data sources in a predetermined order to the adder/accumulator to implement the partial path metric calculation. The architecture is compatible with VLSI techniques and enables the detector to be implemented using minimal semiconductor area. The detector may be used independently as an equaliser or a decoder but in a preferred embodiment both these functions re-use the same common hardware in series enabling both the equaliser and decoder to be implemented in a single integrated circuit (i.e. on one 'chip').
Inventors
- 👤 Andrew Cooper
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-08 16:18
CPC Classifications
H; H04; H04L; H04L25/00; H04L25/02; H04L25/03; H04L25/03006; H04L25/03178; H04L25/03184; H04L25/03197; H03; H03M; H03M13/00; H03M13/37; H03M13/39; H03M13/41
Claims Analysis
Total Claims: 19
Claim 1
Independent Claim
127 words
A maximum likelihood (ML) detector for estimating a data symbol in a sequence of transmitted data symbols received over a communication channel, wherein a plurality of different states is associated with the transmission of said data symbols, the detector comprising a plurality of data sources relating respectively to state transition probabilities and observed values of the received data symbols, and means for calculating the partial path metrics for each state using values from said data sources, characterised in that the calculating means comprise common add/accumulate means for performing repeated additive arithmetic operations and for storing the cumulative result thereof, and in that multiplexing means are provided for selectively coupling said data sources in a predetermined order to said add/accumulate means to implement the partial path metric calculation.
Claim 2
Dependent Claim
127 words
A maximum likelihood (ML) detector for estimating a data symbol in a sequence of transmitted data symbols received over a communication channel, wherein a plurality of different states is associated with the transmission of said data symbols, the detector comprising a plurality of data sources relating respectively to state transition probabilities and observed values of the received data symbols, and means for calculating the partial path metrics for each state using values from said data sources, characterised in that the calculating means comprise common add/accumulate means for performing repeated additive arithmetic operations and for storing the cumulative result thereof, and in that multiplexing means are provided for selectively coupling said data sources in a predetermined order to said add/accumulate means to implement the partial path metric calculation.
Claim 3
Dependent Claim
31 words
A ML detector as claimed in claim 1, wherein the data sources relating to state transition probabilities include data sources relating respectively to previous partial path metrics and to branch metrics.
Claim 4
Dependent Claim
87 words
A ML detector as claimed in claim 2, including a further data source relating to terms characteristic of the communication channel, and means for calculating the branch metrics using values from said further data source and from the data source relating to the observed values of the received data symbols, wherein the branch metric calculating means comprise said common add/accumulate means, the multiplexing means being adapted for selectively coupling said data sources in a predetermined order to said common add/accumulate means to implement the branch metric calculations.
Claim 5
Dependent Claim
25 words
A ML detector as claimed in any of claims 1 to 3, wherein the data sources comprise memory means for storing the state transition probabilities.
Claim 6
Dependent Claim
28 words
A ML detector as claimed in any of claims 1 to 4, wherein the data sources include memory means for storing observed values of the received data symbols.
Claim 7
Dependent Claim
138 words
A ML detector as claimed in any of the preceding claims, wherein the predetermined order in which the multiplexing means couples the data sources to the add/accumulate means is such that for each state at a given time a first partial path metric is calculated in respect of a state transition corresponding to the transmission of a first symbol type, and subsequently a second partial path metric is calculated in respect of a state transition corresponding to the transmission of a second symbol type, means being provided for storing said first partial path metric, the detector further including means for comparing the first partial path metric in the storage means and the second partial path metric in the add/accumulate means and for selecting the larger of said first and second partial path metrics for each state in turn.
Claim 8
Dependent Claim
80 words
A ML detector as claimed in any of claims 1 to 5, wherein the calculating means comprises a first add/accumulate means for calculating for each state in turn a first partial path metric in respect of a state transition corresponding to the transmission of a first symbol type, and a second add/accumulate means for calculating for each state in turn a second partial path metric in respect of a state transition corresponding to the transmission of a second symbol type.
Claim 9
Dependent Claim
21 words
A ML detector as claimed in claim 7, wherein said first and second add/accumulate means are arranged to operate in parallel.
Claim 10
Dependent Claim
46 words
A ML detector as claimed in claim 7 or claim 8, including means for comparing the first and second partial path metrics in said first and second add/accumulate means respectively and for selecting the larger of said first and second partial path metrics for each state.
Claim 11
Dependent Claim
29 words
A ML detector as claimed in claim 6 or claim 9, including memory means for storing the larger of the first and second partial path metrics for each state.
Claim 12
Dependent Claim
36 words
A ML detector as claimed in claim 10, including means for storing for each state either a first or a second symbol depending on which of the first and second partial path metric is the larger.
Claim 13
Dependent Claim
29 words
A ML detector as claimed in claim 11, including means for reading from the data symbol storage means an estimate of the most likely sequence of transmitted data symbols.
Claim 14
Dependent Claim
27 words
A ML detector as claimed in any of the preceding claims, including means for generating information indicative of the relative confidence level of each estimated data symbol.
Claim 15
Dependent Claim
29 words
An equaliser for equalising a signal which has become distorted by transmission through a dispersive communications channel, comprising a ML detector as claimed in any of the preceding claims.
Claim 16
Dependent Claim
23 words
A decoding arrangement for decoding a convolutionally encoded data signal, comprising a ML detector as claimed in any of claims 1 to 13.
Claim 17
Dependent Claim
84 words
An arrangement for receiving a convolutionally encoded signal transmitted over a dispersive communications channel, comprising means for equalising the signal and means for subsequently decoding the equalised signal, wherein the equalising means and the decoding means comprise a common ML detector as claimed in any of claims 1 to 13, the multiplexing means being adapted to couple the data sources to the common add/accumulate means in an order such that the ML detector functions sequentially as the equalising means and as the decoding means.
Claim 18
Dependent Claim
43 words
An arrangement for receiving a convolutionally encoded signal transmitted over a dispersive communications channel, comprising means for equalising the signal and means for subsequently decoding the equalised signal, wherein the equalising means and the decoding means comprise a common maximum likelihood (ML) detector.
Claim 19
Dependent Claim
43 words
An arrangement for receiving a convolutionally encoded signal transmitted over a dispersive communications channel, comprising means for equalising the signal and means for subsequently decoding the equalised signal, wherein the equalising means and the decoding means comprise a common maximum likelihood (ML) detector.
References Cited (Backward Citations)
EP0302330A2
Cited
EP0398690A2
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