Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L1/00—Arrangements for detecting or preventing errors in the information received
  • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
  • H04L1/0056—Systems characterized by the type of code used
  • H04L1/0057—Block codes
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
  • H04L25/03006—Arrangements for removing intersymbol interference
  • H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
  • H04L25/03019—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
  • H04L25/03006—Arrangements for removing intersymbol interference
  • H04L25/03178—Arrangements involving sequence estimation techniques
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L25/00—Baseband systems
  • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
  • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
  • H04L25/03006—Arrangements for removing intersymbol interference
  • H04L25/03178—Arrangements involving sequence estimation techniques
  • H04L25/03184—Details concerning the metric

Definitions

• the present invention relates to coded modulation techniques, and more particularly to techniques for performing equalization and decoding in a communications system that utilizes coded modulation.
• BCM block coded modulation
• CIR channel impulse response
• the foregoing and other objects are achieved in methods and apparatus for performing combined equalization and decoding of codewords encoded in a block coded modulation (BCM) symbol sequence that has been received from a channel, wherein each of the codewords comprises a plurality of symbols.
• BCM block coded modulation
• this is accomplished by determining channel impulse response estimates corresponding to each received symbol of the BCM symbol sequence.
• a codeword of the received BCM symbol sequence is then decoded using a maximum likelihood decoding trellis with a path metric that depends upon the channel impulse response estimates and that includes at least one term representing intersymbol interference resulting from one or more symbols of one or more previously decoded codewords.
• the decoding step is then repeated until all of the codewords of the received BCM symbol sequence have been decoded.
• the path metric is determined in accordance with an equation that is a sum of squared differences associated with one BCM codeword, each one of said differences being a difference between a current received sample and a sum term, wherein the sum term is a sum of one or more symbols of a candidate BCM codeword and one or more symbols of at least one previously decoded BCM codeword.
• the path metric may be determined in accordance with the equation:
• the above-mentioned decoding step may be a forward decoding step, and the technique may further comprise the step of performing backward decoding of the received BCM signal.
• the above-mentioned decoding step may be a backward decoding step.
• the technique further includes retaining a list of N s best previously decoded BCM codewords.
• the step of decoding is performed for each of the N s best BCM codewords.
• the path metric may be determined in accordance with a function that includes a term corresponding to received signal samples at fractional symbol spacing and one or more terms that are proportional to channel impulse response coefficients at fractional symbol sampling locations.
• the block coded modulation symbol sequence that has been received from the channel includes deliberately introduced intersymbol interference.
• combined equalization and decoding of codewords encoded in a block coded modulation (BCM) symbol sequence that has been received from a channel, wherein each of the codewords comprises a plurality of symbols is performed by determining channel impulse response estimates corresponding to each received symbol of the BCM symbol sequence.
• a maximum likelihood sequence estimation trellis is then advanced from one stage to a next stage by updating a path metric having dependence upon the channel impulse response estimates and upon symbols of a codeword corresponding to a state in a previous trellis stage.
• a completed trellis is produced by repeating the trellis-advancing step until all stages of the trellis are completed.
• the codewords contained in the BCM symbol sequence are then decoded by using a traceback along the best path in the completed MLSE trellis.
• the path metric may be determined in accordance with the equation:
• r n ⁇ +k are the received symbols corresponding to a BCM codeword
• N /oc£ is the block length of one BCM codeword
• s ⁇ (n +k) are the symbols of a candidate BCM codeword for the codeword being currently decoded
• s (P) (w +k-l) are the symbols of the BCM codeword corresponding to state i in the previous MLSE trellis stage.
• the path metric may alternatively be determined in accordance with a function that includes a term corresponding to received signal samples at fractional symbol spacing and one or more terms that are proportional to channel impulse response coefficients at fractional symbol sampling locations.
• the above-described step of advancing the maximum likelihood sequence estimation trellis from one stage to the next stage is performed in a forward direction
• the technique further comprises the step of advancing the maximum likelihood sequence estimation trellis in a reverse direction.
• the above-described step of advancing the maximum likelihood sequence estimation trellis from one stage to the next stage may be performed in a backward direction.
• the block coded modulation symbol sequence that has been received from the channel may include deliberately introduced intersymbol interference.
• combined equalization and decoding of codewords encoded in a block coded modulation (BCM) symbol sequence that has been received from a channel, wherein each of the codewords comprises a plurality of symbols is performed by determining channel impulse response (CIR) estimates corresponding to each received symbol of the BCM symbol sequence.
• CIR channel impulse response
• a maximum likelihood sequence estimation trellis is advanced from one stage to a next stage by updating a path metric having dependence upon the channel impulse response estimates and upon symbols of a codeword corresponding to a state in a previous trellis stage, wherein the path metric includes at least one term that represents intersymbol interference resulting from symbols of previously decoded codewords.
• a completed trellis is produced by repeating the trellis-advancing step until all stages of the trellis are completed. Then, the codewords contained in the BCM symbol sequence are decoded by using a traceback along the best path in the completed MLSE trellis.
• the path metric may be determined in accordance with an iterative equation that computes a squared Euclidean distance between received symbols across one codeword of the received BCM signal and a term representing a sum of symbols of a BCM codeword corresponding to a state at stage k of the MLSE trellis and one or more symbols of one or more BCM codewords corresponding to a state at trellis stage (k-1).
• the path metric, T ⁇ may be determined in accordance with the equation:
• N b ⁇ ock is the block length of one BCM codeword
• s ⁇ (n +k) are the symbols of the BCM codeword corresponding to state j at stage k of the MLSE trellis
• s ( ⁇ ) ( « +k-l) are the symbols of the BCM codeword corresponding to state d at stage (k-1) of the MLSE trellis
• s (l) (n +k-2) are the symbols of the tentative decisions for BCM codeword (k - 2), obtained by tracing back from state t at stage (k-1) of the MLSE trellis.
• the above-mentioned step of advancing the maximum likelihood sequence estimation trellis from one stage to the next stage may be performed in a forward direction, and the technique may further comprise the step of advancing the maximum likelihood sequence estimation trellis in a reverse direction.
• the step of advancing the maximum likelihood sequence estimation trellis from one stage to the next stage may alternatively be performed in a backward direction.
• the technique further includes retaining a list of N s best previously decoded codewords.
• the above- mentioned step of decoding is performed for each of the N s best previously decoded codewords.
• the path metric may be determined in accordance with a function that includes a term corresponding to received signal samples at fractional symbol spacing and one or more terms that are proportional to channel impulse response coefficients at fractional symbol sampling locations.
• the block coded modulation symbol sequence that has been received from the channel may include deliberately introduced intersymbol interference.
• FIG. 1 is a schematic of an encoding process of an exemplary BCM scheme, based on the 8-PSK constellation;
• FIGS. 2a and 2b illustrate a sample BCM encoding operation, and the resulting coded bits-to-symbol mapping, respectively;
• FIG. 3 illustrates an exemplary slot structure for use with the inventive combined equalization and decoding algorithm;
• FIG. 4 illustrates a maximum likelihood (ML) decoding trellis for the exemplary BCM scheme considered herein;
• FIG. 5 is a diagram illustrating the two-dimensional memory aspects associated with interleaving of symbols;
• FIG. 6 is a flow chart depicting the steps of a combined equalization/decoding process in accordance with one embodiment of the invention
• FIG. 7 is a flow chart depicting the steps of a combined equalization/decoding process in accordance with another embodiment of the invention.
• FIG. 8 is a flow chart depicting the steps of a combined equalization/decoding process in accordance with yet another embodiment of the invention.
• Such program instructions may, for example be stored on any of a variety of computer readable storage media, including but not limited to: a random access memory (RAM); read only memory (ROM); magnetic storage device such as magnetic disk/diskette or tape; and optical storage medium such as compact disk ROM (CD ROM).
• RAM random access memory
• ROM read only memory
• CD ROM compact disk ROM
• the invention may also be considered to be embodied in the computer readable storage medium having stored therein the program instructions.
• dedicated hardware such as Application Specific Integrated Circuits, or "ASICs" may be designed to carry out the various functions described in detail below.
• the block coded modulation (BCM) encoding device consists of a parallel connection of block encoders, with the outputs of the encoders being used to select a symbol from the signal constellation, by the bits-to-symbol mapper.
• the inventive combined equalization and decoding algorithm will be described for this particular BCM scheme. However, it will be readily apparent to a person of ordinary skill in the art that the invention is not limited to the exemplary embodiments, but instead can be applied to any other BCM scheme as well.
• FIGS. 2a and 2b an example of a BCM scheme with the following component codes is shown:
• FIG. 2a shows a sample encoding operation
• the inventive combined equalization and decoding algorithm will be described for this particular BCM scheme. However, as stated above, the invention can be applied to any other BCM scheme as well.
• FIG. 3 An exemplary slot structure for the inventive combined equalization and decoding algorithm for BCM is shown in FIG. 3.
• This slot structure is a modified version of the IS- 136 slot structure, and is presented to facilitate understanding of the invention, since the invention may be applied to any slot structure.
• a description of the standard IS-136 slot structure may be found in Electronics Industries Association, Dual Mode Mobile Station-Base Station Compatibility Standard, IS-54 Rev. A (incorporating EIA/TIA 553), EIA/TIA Project Number 2398, Oct. 1990.
• CIR channel impulse response
• Step 2 Obtain a refined CIR estimate, C s , by running an adaptive tracking algorithm over the same data.
• the CIR interpolation is done using an interpolation filter (Wiener filter, Nyquist filter, etc.). Interpolation is one approach to obtain CIR values corresponding to each symbol within a slot. Other CIR estimation/interpolation methods can also be used.
• Interpolation filter Wiener filter, Nyquist filter, etc.
• Symbol interleaving is essential in order to achieve the full benefit of a BCM scheme.
• a two-slot interleaving scheme is considered (consisting of a total of 272 data symbols).
• the interleaver is a rectangular matrix of dimensions N row x N co ⁇ , wherein the data is written in by rows and written out by columns.
• the size of the interleaver is limited by the overall encoding and decoding delay that is permissible.
• N col can be advantageously chosen to be the block length (N block ) of the BCM codeword.
• N col 4 and a 68 x 4 rectangular interleaver is obtained, as shown in Table 1.
• Each row of the interleaver matrix contains one BCM codeword. Observe that the interleaver spans two TDMA time slots, as is allowed in the IS-136 standard.
• Table 1 The procedure for obtaining the decoding trellis for a single (n, k) linear block code is described in J. Wolf, "Efficient maximum likelihood decoding of linear block codes using a trellis," IEEE Transactions on Information Theory, vol. IT-24, pp. 76-80, Jan. 1978. Wolf's Method enables maximum likelihood (ML) decoding of linear block codes by applying the Viterbi Algorithm to the decoding trellis. A more detailed description of the Viterbi Algorithm may be found in G.D. Forney, "The Viterbi algorithm, " Proceedings of the IEEE, vol. 61, pp. 268-278, Mar. 1973.
• Generating the decoding trellis for BCM is a multi-dimensional extension of Wolf's method, because BCM schemes have multiple component block codes. Each BCM scheme has its unique decoding trellis.
• the decoding trellis for the BCM example considered herein is given in FIG. 4.
• the main advantages of the BCM trellis are that all the component codes are decoded simultaneously and it yields the optimum maximum likelihood result. Further, trellis decoding of BCM provides a significant reduction in the computational complexity of the BCM decoding process. Still further, it must be noted that maximum likelihood (ML) decoding of BCM yields a performance gain over sequential and iterative decoding approaches.
• FIG. 5 shows an exemplary interleaver matrix 500 indicating the relationship between symbols.
• Data to be coded is written into the interleaver 500 by row and read out by column.
• Each row contains all the symbols comprising one BCM codeword (e.g., the first row 501).
• the coding produces a memory, or relationship between the symbols.
• the coding-related memory extends for one row or codeword. Since data is read out by column and transmitted in this sequence (e.g., the first column 503), the data in the transmitted sequence are related by the memory (intersymbol interference) introduced by the channel.
• the channel memory is defined by the channel impulse response.
• a set of these symbols is optimally decoded using a maximum likelihood sequence estimator (MLSE).
• MLSE maximum likelihood sequence estimator
• These two decoders are independent and the former, typically, works on the output of the latter. This results in suboptimal performance because information relevant to the second decoding could be destroyed by incorrect processing by the first decoding.
• Joint MLSE decoding using a two- dimensional decoder is therefore the optimal approach, because now both rows and columns are decoded using the same input information rather than having one decoder modify the input information to the other.
• joint decoding is more complex.
• the various aspects of the invention are techniques for performing joint demodulation and decoding, and also alternative techniques with lower complexity, while maintaining near-optimal performance.
• ISI Inter-Symbol Interference
• the equalization is done via ISI cancellation using decoder decisions on past symbols (which contribute to the ISI in the current received symbol).
• the symbols to be transmitted are entered into the interleaver matrix by rows and read out by columns.
• each row corresponds to a BCM codeword.
• the constituent symbols of a BCM code are distributed over two time slots on successive TDMA frames.
• the received symbols are written into the de-interleaver by columns.
• Each row of received symbols (corresponding to a BCM codeword) is decoded using a maximum likelihood (ML) approach as described below.
• each symbol will experience ISI from previous or future symbols in the same column, depending on whether the channel impulse response (CIR) has post-cursors or pre-cursors respectively.
• CIR channel impulse response
• the channel has a two ray impulse response with the two rays being one symbol apart in duration.
• ⁇ c ⁇ ,c 2 ⁇ are the CIR coefficients
• s(ni+k) is the current symbol
• s(n j +k-l) is the symbol transmitted immediately before the current symbol.
• the index i is the column index.
• ⁇ (n j +k) is the additive noise (AWGN) sample or a sample of the interference (co-channel) which is herein assumed to be a Gaussian random variable.
• the metric used in the BCM decoder is Y ⁇ (for row k and the j BCM codeword)
• s n +k are the symbols corresponding to the j BCM codeword for row k
• s(n j +k-l) are the symbols of the BCM codeword decoded in the previous row (k - 1), which are used for ISI cancellation.
• This technique of decoding is referred to as ISI cancellation decoding and is related to using a decision feedback equalizer (DFE) for canceling the effect of post-cursors (previous symbols).
• DFE decision feedback equalizer
• This technique is akin to decision feedback equalization with the improvement of having the decoder as part of the feedback section, thereby minimizing the probability of feeding back symbol errors which would otherwise cause error propagation, commonly experienced in decision feedback equalizers.
• a codeword is selected under a ML criterion, using an ISI cancellation metric. The symbols of this codeword are then carried over to the next row and used for ISI cancellation while decoding the next BCM codeword.
• Step 601 Obtain CIR estimates corresponding to each received symbol, as described above.
• Step 603 Decode row k using the ML decoding trellis using a squared Euclidean distance metric that includes a term representing the ISI due to symbol(s) from previously decoded BCM codeword(s). Equation (2) is an example of one such metric.
• Step 605 Repeat Step 603 until entire data (2 slots) is decoded.
• step 603 is well-suited to the case where ISI extends substantially only from one symbol to the next. It will be recognized that for those cases where the ISI extends through multiple symbols, the technique can be extended to include past symbol decisions from several previous rows or succeeding rows with alteration of the metric accordingly.
• step 603 in the equalization/decoding process described above is done with more than a single candidate (i.e., s(n j +k-l)).
• the best N s candidate BCM codewords from row (k - 1) are used to pick the best N s candidate codewords for row k.
• This approach is called a list tree algorithm (LTA), and is described in greater detail in the following section. List Tree Algorithm
• the unreliable branch metrics could cause an error in the decoded BCM codeword.
• the combined equalization and decoding technique employs a decision feedbacklike structure (for ISI cancellation)
• a decoding error in a BCM codeword
• GVA generalized Viterbi algorithm
• LTA list tree algorithm
• the LTA keeps a list of N s best BCM codewords, where N s denotes the number of survivors or codewords in the LTA.
• N s denotes the number of survivors or codewords in the LTA.
• each of these N s candidate codewords from row (k - 1) can be used for ISI cancellation in decoding the next BCM codeword (row k) using the metric given in equation (2).
• the following N s codewords would be retained for row (k - 1) ⁇ ⁇ 0 1 V J (« ⁇ + t-l ),i , (» J (n 2 +jfc- l ),- . c ⁇ v( « 3 +i-l),j , o lI v J ( « 4 +/t-l) L
• the decoding procedure Using the N s codewords from row (k - 1), the decoding procedure will yield a total of N 2 codewords (for row k), some of which may be identical. At this stage the path metrics associated with the N s paths are sorted and the best N s (distinct) codewords
• the ISI in the BCM symbols in row k comes from the symbols of the BCM codeword in row (k - 1).
• G.D. Forney "Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference," IEEE Transactions in Information Theory, vol. IT-18, pp.
• an MLSE approach for BCM decoding in the presence of ISI requires M-states, wherein each state in the trellis represents a BCM codeword.
• the path metric I that is used in the BCM decoder for a transition in the MLSE
• Step 701 Obtain CIR estimates corresponding to each received symbol, as described above.
• Step 703 Perform MLSE (using the Viterbi algorithm) by updating a squared Euclidean distance path metric, y , that
• Equation (5) is an example of one such metric.
• MLSE trellis are completed and then decode the BCM codewords by using the traceback along the best path (as in any Viterbi algorithm).
• step 703 is well-suited to the case where ISI extends substantially only from one symbol to the next. It will be recognized that for those cases where the ISI extends through multiple symbols, the inventive MLSE technique can be extended by appropriately modifying the metric to include terms representing symbols of a BCM codeword corresponding to states in several previous or succeeding trellis stages.
• each state of the MLSE corresponds to a distinct BCM codeword, unlike the LTA approach wherein additional steps are necessary to ensure the same. If the channel model has L taps, then the MLSE trellis has M ( " -states.
• a sub-optimal approach based on using a hybrid between the inventive MLSE approach and the inventive ISI cancellation approach is used in order to reduce the computational complexity of the MLSE approach.
• the MLSE approach requires M -states.
• an M- state trellis can be used. To compute the branch metrics at time n ⁇ , trace back along each trellis path (at time n j - 1) to determine s(n j - 2), and then use these tentatively decoded symbols.
• T The metric, T , that is used in the BCM decoder for a transition in the MLSE trellis from state i at stage (k - 1) of the trellis to state j to stage k of the trellis is
• s w (« +k) are the symbols of the BCM codeword corresponding to state j (row k)
• s (f) ( « +k-l) are the symbols of the BCM codeword corresponding to state fi in row (k - 1)
• s (f) (n +k-2) are the tentative decisions for row (k - 2), obtained by tracing back from state H of the MLSE trellis (at time (n j +k-1)).
• Step 801 Obtain CIR estimates corresponding to each received symbol, as described above.
• Step 803 Perform hybrid MLSE-ISI cancellation algorithm by updating a squared Euclidean distance path metric, T * , that
• Equation (6) is an example of one such path metric.
• step 803 is well-suited to the case where ISI extends substantially only from one symbol to the next. It will be recognized that for those cases where the ISI extends through multiple symbols, the inventive hybrid MLSE technique can be extended by appropriately modifying the metric to include multiple ISI cancellation terms. A number of embodiments of the inventive technique for performing combined equalization/decoding of block code modulated information have been described. In yet further aspects of the invention, additional enhancements may be applied to these techniques. These enhancements will now be described.
• bidirectional decoding The technique of decoding the data in a slot in both the forward and backward directions is referred to as bidirectional decoding. See, for example, S. Chennakeshu, R.D. Koilpillai, and R.L. Toy, "A low complexity MLSE equalizer for U.S. Digital Cellular mobile receivers," G.E. Technical Information Series , Dec. 1991, GE-CRD Technical Report (Class 2) 91CRD247; and Y. Liu, "Bi-directional equalization for TDMA communication systems over land mobile radio channels," Proceedings of IEEE GLOBECOM '91, pp. 41.1.1-41.1.5, Dec. 1991.
• the main motivation for considering bidirectional decoding is to improve the performance of the decoder when the channel exhibits severe non-minimum phase condition, that is, when
• the metric given in equation (2) is minimized over all of the BCM codewords using the symbols (corresponding to each BCM codeword). If lc ⁇ n j )! ⁇ 0, the metric (given by equation (2)) is approximately the same for all values of s +k-l) , (i.e., for all the different BCM codewords) thereby increasing the likelihood of an error in decoding the BCM codeword. This problem is characteristic of decision feedback-based ISI cancellation schemes.
• the metrics for forward and backward decoding respectively using ISI cancellation decoding of the BCM codeword in row k are given by:
• Both forward and backward decoding are done and the direction that provides a better metric over each data segment (block of symbols) is selected and its data decisions are used as the decoded output.
• forward decoding is in accordance with:
• forward decoding proceeds in accordance with:
• Backward hybrid decoding is in accordance with:
• a second, lower complexity option is to perform either forward or backward decoding or a combination of both (i.e., performing forward decoding up to a point, and backward decoding to that same point).
• Typical criteria include received signal strength (RSSI), and comparison of metric values in forward decoding and backward decoding.
• Fractionally Spaced Metrics For fractional delay spreads the performance of the combined equalization and decoding algorithm can be significantly improved by using fractionally spaced metrics as described herein.
• the branch metric In order to mitigate the model mismatch between the true channel and the estimated channel (for fractional delay spreads), the branch metric must be modified.
• a method for doing this for an MLSE equalizer as applied to the DAMPS system has been described in Y. Wan, Q. Liu, and A.M. Sendyk, "A fractionary-spaced maximum likelihood sequence estimation receiver in a multipath fading environment," Proceedings oflEEE ICASSP '92, pp. 689-692, May 1992.
• an approach is now presented for using fractionally spaced metrics for the inventive application of combined equalization and decoding of BCM.
• the ISI extends over several symbol periods because of the pulse shaping function.
• the pulse shape decays rapidly over a few symbol periods, and hence, can be truncated.
• the metric (using a sampling rate of two samples per symbol for the received signal) for the ISI cancellation approach is given by
• the use of fractionally spaced metrics can be applied to the other BCM decoding methods as well.
• the metric would be:
• fractionally spaced hybrid technique would use the following metric:
• Fractionally spaced metrics are used when the channel has fractional delay spread. Typically, additional taps are needed to model fractional delay channels. Hence, as an illustrative example, a channel model with three taps is shown.

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