Method and apparatus for soft information transfer between constituent processor circuits in a soft-value processing apparatus

#The present invention generally relates to soft information processing and particularly relates to the transfer of soft information within a soft-value processing apparatus.

#Soft information finds use in a variety of “belief propagation” systems, such as in communications signal decoding where, as a simple example, a series of soft decisions indicate whether corresponding bits in a received communication signal are more likely 1s or 0s. “Turbo” type receivers are a particularly well-known and useful example of soft information processing. Numerous technical references are available regarding the fundamental aspects of Turbo coding and decoding. See, for example, C. Berrou, A. Glavieux, et al., “Near Shannon limit error-correcting coding and decoding: Turbo-codes”, Proceedings of ICC 1993, Geneva, Switzerland, pp. 1064-1070; and C. Douillard, A. Picart, P. Didier, M. Jézéquel, C. Berrou, and A. Glavieux, “Iterative correction of intersymbol interference: turbo-equalization”, European Transactions on Telecommunications, Vol. 6, No. 5, September-October 1995, pp. 507-512.

#During the last two decades, the Turbo principle has been used in constructing a myriad of channel coding schemes, as well as iterative receiver approaches for applications beyond channel coding. However, in a basic Turbo decoding approach, the target receiver implements an iterative decoding structure in which two relatively simple constituent decoders exchange soft information, so that the probability estimates (“beliefs”) from one decoder aid the other decoder in refining its estimates. The iterative exchange of probability values—such as Log Likelihood Ratios or LLRs—allows Turbo-based coding and decoding schemes to achieve near Shannon limit performance, despite the use of relatively simple constituent codes.

#FIG. 1 is a block diagram of one embodiment of a soft-value processing apparatus.

#FIG. 2 is a block diagram of example compression processes applied to high-reliability and low-reliability soft values in a soft information set, for compression of that soft information set.

#FIG. 3 is a block diagram of one embodiment of parallel soft-value processes implemented in a soft-value processing apparatus.

#FIG. 1 illustrates a soft-value processing apparatus 10 that includes first and second constituent processor circuits 12-1 and 12-2. The first constituent processor circuit 12-1 includes a soft information set generation circuit 14 configured to generate a soft information set 16 comprising a plurality of soft values 18. The soft values 18 indicate the reliability of data detections performed with respect to a set of input data values 20. As a non-limiting example, the input data values 20 are digital samples representing symbols or bits conveyed in a received communication signal.

#In an example case, the soft values 18 are, for example, Log Likelihood Ratios (“LLRs”) representing a ratio of the probability that a corresponding received signal bit value is a “1” to the probability that it is a “0.” Further, as will be shown, the constituent processor circuits 12 implement one or more Turbo-based processes, such as a demodulator performing bit/symbol detection in a demodulation process, where the demodulator process provides soft values to a decoder process in a feedfoward path and/or where the decoder process provides soft values to the demodulator in a feedback path. Thus, the constituent processors 12-1 and 12-2 are, as non-limiting examples, a demodulation circuit and an associated decoding circuit, or are cooperative pair of decoder circuits.

#Turning back to the diagram, the first constituent processor circuit 12-1 further includes a soft value transfer circuit 22—abbreviated as “SV transfer circuit 22” in the diagram—that is configured to detect low-reliability soft values 24 among the plurality of soft values 18 in the soft information set 16 according to a defined reliability threshold 28. Such detection may be performed after generation of the soft information set 16, or on the fly as soft values 18 are being computed.