University of Southern California

Trelliware Technologies, Inc.

Trellisware Technologies, Inc.

Phunsak Thiennviboon

Keith Michael Chugg

Peter A Beerel

Georgios Dimou

Sushil Kumar Singh

Paul Kingsley Gray

Recep O Ozdag

Sunan Tugsinavisut

Georgios Dimitrios Dimou

The present invention relates to methods, apparatuses, and systems for performing data encoding involving encoding data bits according to an outer convolutional code to produce outer encoded bits processing the outer encoded bits using an interleaver and a logical unit to produce intermediate bits, wherein the logical unit receives a first number of input bits and produces a second number of corresponding output bits, the second number being less than the first number, and wherein the logical unit takes each of the first number of input bits into account in producing the second number of output bits, encoding the intermediate bits according to an inner convolutional code to produce inner encoded bits, wherein the inner convolutional code is characterized by at least two states, and combining the data bits and the inner encoded bits to produce encoded outputs.

Method and apparatus for communications using turbo like codes

Decoding an encoded signal (for example, a turbo encoded signal, a block encoded signal or the like) is performed by demodulating the received encoded signal to produce soft information, and iteratively processing the soft information with one or more soft-in/soft-output (SISO) modules. At least one of the SISO modules uses a tree structure to compute forward and backward state metrics. More generally, iterative detection is performed by receiving an input signal corresponding to one or more outputs of a module whose soft-inverse can be computed by running the forward-backward algorithm on a trellis representation of the module, and determining the soft inverse of the module by computing forward and backward state metrics of the received input signal using a tree structure.

Reduced-latency soft-in/soft-out module

A sequential decoder for decoding convolutional code is provided. The sequential decoder includes a computing device comprising a Fano technique. The Fano technique includes a plurality of variables that are normalized to change a point of reference of the technique. One of the variables is a current node metric. The variables are normalized such that the current node metric is set to approximately zero. Methods for using this decoder in applications that include periodic, hard deadlines such as real-time applications are also presented.

Sequential decoder for decoding of convolutional codes

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Phunsak Thiennviboon