Abstract: Embodiments of the present invention provide Forward Error Correcting Code encoders and decoder structures that use DRAM in their memory designs. DRAM is a very attractive memory options in many electronic systems due to the high memory density provided by DRAM. However, the DRAM is typically not included in ASIC or FPGA implementations of encoders and decoders due to complex refresh requirements of DRAM that are required to maintain data stored in DRAM and may interfere with user access to the memory space during refresh cycles. Embodiments of the present invention provide FECC encoder and decoder structures that are implemented using DRAM that do not require complex refresh operations to be performed on the DRAM to ensure data integrity. Accordingly, embodiments of the present invention maximize memory density without the added complexity of introduced by the refresh requirements of DRAM.

Abstract: SAM is a very attractive memory option for systems due to its higher speed and reduced area when compared to RAM. However it is generally not used in implementations of FECCs due to its limitation to sequential accesses. According to the present invention, Forward Error Correcting Code encoder and decoder structures are shown to allow the use of SAM in their memory designs. Thus SAM is utilized in FECC implementations to achieve better area efficiency for the same amount of memory as well as higher throughput for the hardware implementations.

Abstract: Methods, apparatuses, and systems are presented for extracting information from a received signal resulting from a process capable of being represented as a finite state machine having a plurality of states, wherein transitions between the states can be represented by a trellis spanning a plurality of time indices, involving calculating branch metrics taking into account the received signal, calculating state metrics at each time index by taking into account the branch metrics and using a pipelined process, wherein the pipelined process is used to calculate state metrics at a first time index, wherein the pipelined process is then used to calculate state metrics at one or more non-adjacent time indices, and wherein the pipelined process is then used to calculate state metrics at an adjacent time index, and generating at least one output taking into account state metrics for states associated with at least one selected path through the trellis.

Abstract: Embodiments of the present invention provide Forward Error Correcting Code encoders and decoder structures that use DRAM in their memory designs. DRAM is a very attractive memory options in many electronic systems due to the high memory density provided by DRAM. However, the DRAM is typically not included in ASIC or FPGA implementations of encoders and decoders due to complex refresh requirements of DRAM that are required to maintain data stored in DRAM and may interfere with user access to the memory space during refresh cycles. Embodiments of the present invention provide FECC encoder and decoder structures that are implemented using DRAM that do not require complex refresh operations to be performed on the DRAM to ensure data integrity. Accordingly, embodiments of the present invention maximize memory density without the added complexity of introduced by the refresh requirements of DRAM.

Abstract: SAM is a very attractive memory option for systems due to its higher speed and reduced area when compared to RAM. However it is generally not used in implementations of FECCs due to its limitation to sequential accesses. According to the present invention, Forward Error Correcting Code encoder and decoder structures are shown to allow the use of SAM in their memory designs. Thus SAM is utilized in FECC implementations to achieve better area efficiency for the same amount of memory as well as higher throughput for the hardware implementations.

Abstract: 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.

Abstract: 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.

Abstract: 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.