Abstract: In a satellite gateway, data is transmitted over a downstream channel at different throughput rates. Data destined for each subscriber/receiver is assigned a throughput rate depending upon the downstream signal quality of that subscriber/receiver. The downstream data is parsed to extract data packets. The data packets are then loaded into packet queues based on an identifier within such packets. The queues represent a bandwidth efficiency or throughput rate that can be currently tolerated by specific subscribers that may also be based on the current signal quality at a subscriber location. The parsed data traffic is processed based on the profile of the plurality of profiles to produce processed data traffic, and transmitted from the packet queues over a downstream channel.

Abstract: In a satellite gateway, data is transmitted over a downstream channel at different throughput rates. Data destined for each subscriber/receiver is assigned a throughput rate depending upon the downstream signal quality of that subscriber/receiver. The downstream data is parsed to extract data packets. The data packets are then loaded into packet queues based on an identifier within such packets. The queues represent a bandwidth efficiency or throughput rate that can be currently tolerated by specific subscribers that may also be based on the current signal quality at a subscriber location. The parsed data traffic is processed based on the profile of the plurality of profiles to produce processed data traffic, and transmitted from the packet queues over a downstream channel.

Abstract: In a DOCSIS based satellite gateway data is transmitted over a single downstream channel, at different throughput rates. Data destined for each subscriber/receiver is assigned a throughput rate depending upon the downstream signal quality of that subscriber/receiver. To accomplish this, the downstream DOCSIS MAC data is parsed to extract DOCSIS packets. The DOCSIS packets are then loaded into packet queues based on an identifier within such packets such as the MAC destination address or SID. Each of the queues represents a bandwidth efficiency or throughput rate that can be currently tolerated by specific subscribers based on the current signal quality being experienced at the subscriber location. A PHY-MAP describing the downstream data structure to be transmitted and inserted into the downstream data. Data is extracted from the packet queues in queue blocks as defined by the PHY-MAP.

Abstract: In a DOCSIS based satellite gateway data is transmitted over a single downstream channel, at different throughput rates. Data destined for each subscriber/receiver is assigned a throughput rate depending upon the downstream signal quality of that subscriber/receiver. To accomplish this, the downstream DOCSIS MAC data is parsed to extract DOCSIS packets. The DOCSIS packets are then loaded into packet queues based on an identifier within such packets such as the MAC destination address or SID. Each of the queues represents a bandwidth efficiency or throughput rate that can be currently tolerated by specific subscribers based on the current signal quality being experienced at the subscriber location. A PHY-MAP describing the downstream data structure to be transmitted and inserted into the downstream data. Data is extracted from the packet queues in queue blocks as defined by the PHY-MAP.

Abstract: In a DOCSIS based satellite gateway data is transmitted over a single downstream channel, at different throughput rates. Data destined for each subscriber/receiver is assigned a throughput rate depending upon the downstream signal quality of that subscriber/receiver. To accomplish this, the downstream DOCSIS MAC data is parsed to extract DOCSIS packets. The DOCSIS packets are then loaded into packet queues based on an identifier within such packets such as the MAC destination address or SID. Each of the queues represents a bandwidth efficiency or throughput rate that can be currently tolerated by specific subscribers based on the current signal quality being experienced at the subscriber location. A PHY-MAP describing the downstream data structure to be transmitted and inserted into the downstream data. Data is extracted from the packet queues in queue blocks as defined by the PHY-MAP.

Abstract: LDPC (Low Density Parity Check) coded modulation symbol decoding. Symbol decoding is supported by appropriately modifying an LDPC tripartite graph to eliminate the bit nodes thereby generating an LDPC bipartite graph (such that symbol nodes are appropriately mapped directly to check nodes thereby obviating the bit nodes). The edges that communicatively couple the symbol nodes to the check nodes are labeled appropriately to support symbol decoding of the LDPC coded modulation signal. The iterative decoding processing may involve updating the check nodes as well as estimating the symbol sequence and updating the symbol nodes. In some embodiments, an alternative hybrid decoding approach may be performed such that a combination of bit level and symbol level decoding is performed. This LDPC symbol decoding out-performs bit decoding only. In addition, it provides comparable or better performance of bit decoding involving iterative updating of the associated metrics.

Abstract: A method for parallel concatenated (Turbo) encoding and decoding. Turbo encoders receive a sequence of input data tuples and encode them. The input sequence may correspond to a sequence of an original data source, or to an already coded data sequence such as provided by a Reed-Solomon encoder. A turbo encoder generally comprises two or more encoders separated by one or more interleavers. The input data tuples may be interleaved using a modulo scheme in which the interleaving is according to some method (such as block or random interleaving) with the added stipulation that the input tuples may be interleaved only to interleaved positions having the same modulo-N (where N is an integer) as they have in the input data sequence. If all the input tuples are encoded by all encoders then output tuples can be chosen sequentially from the encoders and no tuples will be missed.

Abstract: LDPC (Low Density Parity Check) coded modulation hybrid decoding. A novel approach is presented wherein a combination of bit decoding and symbol level decoding (e.g., hybrid decoding) is performed for LDPC coded signals. Check node updating and symbol node updating are successively and alternatively performed on bit edge messages for a predetermined number of decoding iterations or until a sufficient degree of precision is achieved. The symbol node updating of the bit edge messages involves using symbol metrics corresponding to the symbol being decoded as well as the bit edge messages most recently updated by check node updating. The check node updating of the bit edge messages involves using the bit edge messages most recently updated by symbol node updating. The symbol node updating also involves computing possible soft symbol estimates for the symbol during each decoding iteration.

Abstract: Decoding LDPC (Low Density Parity Check) code and graphs using multiplication (or addition in log-domain) on both sides of bipartite graph. A means for decoding LDPC coded signals is presented whereby edge messages may be updated using only multiplication (or log domain addition). By appropriate modification of the various calculations that need to be performed when updating edge messages, the calculations may be reduced to only performing product of terms functions. When implementing such functionality in hardware within a communication device that is operable to decode LDPC coded signals, this reduction in processing complexity greatly eases the actual hardware's complexity as well. A significant savings in processing resources, memory, memory management concerns, and other performance driving parameters may be made.

Abstract: Bandwidth efficient coded modulation scheme based on MLC (Multi-Level Code) signals having multiple maps. The use of multiple maps is adapted to various types of coded signals including multi-level LDPC coded modulation signals and other MLC signals to provide for a significant performance gain in the continual effort trying to reach towards Shannon's limit. In the instance of LDPC coded signals, various level LDPC codewords are generated from individual corresponding LDPC encoders. These various level LDPC codewords are arranged into a number of sub-blocks. Encoded bits from multiple level LDPC codewords within each of the sub-blocks are arranged to form symbols that are mapped according to at least two modulations. Each modulation includes a constellation shape and a corresponding mapping. This use of multiple mappings provides for improved performance when compared to encoders that employ only a single mapping.

Abstract: LDPC (Low Density Parity Check) code size adjustment by shortening and puncturing. A variety of LDPC coded signals may be generated from an initial LDPC code using selected shortening and puncturing. Using LDPC code size adjustment approach, a single communication device whose hardware design is capable of processing the original LDPC code is also capable to process the various other LDPC codes constructed from the original LDPC code after undergoing appropriate shortening and puncturing. This provides significant design simplification and reduction in complexity because the same hardware can be implemented to accommodate the various LDPC codes generated from the original LDPC code. Therefore, a multi-LDPC code capable communication device can be implemented that is capable to process several of the generated LDPC codes. This approach allows for great flexibility in the LDPC code design, in that, the original code rate can be maintained after performing the shortening and puncturing.

Abstract: Implementation of LDPC (Low Density Parity Check) decoder by sweeping through sub-matrices. A novel approach is presented by which an LDPC coded signal is decoded processing the columns and rows of the individual sub-matrices of the low density parity check matrix corresponding to the LDPC code. The low density parity check matrix can partitioned into rows and columns according to each of the sub-matrices of it, and each of those sub-matrices also includes corresponding rows and columns. For example, when performing bit node processing, the same columns of at 1 or more sub-matrices can be processed together (e.g., all 1st columns in 1 or more sub-matrices, all 2nd columns in 1 or more sub-matrices, etc.). Analogously, when performing check node processing, the same rows of 1 or more sub-matrices can be processed together (e.g., all 1st rows in 1 or more sub-matrices, all 2nd rows in 1 or more sub-matrices, etc.).

Abstract: Sub-matrix-based implementation of LDPC (Low Density Parity Check) decoder. A novel approach is presented by which an LDPC coded signal is decoded by processing 1 sub-matrix at a time. A low density parity check matrix corresponding to the LDPC code includes rows and columns of sub-matrices. For example, when performing bit node processing, 1 or more sub-matrices in a column are processed; when performing check node processing, 1 or more sub-matrices in a row are processed. If desired, when performing bit node processing, the sub-matrices in each column are successively processed together (e.g., all column 1 sub-matrices, all column 2 sub-matrices, etc.). Analogously, when performing check node processing, the sub-matrices in each row can be successively processed together (e.g., all row 1 sub-matrices, all row 2 sub-matrices in row 2, etc.).

Abstract: Partial-parallel implementation of LDPC (Low Density Parity Check) decoder. A novel approach is presented by which a selected number of cycles is performed during each of bit node processing and check node processing when performing error correction decoding of an LDPC coded signal. The number of cycles of each of bit node processing and check node processing need not be the same. At least one functional block, component, portion of hardware, or calculation can be used during both of the bit node processing and check node processing thereby conserving space with an efficient use of processing resources. At a minimum, a semi-parallel approach can be performed where 2 cycles are performed during each of bit node processing and check node processing. Alternatively, more than 2 cycles can be performed for each of bit node processing and check node processing.

Abstract: Iterative metric updating when decoding LDPC (Low Density Parity Check) coded signals and LDPC coded modulation signals. A novel approach is presented for updating the bit metrics employed when performing iterative decoding of LDPC coded signals. This bit metric updating is also applicable to decoding of signals that have been generated using combined LDPC coding and modulation encoding to generate LDPC coded modulation signals. In addition, the bit metric updating is also extendible to decoding of LDPC variable code rate and/or variable modulation signals whose code rate and/or modulation may vary as frequently as on a symbol by symbol basis. By ensuring that the bit metrics are updated during the various iterations of the iterative decoding processing, a higher performance can be achieved than when the bit metrics remain as fixed values during the iterative decoding processing.

Abstract: Variable modulation within combined LDPC (Low Density Parity Check) coding and modulation coding systems. A novel approach is presented for variable modulation encoding of LDPC coded symbols. In addition, LDPC encoding, that generates an LDPC variable code rate signal, may also be performed as well. The encoding can generate an LDPC variable code rate and/or modulation signal whose code rate and/or modulation may vary as frequently as on a symbol by symbol basis. Some embodiments employ a common constellation shape for all of the symbols of the signal sequence, yet individual symbols may be mapped according different mappings of the commonly shaped constellation; such an embodiment may be viewed as generating a LDPC variable mapped signal. In general, any one or more of the code rate, constellation shape, or mapping of the individual symbols of a signal sequence may vary as frequently as on a symbol by symbol basis.

Abstract: Single stage implementation of min*, max*, min and/or max to perform state metric calculation in soft-in soft-out (SISO) decoder. This allows for calculation of state metrics in an extremely efficient, fast manner. When performing min or max calculations, comparisons are made using 2 element combinations of the available inputs. Subsequently, logic circuitry employs the results of the 2 element comparisons the smallest (min) or largest (max) input. The max or min implementations may be employed as part of the max* and/or min* implementations. For max* and/or min* implementations, simultaneous calculation of appropriate values is performed while determining which input is the smallest or largest. Thereafter, the determination of which input is the smallest or largest is used to select the appropriate resultant value (of the values calculated) for max* and/or min*. Various degrees of precision are employed for the log correction values within the max* and/or min* implementations.

Abstract: Efficient construction of LDPC (Low Density Parity Check) codes with corresponding parity check matrix having CSI (Cyclic Shifted Identity) sub-matrices. These constructed LDPC codes can be implemented in multiple-input-multiple-output (MIMO) communication systems. One LDPC code construction approach uses CSI sub-matrix shift values whose shift values are checked instead of non-zero element positions within the parity check matrix (or its corresponding sub-matrices). When designing an LDPC code, this approach is efficient to find and avoid cycles (or loops) in the LDPC code's corresponding bipartite graph. Another approach involves GRS (Generalized Reed-Solomon) code based LDPC code construction. These LDPC codes can be implemented in a wide variety of communication devices, including those implemented in wireless communication systems that comply with the recommendation practices and standards being developed by the IEEE 802.11n Task Group (i.e., the Task Group that is working to develop a standard for 802.

Abstract: Efficient construction of LDPC (Low Density Parity Check) codes with corresponding parity check matrix having CSI (Cyclic Shifted Identity) sub-matrices. These constructed LDPC codes can be implemented in multiple-input-multiple-output (MIMO) communication systems. One LDPC code construction approach uses CSI sub-matrix shift values whose shift values are checked instead of non-zero element positions within the parity check matrix (or its corresponding sub-matrices). When designing an LDPC code, this approach is efficient to find and avoid cycles (or loops) in the LDPC code's corresponding bipartite graph. Another approach involves GRS (Generalized Reed-Solomon) code based LDPC code construction. These LDPC codes can be implemented in a wide variety of communication devices, including those implemented in wireless communication systems that comply with the recommendation practices and standards being developed by the IEEE 802.11n Task Group (i.e., the Task Group that is working to develop a standard for 802.

Abstract: Variable code rate and signal constellation turbo trellis coded modulation (TTCM) codec. A common trellis is employed at both ends of a communication system (in an encoder and decoder) to code and decode data at different rates. The encoding employs a single TTCM encoder whose output bits may be selectively punctured to support multiple modulations (constellations and mappings) according to a rate control sequence. A single TTCM decoder is operable to decode each of the various rates at which the data is encoded by the TTCM encoder. The rate control sequence may include a number of rate controls arranged in a period that is repeated during encoding and decoding. Either one or both of the encoder and decoder may adaptively select a new rate control sequence based on operating conditions of the communication system, such as a change in signal to noise ratio (SNR).