Abstract: Efficient design to implement LDPC decoder. The efficient design presented herein provides for a solution that is much easier, smaller, and has less complexity than other possible solutions. The use of a ping-pong memory structure (or pseudo-dual port memory structure) in conjunction with a metric generator near the decoder's front end allows parallel bit/check node processing. An intelligently operating barrel shifter operates with a message passing memory that is operable to store updated edges messages with respect to check nodes as well as updated edges messages with respect to bit nodes. Using an efficient addressing scheme allows the same memory structure to store the two types of edges messages with respect to bit nodes: (1) corresponding to information bits and (2) corresponding to parity bits. In addition, an intelligently designed hardware macro block may be instantiated a number of times into the decoder design to support ever greater design efficiency.

Abstract: Fast min*? (min-star-minus) or max*? (max-star-minus) circuit in LDPC (Low Density Parity Check) decoder. A novel and efficient approach by which certain of the calculations required to perform check node processing within various types of decoders is presented. The functionality and architectures presented herein are applicable to LDPC decoders and may also be employed within other types of decoders that are operable to decode other types of coded signals as well. The parallel and sometimes simultaneous calculation and determination of certain parts of the overall resultant of the max*? and/or min*? processing allows for very fast operation when compared to prior art approaches.

Abstract: Efficient design to implement min**/min**? or max**/max**? functions in LDPC (Low Density Parity Check) decoders. When compared to prior art approaches, the novel and efficient implementation presented herein allows for the use of substantially less hardware and surface area within an actual communication device implemented to perform these calculations. In certain embodiments, the min** processing (and/or max** processing) is implemented to assist in the computationally intensive calculations required to decoded LDPC coded signals. In one instance, this is operable to assist in check node processing when decoding LDPC coded signals. However, the efficient principles and architectures presented herein may be implemented within other communication device types to decode other types of coded signals as well.

Abstract: Decoding LDPC (Low Density Parity Check) code and graphs using multiplication (or addition in log-domain) on both sides of bipartite graph. A novel approach of 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: A method for decoding a word received at a current time instant into a symbol of a trellis code. The trellis code corresponds to a trellis diagram having N states associated with the current time instant. Each of the N states corresponds to at least one incoming branch. Each of the incoming branches is associated with a symbol of the trellis code. The branch metrics are computed for the incoming branches such that a branch metric represents a distance between the received word and a symbol associated with the corresponding branch. The branch metric is represented by fewer bits than a squared Euclidian metric representation of the distance. For each of the N states, a node metric is computed based on corresponding branch metrics and one of the incoming branches associated with the state is selected. One of the N states is selected as an optimal state based on the node metrics. The symbol associated with the selected incoming branch corresponding to the optimal state is the decoded word.

Abstract: LDPC (Low Density Parity Check) coded signal decoding using parallel and simultaneous bit node and check node processing. This novel approach to decoding of LDPC coded signals may be described as being LDPC bit-check parallel decoding. In some alternative embodiment, the approach to decoding LDPC coded signals may be modified to LDPC symbol-check parallel decoding or LDPC hybrid-check parallel decoding. A novel approach is presented by which the edge messages with respect to the bit nodes and the edge messages with respect to the check nodes may be updated simultaneously and in parallel to one another. Appropriately constructed executing orders direct the sequence of simultaneous operation of updating the edge messages at both nodes types (e.g., edge and check). For various types of LDPC coded signals, including parallel-block LDPC coded signals, this approach can perform decoding processing in almost half of the time as provided by previous decoding approaches.

Abstract: A method for asymmetrical MIMO wireless communication begins by determining a number of transmission antennas for the asymmetrical MIMO wireless communication. The method continues by determining a number of reception antennas for the asymmetrical MIMO wireless communication. The method continues by, when the number of transmission antennas exceeds the number of reception antennas, using spatial time block coding for the asymmetrical MIMO wireless communication. The method continues by, when the number of transmission antennas does not exceed the number of reception antennas, using spatial multiplexing for the asymmetrical MIMO wireless communication.

Abstract: A wireless local area network (WLAN) transmitter includes a baseband processing module and a plurality of radio frequency (RF) transmitters. The baseband processing module is operably coupled to process data by scrambling the data in accordance with a pseudo random sequence to produce scrambled data. The processing of the data continues by selecting one of a plurality of encoding modes based on a mode selection signal. The processing of the data continues by encoding the scrambled data in accordance with the one of the plurality of encoding modes to produce encoded data. The processing of the data continues by determining a number of transmit streams based on the mode selection signal. The processing of the data further continues by converting the encoded data into streams of symbols in accordance with the number of transmit streams and the mode selection signal.

Abstract: A communications system, having a combination Reed-Solomon encoder and a Turbo-Code encoder Data frame configuration which may be changed to accommodate embedded submarkers of known value are embedded in with the data order to aid synchronization in the receiver system, by providing strings of known symbols. The string of known symbols may be the same as the symbols within a training header that appears at the beginning of a data frame. Frame parameters may be tailored to individual users and may be controlled by information pertaining to receivers, such as bit error rate, of the receiver. Additional headers may be interspersed within the data in order to assist in receiver synchronization. Frames of data may be acquired quickly by a receiver by having a string of symbols representing the phase offset between successive header symbols in the header training sequence in order to determine the carrier offset.

Abstract: A wireless local area network (WLAN) transmitter includes a MAC module, a PLCP module, and a PMD module. The Medium Access Control (MAC) module is operably coupled to convert a MAC Service Data Unit (MSDU) into a MAC Protocol Data Unit (MPDU) in accordance with a WLAN protocol. The Physical Layer Convergence Procedure (PLCP) Module is operably coupled to convert the MPDU into a PLCP Protocol Data Unit (PPDU) in accordance with the WLAN protocol. The Physical Medium Dependent (PMD) module is operably coupled to convert the PPDU into a plurality of radio frequency (RF) signals in accordance with one of a plurality of operating modes of the WLAN protocol, wherein the plurality of operating modes includes multiple input and multiple output combinations.

Abstract: Efficient LDPC code decoding with new minus operator in a finite precision radix system. A new mathematical operator is introduced and applied to the decoding of LDPC coded signals. This new operator is referred to as the min†? (min-dagger minus) operator herein. This min†? processing is appropriately applied during the updating of the edge messages with respect to the variable nodes. In a bit level decoding approach to decoding LDPC coded signals, the updating of the edge messages with respect to the bit nodes is performed using the new min†? operator. This approach provides very comparable performance to min** processing as also applied to updating of the edge messages with respect to the bit nodes and may also provide for a significant savings in hardware. Also, within finite precision radix systems, the new min†? operator provides a means by which always meaningful results may be achieved during the decoding processing.

Abstract: IPHD (Iterative Parallel Hybrid Decoding) of various MLC (Multi-Level Code) signals. Various embodiments are provided by which IPHD may be performed on MLC LDPC (Multi-Level Code Low Density Parity Check) coded modulation signals mapped using a plurality of mappings. This IPHD may also be performed on MLC LDPC coded modulation signals mapped using only a singe mapping as well. In addition, various embodiments are provided by which IPHD may be performed on ML TC (Multi-Level Turbo Code) signals. These principles of IPHD, shown with respect to various embodiments IPHD of MLC LDPC coded modulation signals as well as the IPHD of ML TC signals, may be extended to performing IPHD of other signal types as well. Generally speaking, based on the degree of the MLC signal, a corresponding number of parallel paths operate in cooperation to decode the various levels of the MLC signal.

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: Decoding LDPC (Low Density Parity Check) code with new operators based on min* operator. New approximate operators are provided that may be employed to assist in calculating one or a minimum value (or a maximum value) when decoding various coded signals. In the context of LDPC decoding that involves both bit node processing and check node processing, either of these new operators (i.e., the min† (min-dagger) operator or the min? (min-prime) operator) may be employed to perform the check node processing that involves updating the edge messages with respect to the check nodes. Either of these new operators, min† operator or min? operator, is shown herein to be a better approximate operator to the min** operator.

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: A communications system, having a combination Reed-Solomon encoder and a Turbo-Code encoder Data frame configuration which may be changed to accommodate embedded submarkers of known value. The submarkers are embedded in with the data order to aid synchronization in the receiver system, by providing strings of known symbols. The string of known symbols may be the same as the symbols within a training header that appears at the beginning of a data frame. Frame parameters may be tailored to individual users and may be controlled by information pertaining to receivers, such as bit error rate of the receiver. Additional headers may be interspersed within the data in order to assist in receiver synchronization. Frames of data may be acquired quickly by a receiver by having a string of symbols representing the phase offset between successive header symbols in the header training sequence in order to determine the carrier offset.

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: 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: 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: 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-Soloman 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.