Abstract: Systems and methods are presented for controlling the peak-to-average-power of a baseband orthogonal-frequency-domain multiplexing (OFDM) signal by designating a subset of the available subcarriers as information-bearing data-subcarriers, and loading remaining subcarriers by symbols that are a function of the symbols loading the data-subcarriers. At the receiver, the data-dependent subcarriers are optionally combined with data-subcarriers to increase error protection.

Abstract: A method and system are provided in a wireless communications system comprising a plurality of nodes (users) working cooperatively. The system provides cooperative diversity by allowing nodes to actively share their antennas and other resources to obtain spatial diversity. The nodes receive the same message (information data) from a common source. Each node enhances the reliability of the message with a modern forward error correction (FEC) code, converts the FEC encoded message into an ensemble of symbols, divides the ensemble of symbols into packets, modulates, dithers and transmits the packets to a receiving node. The dithering process is performed by varying the signal amplitude, phase, frequency and/or symbol timing of the modulated packets. A unique dither pattern is assigned to each node. The receiving node captures a composite signal comprising the transmitted packets of all or most of the transmitting nodes in the cooperative communications system.

Abstract: A method of decoding channel outputs using an iterative decoder to provide hard decisions on information bits includes activating each SISO decoder of the iterative decoder to provide soft-decisions associated with the information bits. The method also includes computing a fidelity estimate and stopping decoding based on the fidelity estimate.

Abstract: Systems and techniques for transmitting an Irregular Systematic with Serially Concatenated Parity (Ir-S-SCP) are described. The techniques include generating an outer code comprising a plurality of bits using systematic bits as input, repeating the plurality of bits of the outer code a pre-determined number of times to generate at least a first set of repeated bits and a second set of repeated bits, serializing the generated sets of repeated bits, wherein each generated set is serialized in parallel with another generated set, interleaving the generated sets of repeated bits, generating an inner code, the inner code generated in part based on the interleaved sets, puncturing the inner code to output parity bits, wherein the puncturing is non-uniform and the puncturing is based at least in part on an incremental redundancy scheme, and transmitting the parity bits, wherein the transmitted parity bits and the systematic bits comprise the Ir-S-SCP code.

Abstract: Systems, devices and techniques for soft-in, soft-out (SISO) decoding can include accessing initial soft information on a series of data units received over a communication channel, using a cyclic graphical model to represent a coding scheme associated with the received data units, obtaining cycle-free graphical models for a plurality of second conditions allowable by the coding scheme, and generating soft-out decision information by using information that includes the obtained cycle-free graphical models and the initial soft information. The number of obtained cycle-free graphical models can be less than a total number of conditions associated with the cyclic graphical model. Soft decision information can include confidence levels for each data unit.

Abstract: Systems and methods for signal analysis are described. The method can include digitizing a signal modulated by a pseudo noise (PN) sequence, dividing the digitized signal into a plurality of sample blocks, and estimating a PN phase embedded in a sample block of the plurality of sample blocks using an iterative message passing algorithm (iMPA) executed on a redundant graphical model.

Abstract: A method of decoding channel outputs using an iterative decoder to provide hard decisions on information bits includes activating each SISO decoder of the iterative decoder to provide soft-decisions associated with the information bits. The method also includes computing a fidelity estimate and stopping decoding based on the fidelity estimate.

Abstract: A method and system are provided in a wireless communications system comprising a plurality of nodes (users) working cooperatively. The system provides cooperative diversity by allowing nodes to actively share their antennas and other resources to obtain spatial diversity. The nodes receive the same message (information data) from a common source. Each node enhances the reliability of the message with a modern forward error correction (FEC) code, converts the FEC encoded message into an ensemble of symbols, divides the ensemble of symbols into packets, modulates, dithers and transmits the packets to a receiving node. The dithering process is performed by varying the signal amplitude, phase, frequency and/or symbol timing of the modulated packets. A unique dither pattern is assigned to each node. The receiving node captures a composite signal comprising the transmitted packets of all or most of the transmitting nodes in the cooperative communications system.

Abstract: Systems and methods for signal analysis are described. The method can include digitizing a signal modulated by a pseudo noise (PN) sequence, dividing the digitized signal into a plurality of sample blocks, and estimating a PN phase embedded in a sample block of the plurality of sample blocks using an iterative message passing algorithm (iMPA) executed on a redundant graphical model.

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: In a digital information processing system wherein a model of a finite state machine (FSM) receiving a plurality of FSM inputs and producing a plurality of FSM outputs is represented by a reduced-state trellis and wherein the FSM inputs are defined on a base closed set of symbols, a novel method is presented for updating soft decision information on the FSM inputs into higher confidence information whereby (1) the soft decision information is inputted in a first index set, (2) a forward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce forward state metrics, (3) a backward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce backward state metrics, wherein the backward recursion is independent of the forward recursion and (4) the forward state metrics and the backward state metrics are operated on to produce the higher confidence information.

Abstract: A system for estimating inputs and outputs of a digital transmission system is disclosed. The system includes a receiver front-end configured to receive an observed digital signal in the digital transmission system. The system further includes forward and backward recursion elements, at least one forward channel estimator, at least one backward channel estimator, and a combiner. The forward recursion element is adapted to receive the observed digital signal, and generate a first sequence of soft information by performing a forward recursion. The forward channel estimator is adapted to receive the observed digital signal and the first sequence of soft information, and to estimate channel parameters using the first sequence of soft information. The backward recursion element is adapted to receive the observed digital signal, and generate a second sequence of soft information by performing a backward recursion.

Abstract: A method for co-channel interference identification and mitigation employs adaptive sequence detection in connection with a model composed of a signal of interest and a combination of other signals which constitute interference in a channel of interest, wherein the signal of interest is distinguished from the interference by adaptive tracking of signal parameters of all identifiable signals. In a particular embodiment, the process involves estimating the number and time spans of co-channel interference channels based on maximum likelihood estimation and minimum description length from training information derived from a single time division multiple access packet; and then applying the estimate to mitigation of co-channel interference at a receiver. Per-survivor-processing is one technique for adaptive sequence detection.

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: 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.

Abstract: A method for co-channel interference identification and mitigation employs adaptive sequence detection in connection with a model composed of a signal of interest and a combination other signals which constitute interference in a channel of interest, wherein the signal of interest is distinguished from the interference by adaptive tracking of signal parameters of all identifiable signals. In a particular embodiment, the process involves estimating the number and time spans of co-channel interference channels based on maximum likelihood estimation and minimum description length from training information derived from a single time division multiple access packet; and then applying the estimate to mitigation of co-channel interference at a receiver. Per-survivor-processing is one technique for adaptive sequence detection.

Abstract: 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.

Abstract: In a digital information processing system wherein a model of a finite state machine (FSM) receiving a plurality of FSM inputs and producing a plurality of FSM outputs is represented by a reduced-state trellis and wherein the FSM inputs are defined on a base closed set of symbols, a novel method is presented for updating soft decision information on the FSM inputs into higher confidence information whereby (1) the soft decision information is inputted in a first index set, (2) a forward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce forward state metrics, (3) a backward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce backward state metrics, wherein the backward recursion is independent of the forward recursion and (4) the forward state metrics and the backward state metrics are operated on to produce the higher confidence information.

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.