Abstract: A method among the embodiments includes calculating a value of a parameter of a nonlinear model of a signal as transmitted into a transmission channel, and applying the calculated value to obtain an estimate of data values carried by the signal. Applications to multicarrier signals are described.

Abstract: A method of signal processing according to one of several embodiments includes estimating a deterministic component of a received signal. The estimating is based on an estimated response of a transmission channel. Based on the estimated deterministic component, a non-deterministic component of the received signal is estimated. Based on corrupted portions of the estimated non-deterministic component, a noise estimate is obtained, and the received signal is compensated based on the noise estimate. A method according to another embodiment includes replacing received samples at corrupted locations with values from a calculated model.

Abstract: A method of signal processing according to an embodiment includes estimating a response of a transmission channel during a symbol period. Based on an estimated response of the transmission channel, components of a model of a phase noise process during the symbol period are estimated. Based on the phase noise process model, an estimate of a symbol received during the symbol period is obtained.

Abstract: A receiver employs iterative decoding of packet data, where the packet data represents a data frame encoded with at least two logical dimensions. A logical dimension refers to a layer, or sub-layer, of a layered network architecture. Consequently, a first logical dimension of encoding might refer to error detection in a packet frame at the data link layer, while a second logical dimension of coding might refer to error detection/correction encoding at a physical layer. For example, a data frame might be divided into several packets, each with a corresponding cyclic redundancy check (CRC) value as coding in the first logical dimension, which are then transmitted with a convolutional code as coding in the second logical dimension. The receiver performs iterative decoding in the first and second logical dimensions until either i) all errors are identified and corrected or ii) another type of stopping condition is met.

Abstract: A trellis decoder decodes a stream of encoded symbols, including symbols of a first type (e.g. symbols encoded with a first trellis code) and symbols of a second type (e.g. encoded with a second, more robust, trellis code), without storing path indicators along a trellis for symbols of the first type. In this way, limited memory may be used to store path indicators along the trellis for symbols of the second type. This allows for more accurate decoding of the symbols of the second type. For transitions from symbols of the second type to symbols of the first type, states of the trellis decoder may be stored. In this way, paths may be traced back along the trellis for trellis decoding, without the path indicators for the symbols of the first type.

Abstract: Carrier phase recovery employs a single-axis blind cost criterion from the Bussgang class of functions, and its stochastic gradient, to generate a carrier phase error used to adjust a received and demodulated signal to near baseband. For one implementation, the estimate is derived in accordance with a Single-Axis Constant Modulus (SA-CM) criterion and its stochastic gradient via a SA-CM algorithm (SA-CMA). The carrier phase error is then used to adjust the carrier frequency and phase of the received and demodulated signal toward the frequency and phase of the carrier used to modulate the transmitted symbols, driving the carrier phase error to zero. The values used for the phase recovery may be either i) an IIR filtered signal, ii) a processed signal (e.g., decisions for the signal symbols), or iii) an equalized and processed signal.

Abstract: A single-axis receiver processing, for example, complex vestigial sideband modulated signals with an equalizer with forward and feedback filters. Forward and feedback filters have parameters that are initialized and adapted to steady state operation. Adaptive equalization employs linear predictive filtering and error term generation based on various cost criteria. Adaptive equalization includes recursive update of parameters for forward and feedback filtering as operation changes between linear and decision-feedback equalization of either single or multi-channel signals. An adaptive, linear predictive filter generates real-valued parameters that are employed to set the parameters of the feedback filter. In an initialization mode, filter parameters are set via a linear prediction filter to approximate the inverse of the channel's impulse/frequency response and a constant modulus error term for adaptation of the filter parameters.

Abstract: Symbol timing recovery employs a blind cost criterion from the Bussgang class of functions, and its stochastic gradient, to generate a timing phase error used to adjust sampling of received symbols. For one implementation, the estimate is derived in accordance with the Constant Modulus (CM) criterion and its gradient via the CM algorithm (CMA), and the estimate is calculated from a sequence of samples. This estimate is then used to adjust the period and phase of the sample sequence toward the period and phase of the transmitted symbols, driving the timing phase error to zero. The values used may be either i) samples themselves, ii) processed (e.g., interpolated) samples, or iii) equalized and processed samples. In addition, timing phase error estimates for other cost criteria, including the least mean squares algorithm, may be generated. These timing phase error estimates are selected either alone or in combination for deriving the timing phase error used to adjust the period and phase of the sample sequence.

Abstract: A digital receiver, that may be used to receive VSB/QAM digital television signals, includes an adaptive fine carrier recovery circuit that compensates for deviations in the carrier frequency or phase. The carrier recovery circuit de-rotates a signal including phase errors. Estimations of phase errors are filtered using a filter whose gain and bandwidth are adjusted adaptively. This allows the carrier recovery circuit to track phase/frequency offset without introducing significant jitter. In one embodiment, the receiver includes a DFE, and the adaptive carrier recovery circuit mitigates instability that might be associated with the DFE.

Abstract: A method for selecting an antenna direction setting for optimum signal reception prior to channel equalization provides a set of metrics, referred to as channel quality metrics (CQM), that characterize the quality of the received signal for a given antenna setting and a generic algorithm that uses these metrics to select the antenna setting for an optimum reception. This invention utilizes five main CQMs: a Signal Strength Metric (SSM), a minimum mean squared error of a decision feedback equalizer (MMSE (DFE)) channel quality metric, a MMSE for a linear equalizer (MMSE(LE)) channel quality metric, a Spectral Flatness Metric (SFM) and an interference degradation metric (IDM).