Abstract: A single-axis receiver processes, for example, a complex vestigial sideband (VSB) modulated signal with an equalizer accounting for DC offset within the modulated signal. The DC offset embedded in a received signal may degrade the process of equalization. The equalizer includes a method of blind estimation of the DC offset for removing the DC offset. The estimate is generated by jointly minimizing a Constant Modulus (CM) cost function over equalizer parameters and a DC offset estimate. An arbitrary DC offset the CM cost function admits local spurious minima in terms of the equalizer function. If the DC offset at the receiver is equal to the DC offset inserted at the transmitter, which is equivalent to neglecting the ISI caused by the channel, then only half of the CM equalizer minima remain unchanged.

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.