Ati Technologies Inc.

Other

Ati Research, Inc.

Omereen Wireless, LLC

Iberium Communications Inc.

Nxtwave Communications, Inc.

Dotcast, Inc.

Netwave Communications

Sarnoff Digital Communications

Thomas J Endres

Christopher H Strolle

Troy A Schaffer

Samir N Hulyalkar

Anand M Shah

Wonzoo Chung

Christopher David Long

Raúl Alejandro Casas

Stephen L Biracree

Slobodan Simovich

Stephen Biracree

Azzedine Touzni

Raul Alejandro Casas

Rory Fagan

Douglas Whitcomb

Azzédine Touzni

W Leo Hoarty

Haosong Fu

The current application is directed to maintaining the correct number of symbols in a protocol frame in a digital communications receiver, to prevent catastrophic failure due to dynamic multipath or cycle slips. Timing recovery and framing are coherent, facilitated by placing channel estimation directly into a larger timing recovery loop.

Coherent synchronization and framing in a digital television receiver

Certain embodiments of the present invention encapsulate a variety of data, for example, FIC data, together with ATSC-M/H Service data, into a single IP stream, to simplify the interface between demodulator and media processor. A single data interface protocol within the receiver can then be used, facilitating integration.

Simplified data interface protocol for digital television receiver

Various aspects and embodiments of the present invention derive statistics of received signal quality and use these statistics to jointly control operation of timing recovery, carrier recovery, automatic gain control, and equalization functions.

Joint, adaptive control of equalization, synchronization, and gain in a digital communications receiver

The present invention is related to joint trellis decoding and equalization using a decision feedback equalizer.

Method and apparatus for joint decoding and equalization

The present invention uses a novel adaptive soft decision device in order to jointly optimize decision device and DFE operation. The soft decision device receives the input and output samples of the slicer and generates a feedback sample by non-linearly combining them with respect to a single decision reference parameter. Moreover, the soft decision device provides novel error terms used to adapt equalizer coefficients in order to jointly optimize decision device and equalizer coefficients.

Joint adaptive optimization of soft decision device and feedback equalizer

Methods and apparatus for remote antenna subsystems that are physically isolated from receiver subsystems are disclosed. The communication between the two subsystems uses remote communication links. The remote communication links are either wireless, in which case they are designed for indoor low power communications, or are wired, using existing infrastructure in the household such as the power lines. Partitioning between the antenna and receiver subsystems is selectable.

Remote antenna and local receiver subsystems for receiving data signals carried over analog television

A decision feedback equalizer (DFE) architecture uses feedback samples that are over-sampled with respect to the symbol rate. On-baud feedback samples are quantized with a slicer, while off-baud samples are linear, IIR samples. Both forward and feedback filters are fractionally-spaced, but adapted only at the baud instances.

Decision feedback equalization with fractionally-spaced feedback data

Compensation methods and apparatus are disclosed for mitigating non-linear distortion of high power amplifiers used in communication transmitters, based on the minimization of the symbol error, in particular, for communication systems transmitting data embedded in existing co-channel signals. To overcome the difficulty of constructing pre-distortion mapping from the symbol error, an adaptive algorithm is disclosed to update compensation parameters for pre-distortion mapping. Another method exploits a test signal interval in the co-channel signal. During a known constant co-channel signal, distortion is estimated as a complex number and used to construct compensation parameters. The latter is further expanded by employing the adaptive algorithm for the non-test signal intervals.

Symbol error based compensation methods for nonlinear amplifier distortion

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. Provided a power constraint on the signal space is satisfied, the DC offset estimate, computed with a joint optimization of the CM cost function, converges to the desired solution, and, thus, the equalizer parameters are guaranteed to converge to the desired solution.

DC offset correction for constant modulus equalization

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.

Carrier phase estimation based on single-axis constant modulus cost criterion and Bussgang criteria

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. In an acquisition mode, equalization is as linear equalization with a constant modulus error term, and possibly other error terms in combination, for adaptation of the filter parameters. In a tracking mode, equalization is as decision feedback equalization with decision-directed error terms for adaptation of the filter parameters. For some equalizer configurations, feedback filtering is applied to real-valued decisions corresponding to complex-valued received data, and includes real-part extraction of the error term employed for recursive update of filtering parameters. Where a training sequence is available to the receiver, initial parameters for forward filtering are estimated by correlation of the received signal with the training sequence.

Linear prediction based initialization of a single-axis blind equalizer for VSB signals

An equalizer for use in a communication receiver includes an infinite impulse response (IIR) feedback filter operated in acquisition and tracking feedback modes on a sample by sample basis to form a hybrid Decision Feedback Equalizer (DFE) architecture. In acquisition mode, soft decision samples from the filtered received signal are input to the IIR filter. In the tracking mode, hard decision samples from a slicer are input to the IIR filter. Acquisition and tracking operating modes are selected in accordance with a set of decision rules on a sample by sample basis based on the quality of the current hard decision. If the current hard decision is low quality, then the soft decision sample (acquisition mode) is used. If the current hard decision is high quality, then the hard decision sample (tracking mode) is used. In such manner, the DFE is operated in a hybrid mode, i.e., using both soft and hard decisions on a sample by sample basis. Furthermore, the decision rules that are responsive to signal reliability to select acquisition and tracking modes are adaptive.

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Pipersville, PA, United States of America

Thomas J Endres