Abstract: Certain aspects provide a method for determining decoding order and reconstruction weights for decoded streams to be cancelled in a MIMO system with successive interference cancellation, based on estimates of the channel characteristics, the received composite signal and parameters of the system.

Abstract: A method for using a joint decoding engine in a wireless device is disclosed. A first symbol stream and a second symbol stream in a received multiple input multiple output (MIMO) signal is determined. A scaled channel estimate for a wireless transmission channel and a scaled noise covariance of the MIMO signal are also determined. The scaled channel estimate and the first symbol stream are whitened. Max log maximum a posteriori (MLM) processing is performed on the whitened first symbol stream to produce a first data stream. The first data stream may be de-rate matched and decoded to produce a decoded first data stream.

Abstract: Methods and apparatuses for data aided channel quality estimation using both pilot and data information are disclosed herein. In one exemplary aspect, a method for estimating channel quality in a wireless communication system is disclosed. The method comprises estimating a pilot noise variance based on a pilot signal received from a base station on a downlink and estimating a data noise variance based on a data signal received from the base station on the downlink. The method also comprises combining the pilot noise variance and the data noise variance to obtain a combined noise variance, and estimating the channel quality based on the combined noise variance.

Abstract: Techniques for determining channel quality indicators (CQIs) for a non-linear detector at a user equipment (UE) are described. In one design, the UE may determine at least one parameter (e.g., at least one threshold) based on at least one constellation constrained capacity function. Each threshold may correspond to a maximum number of information bits for one stream when a particular modulation order is used for another stream. The UE may determine CQIs for multiple streams for the non-linear detector based on the at least one parameter. The UE may also select a precoding matrix (e.g., jointly with the CQIs) based on the at least one parameter. The UE may report the selected precoding matrix and the CQIs for the multiple streams. The UE may thereafter receive a transmission of the multiple streams, which may be transmitted based on the selected precoding matrix and the CQIs.

Abstract: Disclosed are methods and apparatus for predicting a channel quality indicator in a communication system, and in particular a delayed receiver. A disclosed method for determining the predictive channel quality indicator for a delayed receiver includes determining at least one channel quality indicator from a non-delayed receiver. The method also includes determining another channel quality indicator from the delayed receiver, and then calculating the predictive channel quality indicator for the delayed receiver through a function of the channel quality indicators from the non-delayed receiver and the channel quality indicator from the delayed receiver. Corresponding apparatus are also disclosed.

Abstract: Techniques for filtering noisy estimates to reduce estimation errors are described. A sequence of input values (e.g., for an initial channel impulse response estimate (CIRE)) is filtered with an infinite impulse response (IIR) filter having at least one coefficient to obtain a sequence of output values (e.g., for a filtered CIRE). The coefficient(s) are updated based on the sequence of input values with an adaptive filter, a bank of prediction filters, or a normalized variation technique. To update the coefficient(s) with the adaptive filter, a sequence of predicted values is derived based on the sequence of input values. Prediction errors between the sequence of predicted values and the sequence of input values are determined and filtered to obtain filtered prediction errors. The coefficient(s) of the IIR filter are then updated based on the prediction errors and the filtered prediction errors.

Abstract: Certain aspects provide a method for determining decoding order and reconstruction weights for decoded streams to be cancelled in a MIMO system with successive interference cancellation, based on estimates of the channel characteristics, the received composite signal and parameters of the system.

Abstract: Certain aspects provide a method for determining decoding order and reconstruction weights for decoded streams to be cancelled in a MIMO system with successive interference cancellation, based on estimates of the channel characteristics, the received composite signal and parameters of the system.

Abstract: Methods and apparatuses for data aided channel quality estimation using both pilot and data information are disclosed herein. In one exemplary aspect, a method for estimating channel quality in a wireless communication system is disclosed. The method comprises estimating a pilot noise variance based on a pilot signal received from a base station on a downlink and estimating a data noise variance based on a data signal received from the base station on the downlink. The method also comprises combining the pilot noise variance and the data noise variance to obtain a combined noise variance, and estimating the channel quality based on the combined noise variance.

Abstract: Techniques for determining channel quality indicators (CQIs) for a non-linear detector at a user equipment (UE) are described. In one design, the UE may determine at least one parameter (e.g., at least one threshold) based on at least one constellation constrained capacity function. Each threshold may correspond to a maximum number of information bits for one stream when a particular modulation order is used for another stream. The UE may determine CQIs for multiple streams for the non-linear detector based on the at least one parameter. The UE may also select a precoding matrix (e.g., jointly with the CQIs) based on the at least one parameter. The UE may report the selected precoding matrix and the CQIs for the multiple streams. The UE may thereafter receive a transmission of the multiple streams, which may be transmitted based on the selected precoding matrix and the CQIs.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: A method for using a joint decoding engine in a wireless device is disclosed. A first symbol stream and a second symbol stream in a received multiple input multiple output (MIMO) signal is determined. A scaled channel estimate for a wireless transmission channel and a scaled noise covariance of the MIMO signal are also determined. The scaled channel estimate and the first symbol stream are whitened. Max log maximum a posteriori (MLM) processing is performed on the whitened first symbol stream to produce a first data stream. The first data stream may be de-rate matched and decoded to produce a decoded first data stream.

Abstract: Techniques for filtering noisy estimates to reduce estimation errors are described. A sequence of input values (e.g., for an initial channel impulse response estimate (CIRE)) is filtered with an infinite impulse response (IIR) filter having at least one coefficient to obtain a sequence of output values (e.g., for a filtered CIRE). The coefficient(s) are updated based on the sequence of input values with an adaptive filter, a bank of prediction filters, or a normalized variation technique. To update the coefficient(s) with the adaptive filter, a sequence of predicted values is derived based on the sequence of input values. Prediction errors between the sequence of predicted values and the sequence of input values are determined and filtered to obtain filtered prediction errors. The coefficient(s) of the IIR filter are then updated based on the prediction errors and the filtered prediction errors.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: Certain aspects provide a method for determining decoding order and reconstruction weights for decoded streams to be cancelled in a MIMO system with successive interference cancellation, based on estimates of the channel characteristics, the received composite signal and parameters of the system.

Abstract: Certain aspects provide a method for determining decoding order and reconstruction weights for decoded streams to be cancelled in a MIMO system with successive interference cancellation, based on estimates of the channel characteristics, the received composite signal and parameters of the system.

Abstract: In general, this disclosure describes techniques for demodulating wireless signals. In particular, the techniques of this disclosure dynamically select between two or more demodulators based on channel quality information measured over a plurality of measurement periods. For example, a wireless communication device (WCD) may switch from a first demodulator to a second demodulator when the channel quality information associated with the demodulators indicates a better channel quality for the second demodulator than the first demodulator for a consecutive number of measurement periods. As another example, the WCD may compute, for each measurement period, the difference between the channel quality information associated with each of the demodulators, sum the differences, and switch demodulators when the total accumulation of the differences exceeds a threshold.

Abstract: Techniques for filtering noisy estimates to reduce estimation errors are described. A sequence of input values (e.g., for an initial channel impulse response estimate (CIRE)) is filtered with an infinite impulse response (IIR) filter having at least one coefficient to obtain a sequence of output values (e.g., for a filtered CIRE). The coefficient(s) are updated based on the sequence of input values with an adaptive filter, a bank of prediction filters, or a normalized variation technique. To update the coefficient(s) with the adaptive filter, a sequence of predicted values is derived based on the sequence of input values. Prediction errors between the sequence of predicted values and the sequence of input values are determined and filtered to obtain filtered prediction errors. The coefficient(s) of the IIR filter are then updated based on the prediction errors and the filtered prediction errors.

Abstract: Techniques for filtering noisy estimates to reduce estimation errors are described. A sequence of input values (e.g., for an initial channel impulse response estimate (CIRE)) is filtered with an infinite impulse response (IIR) filter having at least one coefficient to obtain a sequence of output values (e.g., for a filtered CIRE). The coefficient(s) are updated based on the sequence of input values with an adaptive filter, a bank of prediction filters, or a normalized variation technique. To update the coefficient(s) with the adaptive filter, a sequence of predicted values is derived based on the sequence of input values. Prediction errors between the sequence of predicted values and the sequence of input values are determined and filtered to obtain filtered prediction errors. The coefficient(s) of the IIR filter are then updated based on the prediction errors and the filtered prediction errors.