Abstract: A receiver and method for receiving and processing a sequence of transmitted symbols in a digital communication system utilizing soft pilot symbols. A set of soft pilot symbols are transmitted with higher reliability than the remaining symbols in the sequence by modulating the soft pilot symbols with a lower order modulation such as BPSK or QPSK while modulating the remaining symbols with a higher order modulation such as 16QAM or 64QAM. The receiver knows the modulation type and location (time/frequency/code) of the soft pilot symbols, and demodulates them first. The receiver uses the demodulated soft pilot symbols as known symbols to estimate parameters of the received radio signal. Unlike traditional fixed pilots, the soft pilots still carry some data. Additionally, the soft pilots are particularly helpful in establishing the amplitude reference essential in demodulating the higher order modulation symbols.

Abstract: With a nonparametric G-Rake receiver, combining weights may be determined using a nonparametric mechanism in multiple-input, multiple-output (MIMO) scenarios. In an example embodiment, a method for a receiving device having a nonparametric G-Rake receiver entails calculating an impairment covariance matrix and determining combining weights. More specifically, the impairment covariance matrix is calculated based on a pilot channel using a nonparametric mechanism in a MIMO scenario in which a code-reuse interference term exists. The combining weights are determined for the nonparametric G-Rake receiver responsive to the impairment covariance matrix and by accounting for the code-reuse interference term.

Abstract: In receiving equipment such as a mobile terminal (1) for decoding received signals coded according to a CDMA method are decoded in an intermediate processor (9). In the decoding, coefficients or elements of a vector w are calculated in a unit or module (13) by an iterative method as a solution to a system of linear equations represented by the matrix equation Rw=h, where R is a matrix of known elements and h is a known vector. For terminating the iteration, a value of the signal to interference plus noise ratio SINR for the received signals is calculated in each iteration step and then this calculated value or a quantity derived therefrom is used as a stopping criterion or generally in a stopping algorithm.

Abstract: According to the teachings presented herein, a wireless communication apparatus compensates for timing misalignment in its received signal processing. In at least one embodiment, the apparatus estimates a set of path delays for a received signal and sets processing delays on the estimated path delays. The apparatus jointly hypothesizes combinations of fractional timing offsets for two or more paths, and computes a decision metric for each joint hypothesis that indicates the accuracy of the joint hypothesis. As non-limiting examples, the decision metric may be a signal quality metric, or a distance metric (such as between a measured net channel response and an effective net channel response reconstructed as a function of the combination of fractional timing offsets included in the joint hypothesis). The apparatus evaluates the decision metrics to identify a best estimate of timing misalignment, and correspondingly compensates coherent processing of the received signal.

Abstract: Exemplary combining weight generation is based on estimating received signal impairment correlations using a weighted summation of interference impairment terms, such as an interference correlation matrix associated with a transmitting base station, and a noise impairment term, such as a noise correlation matrix, the impairment terms scaled by fitting parameters. The estimate is updated based on adapting the fitting parameters responsive to measured signal impairment correlations. The interference matrices are calculated from channel estimates and delay information, and knowledge of the receive filter pulse shape. Instantaneous values of the fitting parameters are determined by fitting the impairment correlation terms to impairment correlations measured at successive time instants and the fitting parameters are adapted at each time instant by updating the fitting parameters based on the instantaneous values.

Abstract: A mobile receiver having a multi-mode interference suppression function and a way to estimate its speed utilizes a parametric approach to interference suppression at high speeds, and a nonparametric approach at low speeds. In particular, if the mobile receiver is currently operating in a nonparametric mode and its speed exceeds a first predetermined threshold, the mobile receiver switches to a parametric mode. Conversely, if the mobile receiver is currently in parametric mode and its speed is less than a second predetermined threshold, the mobile receiver switches to nonparametric mode. In one embodiment, the speed may be estimated by a Doppler frequency in the received signal, and the thresholds are Doppler frequencies. In one embodiment, the first and second thresholds are different, creating a hysteresis in the mode switching.

Abstract: A signal-to-interference estimate is generated using unknown data symbols in place of or in addition to pilot symbols. Data received over a data channel (traffic channel or control channel) are collected. The data symbols are then used to compute an observation metric based on deviations of the data symbols from a predetermined set of possible data symbols, wherein one of the data symbols and symbol constellation is normalized. A data channel signal-to-interference ratio is then computed based on the observation metric.

Abstract: Signal quality estimation and demodulation are tailored to the received signal quality. According to one embodiment, a received signal is processed by determining a first set of combining weights based on a first impairment covariance estimate derived assuming a low signal quality environment. A second set of combining weights is determined based on a second impairment covariance estimate derived assuming a high signal quality environment. A metric is determined corresponding to the difference between symbol estimates derived from the second set of combining weights and hard symbol decisions. The received signal is demodulated based on the second set of combining weights if the metric satisfies a threshold indicating high signal quality and otherwise based on the first set of combining weights.

Abstract: As taught herein channelization code power estimates are generated for a number of data channels in a received CDMA signal based on a joint determination process. Joint processing in this context yields improved estimation of data channel code powers and corresponding estimations of noise variance. These improvements arise from exploitation of joint processing of measured data value correlations across two or more data channel codes represented in the received signal. In one or more embodiments, joint determination of data channel code powers comprises forming a correlation matrix as a weighted average of correlations determined for a plurality of data channels. In one or more other embodiments, joint determination of data channel code powers comprises jointly fitting the correlation matrices for a plurality of data channels in a least squares error estimation process.

Abstract: Methods and apparatus are disclosed for calculating a channel response for use in received signal processing. In an exemplary embodiment, a method comprises calculating a channel response correlation matrix based on measured channel responses derived from pilot symbols in a received signal and forming a traffic data correlation matrix based on measurements of traffic symbols in the received signal. The traffic data correlation matrix, the channel response correlation matrix, and the measured channel responses are used in an minimum mean-squared error (MMSE) estimation process to calculate the channel response estimates. In one or more embodiments, the calculated channel response estimates comprise estimates of net channel response corresponding to signal processing delays in a G-RAKE receiver. An exemplary receiver circuit comprises a baseband processor configured to calculate channel response estimates according to one or more of the disclosed methods.

Abstract: Methods and apparatus for estimating code-reuse interference associated with a received multi-stream multiple-input multiple-output (MIMO) signal are disclosed. An estimate for the data-to-pilot power ratio, ?D/P, may be obtained as a by-product of parametric estimation of impairment covariance associated with the received MIMO signal. In an exemplary method, a parametric impairment model is constructed for a received MIMO signal, the parametric model omitting code-reuse interference. Impairment covariance is measured, using, in one or more embodiments, received pilot symbol data. The parametric impairment model is fitted to the measured impairment covariance to obtain one or more scaling parameter values. A per-code energy value for a first data stream is then calculated from the one or more scaling parameter values.

Abstract: A CDMA receiver computes an accurate estimate of the data to pilot power ratio. First, a biased estimate of the data to pilot power ratio is obtained from the data channel. A multiplicative correction factor is then computed from the pilot channel, and applied to the biased data to pilot power ratio estimate.

Abstract: A model-based technique for estimating impairment covariance associated with a MIMO signal is disclosed. In an exemplary method, an impairment model is constructed for a received composite information signal comprising at least a first data stream transmitted from first and second antennas according to a first antenna weighting vector. The impairment model includes first and second model terms corresponding to the first and second antennas, respectively, but in several embodiments does not include a cross-antenna interference term. In another embodiment, an impairment model for a received MIMO signal is constructed by computing an impairment model term for each antenna and an additional term to account for preceding interference in a single-stream MIMO transmission scenario. The impairment terms are grouped so that only two associated scaling terms are unknown; values for the scaling terms are estimated by fitting the model to measured impairment covariance values.

Abstract: The computation of code-specific channel matrices for an Assisted Maximum Likelihood Detection (AMLD) receiver comprises separately computing high rate matrices that change each symbol period, and a low rate matrix that is substantially constant over a plurality of symbol periods. The high and low rate matrices are combined to generate a code-specific channel matrix for each receiver stage. The high rate matrices include scrambling and spreading code information, and the low rate matrices include information on the net channel response and combining weights. The low rate matrices are efficiently computed by a linear convolution in the frequency domain of the net channel response and combining weights (with zero padding to avoid circular convolution), then transforming the convolution to the time domain and extracting matrix elements. Where the combining weights are constant across stages, a common code-specific channel matrix may be computed and used in multiple AMLD receiver stages.

Abstract: Teachings presented herein present a “whitening” channel estimation method and apparatus that produce high-quality net channel estimates for processing a received signal, such as a received CDMA signal. Processing includes forming an initial least squares problem (for medium channel estimates) using known pilot values and corresponding pilot observations for the received signal, transforming the initial least squares problem using a whitening transformation term, and solving the transformed least squares problem to obtain whitened medium channel estimates. The whitening transformation term may be determined, for example, by carrying out a Cholesky factorization of a (traffic) data correlation matrix, which can be obtained from traffic data values for the received signal. Processing further includes converting the whitened medium channel estimates into whitened net channel estimates, which consider the effects of transmit/receive filtering.

Abstract: A method and apparatus for reducing the complexity of waveform correlation computations used by a multicode receiver is described herein. One exemplary multicode receiver includes a despreading unit, channel estimator, and waveform correlation calculator. The despreading unit despreads a received multicode signal to generate despread symbols. The channel estimator estimates channel coefficients associated with the despread symbols. The waveform correlation calculator determines waveform correlations between the transmitted symbols in successive processing windows that span two or more symbol periods and that overlap in time. To reduce the computational complexity associated with computing waveform correlations, the calculator may reuse channel coefficients and/or net channel correlations for multiple symbol periods and/or processing windows.

Abstract: Adaptive reconfiguration of a wireless receiver is enabled based on channel geometry. According to an embodiment, the wireless receiver includes a geometry factor processing module and signal processing modules, e.g. such as but not limited to an SIR estimation module, a power estimation module, a despreading module, a low-pass filter, a combing weight generation module, a coefficient estimation module, a synchronization control channel interference canceller module, etc. The geometry factor processing module determines a geometry factor for the channel over which signals are transmitted to the wireless receiver, the geometry factor being a measure of the ratio of total transmitted power received by the wireless receiver to total interference plus noise power at the wireless receiver. One or more of the receiver signal processing modules are reconfigurable based on the geometry factor.

Abstract: Path delay information generated by a path searcher module of a wireless receiver is used to generate net channel coefficients for use in suppressing interference from a received signal. According to one embodiment, interference is suppressed from a signal transmitted over a communication channel including transmit and receive pulse shaping filters and a radio channel by generating net channel coefficients for the communication channel at processing delays such as G-Rake finger delays or chip equalizer tap delays. Medium channel coefficients are generated for the radio channel at estimated path delays as a function of the net channel coefficients. The net channel coefficients are regenerated at arbitrary delays as a function of the medium channel coefficients and an impairment covariance estimate is generated based at least in part on the regenerated net channel coefficients.

Abstract: Detecting a symbol of interest comprises despreading a received signal to obtain despread values corresponding to the symbol of interest and to one or more interfering symbols, combining the despread values to generate combined values for the symbol of interest and the interfering symbols, computing spreading waveform correlations between the spreading waveform for the symbol of interest and the spreading waveforms for the interfering symbols, computing interference rejection terms representing the interference present in the combined value for the symbol of interest attributable to the interfering symbols based on the spreading waveform correlations, and generating an estimate of the symbol of interest by combining the combined values with the interference rejection terms. The interference rejection terms are computed by scaling the spreading waveform correlations by corresponding signal powers and compensating the estimates for noise.

Abstract: Channel estimation and/or equalization processing is performed in a wireless receiver in two stages. The first stage involves pre-filtering in the frequency domain to compact a grid-based representation of the net channel. The second stage involves implementing reduced-complexity time domain channel estimation and/or equalization. According to one embodiment, a received signal transmitted over a net channel is processed by pre-filtering the received signal in the frequency domain. The frequency domain pre-filtering compacts an N-tap effective grid-based representation of the net channel into a K-tap compacted grid-based representation of the net channel where K<N. The pre-filtered received signal is equalized in the time domain based on the K-tap compacted grid-based representation of the net channel generated by pre-filtering the received signal in the frequency domain.