摘要:
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.
摘要:
A symbol detector converts initial symbol estimates of received symbols to soft estimates for decoding. The symbol detector computes spreading waveform correlations between a spreading waveform for a symbol of interest and spreading waveforms for one or more interfering symbols. Interference rejection terms are computed by scaling the spreading waveform correlations by corresponding signal powers and compensating for noise. A soft scaling factor for the symbol of interest is computed from the interference rejection terms. The soft scaling factors are then applied to the initial symbol estimates to generate the soft estimates.
摘要:
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.
摘要:
Methods and apparatus for determining an impairment covariance matrix for use in an interference-suppressing CDMA receiver are disclosed. In several of the disclosed embodiments, precise information regarding signal propagation delays is not needed. An exemplary method includes the selection of a plurality of processing delays for processing a received CDMA signal. Net channel coefficients for the processing delays are estimated and used to calculate an impairment covariance matrix. The impairment covariance matrix is calculated as a function of the estimated net channel coefficients and the processing delays, without estimating a propagation medium channel response for the received signal.
摘要:
Exemplary received signal processing may be based on maintaining a model of received signal impairment correlations, wherein each term of the model is updated periodically or as needed based on measuring impairments for a received signal of interest. An exemplary model comprises an interference impairment term scaled by a first model fitting parameter, and a noise impairment term scaled by a second model fitting parameters. The model terms may be maintained based on current channel estimates and delay information and may be fitted to measured impairment by adapting the model fitting parameters based on the measured impairment. The modeled received signal impairment correlations may be used to compute RAKE combining weights for received signal processing, or to compute Signal-to-Interference (SIR) estimates. Combined or separate models may be used for multiple received signals. As such, the exemplary modeling is extended to soft handoff, multiple antennas, and other diversity situations.
摘要:
According to one embodiment taught herein, a method of determining impairment correlations between a plurality of delays of interest for a received CDMA signal comprises generating kernel functions as samples of a net channel response of the received CDMA signal taken at defined chip sampling phases for delay differences between the plurality of delays of interest. In a parametric Generalized Rake (G-Rake) receiver embodiment, the delays of interest represent the delay positions of the fingers being used to characterized received signal. In a chip equalizer receiver embodiment, the delays of interest represent the delay positions of the equalizer taps. The method continues with determining impairment correlations based on convolving the kernel functions. Corresponding receiver circuits, including an impairment correlation estimation circuit configured for parametric G-Rake operation, may be implemented in a variety of communication devices and systems, such as in wireless communication network base stations and mobile stations.
摘要:
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.
摘要:
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. This provides a robust interference model that avoids numerical problems associated with conventional joint detection.
摘要:
According to the teachings presented herein, “spreading code” knowledge is used in forming amplitude references for QAM demodulation in a DS-CDMA receiver. Here, “spreading code” broadly refers to spreading/channelization codes, scrambling codes, or the product of such codes. Further, these teachings apply to any linear DS-CDMA demodulator, such as Rake, Generalized Rake (G-Rake), or chip equalizer, and to nonlinear demodulators that employ linear filtering, such as decision feedback equalizers (DFEs). Advantageously, the determination of symbol-specific amplitude references relies on shared correlation estimates and/or shared combining weights that are common to two or more symbols of interest, thereby significantly reducing processing requirements as compared to the use of symbol-specific impairment correlation estimates.
摘要:
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. This provides a robust interference model that avoids numerical problems associated with conventional joint detection.