Abstract: 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.

Abstract: Teachings presented herein offer a technique for using a demodulator to improve a demodulation process. For example, a demodulation unit according to an embodiment of the present invention may be a multi-stage demodulator and may include: a demodulator configured to receive a baseband signal and configured to produce modem bit likelihood values based on the received baseband signal; a decoder configured to receive and process the modem bit likelihood values to produce improved modem bit likelihood values; a candidate value generator configured to produce, based on the improved modem bit likelihood values, candidate symbol values for a group of one or more symbols; and a detector configured to receive the baseband signal and the candidate symbol values and configured to produce one of (a) final modem bit estimates and (b) candidate symbol values for a group of symbols.

Abstract: Signal impairment correlations for multiple signals in a received multipath signal are constructed by fitting parametric models associated with each high-data-rate signal in the multipath against measured impairment correlations. The estimated model fitting parameters are applied to form impairment correlation estimates for all signals. The models comprise a separate impairment covariance matrix scaled by a model fitting parameter for each high-data-rate signal and a noise covariance matrix scaled by a noise element model fitting parameter. The model fitting parameters may be estimated by a least-squares formulation and applied to form impairment correlation estimates for all signals of interest. The resulting impairment correlation estimates may be provided to G-RAKE receivers or joint scaling demodulators to demodulate the signals while suppressing interference from the high-data-rate signals.

Abstract: Teachings presented herein offer reduced and stable computational complexity for symbol block detection using multi-stage assistance, and also provide for the generation of soft bit values. A demodulator generates these soft bit values by forming from a set of candidate symbol combinations, for each group of symbols in a symbol block, a subset of candidate symbol combinations for that group. The demodulator selects from the set the most likely combination, at least one combination that has a complementary bit value for a respective bit value in the most likely combination, and as many of the next most likely combinations not already selected as are needed for the size of the subset to conform to a pre-determined size. The demodulator generates soft bit values by limiting the candidate combinations of symbols considered for a symbol block according to the subsets formed for the groups of symbols in that symbol block.

Abstract: Method, communication system, receiver, baseband processor and computer program for recovering data from a received OFDM-signal containing recurrent redundant information where part of the recurrent redundant information is used to maximize the SINR (Signal to Interference plus Noise Ratio) of the received OFDM-signal.

Abstract: A receiver comprises plural receive antennas and electronic circuitry. The plural receive antennas are configured to receive, on plural subcarriers transmitted over a radio interface, a frequency domain signal that comprises contribution from a block of time domain symbols. The electronic circuitry is configured or operable to perform symbol detection of time domain symbols comprising the block by performing a multi-stage joint detection procedure comprising plural stages, and thus serves as a detector (40). For a first stage the block is divided into a first number of sub-blocks each having a sub-block first size. For a second stage the block is divided into a second number of sub-blocks each having a sub-block second size, the sub-block second size being greater than the sub-block first size.

Abstract: Teachings presented herein combine the relative simplicity of equalization with the performance of maximum likelihood (ML) processing. These teachings are applied to the detection of symbols in a stream of symbol blocks. In one or more embodiments, block-based equalization, including feedforward filtering, suppresses inter-block interference and produces detection statistics for the symbols in each symbol block, and joint detection addresses intra-block interference by jointly detecting the most likely combination of symbols within each symbol block, based on the corresponding detection statistics. The joint detection obviates the need to address intra-block interference within the equalization filters, while, at the same time, the block-based equalization produces detection statistics for each symbol block thereby simplifying the joint detection process.

Abstract: Where two or more multi-valued digital data symbols are modulated so that they overlap after passing through a channel, forming a combined signal, a receiver receives the combined signal and forms detection statistics to attempt to recover the symbols. Where forming detection statistics does not completely separate the symbols, each statistic comprises a different mix of the symbols. A receiver determines the symbols which, when mixed in the same way, reproduce or explain the statistics most closely. For example, the receiver hypothesizes all but one of the symbols and subtracts the effect of the hypothesized symbols from the mixed statistics. The remainders are combined and quantized to the nearest value of the remaining symbol. For each hypothesis, the remaining symbol is determined. A metric is then computed for each symbol hypothesis including the so-determined remaining symbol, and the symbol set producing the best metric is chosen as the decoded symbols.

Abstract: In one of its aspects, the technology concerns a method of processing a signal which includes physical data channels which have been channelized using spreading codes. The method comprises (1) despreading unoccupied spreading codes (e.g., codes which are essentially unobscured by traffic data) included in the signal to obtain unoccupied code despread values, (2) using the unoccupied code despread values to form an impairment covariance matrix; and (3) using the impairment covariance matrix along with a channel estimate to form a processing parameter. The processing parameter can be one of combining sets and a signal quality estimate. In another of its aspects, the technology concerns a coherent, linear equalizer apparatus configured to process a signal which includes physical data channels which have been channelized using spreading codes.

Abstract: A device that estimates information from a signal includes a receiver for receiving an input signal and electronic processing circuitry. The electronic processing circuitry generates a matrix associated with the input signal and determines an approximation of an inverse of the matrix based on the adjoint of the matrix and a scaling factor associated with the determinant of the matrix. This approximation avoids possible mathematical difficulties that may be encountered in certain situations when a typical matrix inversion is calculated. The approximated inverse matrix is applied to the input signal to transform the input signal into an output signal. Information associated with the input signal is then determined based on the output signal.

Abstract: According to a method and apparatus taught herein, a decoding circuit and method decode linear block codes based on determining joint probabilities for one or more related subsets of bits in received data blocks. The use of joint probabilities enables faster and more reliable determination of received bits, meaning that, for example, joint probability decoding requires fewer decoding iterations than a comparable decoding process based on single-bit probabilities. As a non-limiting example, the decoding circuit and method taught herein provide advantageous operation with Low Density Parity Check (LDPC) codes, and can be incorporated in a variety of communication systems and devices, such as those associated with wireless communication networks.

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: An SIR estimate of a communication signal in a wireless communication system receiver is calculated based on soft bit values output by a nonlinear detector. The average amplitude of the detector output soft bits is estimated. The average power of the detector output soft bits is estimated, e.g., as a mean-square or variance. An SIR of the communication signal is estimated based on the soft bit amplitude and power estimates. In particular, the SIR is estimated as the ratio of the square of soft bit amplitude to the difference between the mean square soft bit power and the square of soft bit amplitude, or the ratio of the square of soft bit amplitude to the variance. In either case, the SIR estimate may be scaled to obtain the desired units. The communication signal may be a received signal, or a simulated signal generated using channel estimates obtained from, e.g., a pilot channel.

Abstract: A node (e.g., base station, signal processing unit) is described herein that includes a symbol detector and a method which are capable of suppressing interference caused by one user device (which may be in softer handoff mode) to reduce performance degradation to other intra-cell user devices and/or other inter-cell user devices (which may not be in softer handoff mode).

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: An iterative demodulator and decoder uses feedback attenuation to maintain proper balance between the demodulator and decoder. Balance is maintained by attenuating the influence of extrinsic information fed back from the decoder to the demodulator to prevent strong decisions by the decoder from overwhelming the demodulator.

Abstract: Teachings presented herein offer improved symbol block detection by including a decoder unit in a demodulation system. Utilizing a decoder unit in a demodulation system can significantly enhance symbol block detection because the decoder can produce bit likelihood values (soft bit values), and these bit likelihood values can be used to construct a set of candidate symbol values. Advantageously, this set of candidate symbol values is more likely to contain the actually transmitted symbol(s) than if the decoder unit was not used in the demodulation system.

Abstract: Teachings presented herein offer a technique for using a demodulator to improve a demodulation process. For example, a demodulation unit according to an embodiment of the present invention may be a multi-stage demodulator and may include: a demodulator configured to receive a baseband signal and configured to produce modem bit likelihood values based on the received baseband signal; a decoder configured to receive and process the modem bit likelihood values to produce improved modem bit likelihood values; a candidate value generator configured to produce, based on the improved modem bit likelihood values, candidate symbol values for a group of one or more symbols; and a detector configured to receive the baseband signal and the candidate symbol values and configured to produce one of (a) final modem bit estimates and (b) candidate symbol values for a group of symbols.

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: A wireless communication device or system generates transmit power control feedback for a received power control channel by determining a command error rate (CER), or by identifying a target signal quality for the power control channel according to a defined signal-quality-to-CER mapping function. Generally, the power control channel does not include error-coded data to use for CER estimation. However, in one embodiment, the channel does include known reference bits that are evaluated for CER estimation, with the estimated CER used to set the signal quality target for inner loop power control. In other embodiments, a computed reception error probability is used to identify a CER estimate according to a defined probability-to-CER mapping function. By way of non-limiting example, these embodiments may be used to provide power control feedback for power control commands transmitted on a Fractional Dedicated Physical Channel in WCDMA systems.