Abstract:
In one or more embodiments, a method of processing a soft value sequence according to an iterative soft-input-soft-output (SISO) algorithm comprises carrying out sliding-window processing of the soft value sequence in a first iteration using first window placements and in a second iteration using second window placements, and varying the window placements between the first and second iterations. In at least one embodiment a communication receiver circuit is configured to carry out iterative SISO processing, wherein it processes a soft value sequence using sliding windows, and wherein it varies window placements between one or more iterations. The communication receiver circuit comprises, for example, all or part of a turbo decoding circuit or other type of iterative block decoding circuit, an equalization and decoding circuit, a soft demodulation and decoding circuit, a multi-user detection and decoding circuit, or a multiple-input-multiple-output detection and decoding circuit.
Abstract:
Channel response and impairment correlation estimates are iteratively determined. According to one embodiment of performing channel estimation for use in received signal processing, a channel response estimate is calculated based on an initial impairment correlations estimate and a measured channel response derived from a received signal. A revised impairment correlations estimate is calculated using a parametric approach based on the channel response estimate and the channel response estimate is recalculated based on the revised impairment correlations estimate. According to one embodiment of a wireless communication device, the device comprises a parameter estimation unit configured to iteratively calculate a medium channel response estimate based on a parametric impairment correlations estimate and a measured net channel response derived from a received signal. The wireless communication device also comprises circuitry configured to control how many times the parameter estimation unit calculates the medium channel response estimate.
Abstract:
According to a method taught herein, a multi-branch communication receiver operates in a first mode wherein it despreads individual receiver branch signals with respect to all channelization codes of interest, if sufficient despreader resources are available for such operation. If the receiver estimates that sufficient despreader resources are not available, it operates in a second mode wherein it despreads one or more of the channelization codes from a branch combination signal formed from two or more of the receiver branch signals. The receiver may calculate optimal branch combining weights using an algorithm that maximizes a signal quality of the branch combination signal. A Generalized RAKE (GRAKE) receiver embodiment applies GRAKE detection to the individual receiver branch signals with respect to all channelization codes of interest if sufficient despreader resources are available and, if not, applies GRAKE detection to the branch combination signal for one or more such codes.
Abstract:
A method and corresponding circuit for determining a final result for a desired series of multiply-and-accumulate (MAC) operations are based on counting the occurrence of products in the desired series of MAC operations, multiplying the counts by their corresponding products to obtain partial sums, and adding the partial sums to obtain the final result. MAC processing as taught herein can be applied to a wide range of applications, such as received signal processing in wireless communication for computationally efficient (and high-rate) generation of interference correlation estimates and/or equalization filter values for a received communication signal.
Abstract:
A linear turbo-equalizer for use in a CDMA receiver equalizes a despread received signal (rather than the spread received signal) to suppress self-interference resulting from coupling between transmitted symbols. In an example implementation, a linear equalizer based on a generalized-Rake (G-Rake) receiver design uses decoder feedback in forming Rake combining weights as well as in forming a self-interference estimate removed from the equalizer signal provided to the decoder. Preferably, turbo de-coding is also performed. In that case, each turbo-decoder component preferably executes one pass before feeding back information to the equalizer. This ensures that the turbo-decoder does not prematurely lock onto an incorrect code word before feeding back extrinsic information to the equalizer.
Abstract:
A wireless communication receiver improves signal impairment correlation estimation in MIMO/MISO systems by considering different transmit power allocations and different transmit antenna power distributions in its impairment correlation calculations. The receiver may be implemented in according to a variety of architectures, including, but not limited to, Successive Interference Cancellation (SIC) Generalized RAKE (G-RAKE), Joint Detection (JD) G-RAKE, and Minimum Mean Squared Error (MMSE) G-RAKE. Regardless of the particular receiver architecture adopted, the improved impairment correlations may be used to calculate improved (RAKE) signal combining weights and/or improve channel quality estimates for reporting by receivers operating in Wideband CDMA (W-CDMA) systems transmitting HSDPA channels via MIMO or MISO transmitters. A transmitter may be configured to facilitate impairment correlation determinations by wireless communication receivers operating in MIMO/MISO environments, by signaling one or more values, e.g., data-to-pilot signal transmit power ratios and/or transmit antenna power distributions for the data and pilot signals.
Abstract:
Coded digital data symbols sent through a transmission channel of a communications network are received in a receiver. Estimates (y) represented by a first number (a+b) of bits are calculated, and modified estimates (y') represented by a second number (c) of bits provided therefrom, the second number being lower than the first number. An amplitude value is calculated for each estimate (y), and an averaged amplitude value calculated for a number of amplitude values. A scaling factor (s) is calculated from the averaged amplitude value, and scaled estimates generated in dependence of the scaling factor. The scaling factor is used for a number of scaled estimates corresponding to the number of amplitude values for which the averaged amplitude value was calculated. Thus a better scaling factor is provided for most channel cases, which can still be calculated with the limited computational resources of a terminal for such networks.
Abstract:
A receiver includes a receiver circuit that decodes multiple signals of interest contained in a composite receive signal. The receiver comprises at least one Generalized RAKE combining circuit and a joint demodulation circuit and generates a detected signal at an output. The joint demodulation circuit contains a reduced search soft value generator circuit and generates soft bit values that represent coded bits received from the transmitter.
Abstract:
A receiver circuit provides improved noise estimation processing by at least partially removing receiver frequency error bias. An initial noise estimate is compensated using an error term based on the observed receiver frequency error, and the resulting compensated noise estimate can be used to improve other signal processing in the receiver. For example, the receiver may use compensated noise estimates to generate signal quality estimates, e.g., Signal-to-Interference (SIR) estimates, having improved accuracy. Additionally, or alternatively, the receiver may use the compensated noise estimates to generate RAKE combining weights having improved noise suppression characteristics. In an exemplary embodiment, the initial noise estimate is a noise correlation matrix generated from a received reference signal, e.g., pilot symbols, and the error term is an error matrix directly generated using the observed receiver frequency error and channel estimates taken from the reference signal.
Abstract:
A processing circuit and method generate signal quality estimates based on scaling measured inter-symbol interference (ISI) in a received signal according to a cancellation metric corresponding to ISI cancellation performance of the receiver. By accounting for ISI cancellation performance of the receiver based on a simple scaling metric, accurate received signal quality measurements are obtained in a manner that accounts for un-cancelled ISI in the received signal without requiring use of potentially complex multipath combining weight calculations in the signal quality calculation. Signal quality estimation results may be used for sending corresponding Channel Quality Indicators, communication link transmit power control commands, etc. In some embodiments, the cancellation metric is maintained as a dynamic value based on measured ISI cancellation performance, while in other embodiments the cancellation metric comprises a pre-configured value stored in memory, for example.