Novatel Communications Ltd.

Andrew M Sendyk

Yongbing Wan

Qingli Liu

Jin Yang

Xiangyang Chen

Lise Vanterpool

A maximum likelihood decoding system includes a branch metric processor which calculates only one of four branch metrics associated with branches leading to two consecutive states S.sub.j and S.sub.+1, where j is even. The system determines the remaining three metrics by producing a second branch metric by manipulating the first branch metric using simple binary operations and assigns the first and second metrics to the second and first branches, respectively, leading to the odd state. The system next retrieves associated path metrics from a path metric memory which stores the information in locations accessed by addresses related to identifiers associated with the branch initial states. After the system selects a surviving path for each end state, it stores in a path memory location associated with the end state information identifying the previous state on the surviving path. Thereafter, during trace back, the system uses the identifier of a known end state to address the path memory and retrieve the information identifying a previous state on the path. The system assigns data values to the branches of the most likely path as soon as it identifies the states on the path, since all branches leading to an even state are associated with a 'ZERO' data bit and all branches leading to an odd state are associated with a 'ONE' data bit.

Maximum likelihood convolutional decoder

A noise-suppression circuit (10) divides the signal from a microphone (12) into a plurality of frequency sub-bands by means of a noise-band divider (18) and a subtraction circuit (36). By means of gain circuits (32) and (34), it applies separate gains to the separate bands and then recombines them in a signal combiner (38) to generate an output signal in which the noise has been suppressed. Separate gains are applied only to the lower subbands in the voice spectrum. Accordingly, the noise-band divider (18) is required to compute spectral components for only those bands. By employing a sliding-discrete-Fourier-transform method, the noise-band divider (18) computes the spectral components on a sample-by-sample basis, and circuitry (50, 52) for determining the individual gains can therefore update them on a sample-by-sample basis, too.

Noise-reduction system

An improved decision feedback equalizer for use with digital communications. The equalizer includes an error detector, a process controller, a parameter selector and a data buffer for temporarily storing a digital data signal received from a communication channel. The error detector determines whether the equalizer is accurately tracking changes in the communication channel's characteristics or is lost. When the error detector determines that the equalizer is lost, the process controller responsively generates control signals for initiating an optimal retraining/recovery method for the prevailing conditions. In some retraining/recovery methods, data is temporarily stored in the buffer. The stored data is later retrieved and processed once the equalizer is retrained. Retraining is performed using a retraining signal received via the communication channel or, if available, a portion of the data signal which is suitable for retraining, thereby permitting more rapid resumption of data reception. In addition, the parameter selector, in response to a control signal from the process controller and a signal which is indicative of the rate of change of the communication channel's characteristics, dynamically adjusts the convergence rate of the equalizer to provide to an optimum value for performing retraining or tracking changes in the channel's characteristics.

Decision feedback equalizer

The channel-impulse-response estimator (44') in a maximum-likelihood-sequence-estimation receiver produces channel-model parameters for a model that produces outputs not only for symbol times but also for intermediate times between the symbol times. A symbol-sequence-derivation circuit (42') determines the most-likely sequence on the basis of metrics computed from the differences between the received signal and the responses of the model to candidate sequences not only at the symbol times but also at the intermediate times. An interpolator (100) receives the symbol decisions from the symbol-sequence-derivation circuit (42') and generates intermediate values from them by simulating the Nyquist filter formed by the concatenation of pulse-shaping filters (22 and 24) in the transmitter and matched filters (36 and 37) in the receiver. The channel-impulse-response estimator (44') uses these values together with the derived-symbol values as inputs to its updating process to maintain the model.

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Calgary, Canada

Andrew M Sendyk