Novatel Communications Ltd.

Qingli Liu

Andrew M Sendyk

Yongbing Wan

Xiangyang Chen

Lise Vanterpool

A diversity receiver employing maximum-likelihood-sequence estimation employs a separate channel estimator (44-1, . . . , 44-L) for each of a plurality of diversity channels. Each channel estimator (44-1, . . . , 44-L) produces channel-model parameters ([f.sub.1 ], . . . , [f.sub.L ]) that characterize their respective channels. A weighting-and-accumulation circuit (56) computes the responses of the thus-represented models to candidate symbol sequences, and metrics indicating the likelihoods that respective candidate sequences were the sequences actually sent are determined by comparing the model output with the received signal in a comparison circuit (58) and squaring the magnitudes of the results in a squaring circuit (60). The receiver then employs a Viterbi algorithm (62) to determine which sequence is the one most likely to have been sent. In one version, it does this on the basis of the metrics calculated for the channel whose signal is strongest, while it determines the metrics by combining partial metrics from all of the channels in another version.

Adaptive-sequence-estimation apparatus employing diversity

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

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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Qingli Liu