Abstract: A system, device, and method for time-domain equalizer training injects noise into the received training signal prior to determining time-domain equalizer coefficients and determines the time-domain equalizer coefficients in the presence of the injected noise.

Abstract: A system, device, and method for determining the sampling time for a time-domain equalizer divides an over-sampled sequence into a plurality of individual Nyquist rate sequences, determines time-domain equalizer coefficients using one of the individual Nyquist rate sequences, and measures the performance for all individual Nyquist rate sequences using the time-domain equalizer coefficients. The Nyquist rate sequence having the best performance is selected, and the sampling time (delay) is configured so that the selected Nyquist rate sequence is provided to the time-domain equalizer.

Abstract: A system, device, and method for time-domain equalizer (TEQ) training determines the TEQ order and TEQ coefficients by applying the multichannel Levinson algorithm for auto-regressive moving average (ARMA) modeling of the channel impulse response. Specifically, the TEQ is trained based upon a received training signal. The received training signal and knowledge of the transmitted training signal are used to derive an autocorrelation matrix that is used in formulating the multichannel ARMA model. The parameters of the multichannel ARMA model are estimated via a recursive procedure using the multichannel Levinson algorithm. Starting from a sufficiently high-order model with a fixed pole-zero difference, the TEQ coefficients corresponding to a low-order model are derived from those of a high-order model.

Abstract: The invention provides simple and reliable detection of .pi./4 shifted DQPSK modulated digital signals in a single-subscriber-unit, a multiple-subscriber unit (MSU) or a base transceiver station (BTS) of a fixed-wireless system, and is also directly applicable to other digital cellular or personal communication systems which utilizes a binary or M-ary PAM, FSK or PSK digital modulation scheme with differential or coherent encoding and time- and/or frequency-multiplexing. It offers great simplicity while providing soft-decision information for the later stage decoding of information bits encoded with an error correcting code. For each received sample z.sub.k+L and its estimated one z.sub.k+L, a Euclidean distance function is calculated. This Euclidean distance u(z.sub.k+L .vertline.v.sub.k+L, . . . , v.sub.k) is then added to the function derived from the previous iteration g(v.sub.k+L-1, . . . , v.sub.k), to yield a new Euclidean distance function f(v.sub.k+L, . . . , v.sub.k).

Abstract: 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.

Abstract: 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.

Abstract: 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.