A method for equalizing a signal comprising modulated symbols comprising a block of N received symbols comprises: demultiplexing the N received symbols by factor L to generate a predetermined number L of sub-blocks of symbols, each comprising a version of the N received symbols sub-sampled by factor L, the independent equalization of each sub-block using an identical equalization algorithm, multiplexing the equalized symbols of each sub-block to obtain a block of N equalized symbols, removing instances of interference linked to paths other than two paths of higher power comprising generating an interference term resulting from the influence, on the equalized symbols, of all paths of the channel having the impulse response of the transmission channel except two paths of higher power, subtracting the interference term from the symbols of the block of N received symbols, and, a second equalization step equal to a second iteration of the first equalization step.

The invention relates to an apparatus for selecting candidates in a K-Best algorithm of a MIMO decoder. The K-Best algorithm uses a layered structure comprising a first layer and subsequent layers. In each subsequent layer 2.sup.L candidates are selected by iteratively carrying out a selection step, wherein in the selection step the apparatus is configured to calculate and select at least two candidates having minimum distance values of a candidate group, and after each iteratively carried out selection step, the selected at least two candidates are sent to a further subsequent layer for iteratively generating a further candidate group of 2.sup.L candidates in the further subsequent layer.

The invention relates to an apparatus for selecting candidates in a K-Best algorithm of a MIMO decoder. The K-Best algorithm uses a layered structure comprising a first layer and subsequent layers. In each subsequent layer 2.sup.L candidates are selected by iteratively carrying out a selection step, wherein in the selection step the apparatus is configured to calculate and select at least two candidates having minimum distance values of a candidate group, and after each iteratively carried out selection step, the selected at least two candidates are sent to a further subsequent layer for iteratively generating a further candidate group of 2.sup.L candidates in the further subsequent layer.

A method, system, and apparatus are provided for computing soft bits in a non-linear MIMO detector which decodes a signal received at a plurality of receive antennas using channel estimate information and a decoding tree to produce output data for a bit estimation value which includes a maximum likelihood solution along with a naturally ordered vector identifying all explored node metrics and node indices, where soft bits are computed for each bit estimation value by determining a set of bit-masks through repetition and indexing operations applied on the explored node indices, masking the naturally ordered vector with the set of bit-masks to generate masked node metrics, determining candidate soft bit values by subtracting metrics of all nodes that form the maximum likelihood solution from the masked node metrics, and determining a final soft bit value by identifying which of the candidate soft bit values has a lowest value.

A method, system, and apparatus are provided for computing soft bits in a non-linear MIMO detector which decodes a signal received at a plurality of receive antennas using channel estimate information and a decoding tree to produce output data for a bit estimation value which includes a maximum likelihood solution along with a naturally ordered vector identifying all explored node metrics and node indices, where soft bits are computed for each bit estimation value by determining a set of bit-masks through repetition and indexing operations applied on the explored node indices, masking the naturally ordered vector with the set of bit-masks to generate masked node metrics, determining candidate soft bit values by subtracting metrics of all nodes that form the maximum likelihood solution from the masked node metrics, and determining a final soft bit value by identifying which of the candidate soft bit values has a lowest value.

Maximum likelihood bit-stream generation and detection techniques are provided using the M-algorithm and Infinite Impulse Response (IIR) filtering. The M-Algorithm is applied to a target input signal X to perform Maximum Likelihood Sequence Estimation on the target input signal X to produce a digital bit stream B, such that after filtering by an IIR filter, the produced digital stream Y produces an error signal satisfying one or more predefined requirements. The predefined requirements comprise, for example, a substantially minimum error. In an exemplary bit detection implementation, the target input signal X comprises an observed analog signal and the produced digital stream Y comprises a digitized output of a receive channel corresponding to a transmitted bit stream. In an exemplary bit stream generation implementation, the target input signal X comprises a desired transmit signal and the produced digital stream Y comprises an estimate of the desired transmit signal.

Maximum likelihood bit-stream generation and detection techniques are provided using the M-algorithm and Infinite Impulse Response (IIR) filtering. The M-Algorithm is applied to a target input signal X to perform Maximum Likelihood Sequence Estimation on the target input signal X to produce a digital bit stream B, such that after filtering by an IIR filter, the produced digital stream Y produces an error signal satisfying one or more predefined requirements. The predefined requirements comprise, for example, a substantially minimum error. In an exemplary bit detection implementation, the target input signal X comprises an observed analog signal and the produced digital stream Y comprises a digitized output of a receive channel corresponding to a transmitted bit stream. In an exemplary bit stream generation implementation, the target input signal X comprises a desired transmit signal and the produced digital stream Y comprises an estimate of the desired transmit signal.

Software Digital Front End (SoftDFE) signal processing techniques are provided. One or more digital front end (DFE) functions are performed on a signal in software by executing one or more specialized instructions on a processor to perform the one or more digital front end (DFE) functions on the signal, wherein the processor has an instruction set comprised of one or more of linear and non-linear instructions. A block of samples comprised of a plurality of data samples is optionally formed and the digital front end (DFE) functions are performed on the block of samples. The specialized instructions can include a vector convolution function, a complex exponential function, an x.sup.k function, a vector compare instruction, a vector max( ) instruction, a vector multiplication instruction, a vector addition instruction, a vector sqrt( ) instruction, a vector 1/x instruction, and a user-defined non-linear instruction.

Quasi-reduced state trellis equalization techniques achieve low-latency inter-symbol interference (ISI) equalization by selecting a subset of accumulated path metrics (APMs) for a leading symbol to propagate over a trellis to candidate states of a trailing symbol. This simplifies the computation of APMs for candidate states of the trailing symbol. Thereafter, APMs for candidate states of the trailing symbol are computed based on the subset of APMs for the leading symbol that were propagated over the trellis. Propagating fewer than all APMs for the leading symbol to the trailing symbol reduces the complexity of APM computation at the trailing symbol.

Methods and apparatus are provided for direct synthesis of RF signals using maximum likelihood sequence estimation. An RF digital RF input signal is synthesized by performing maximum likelihood sequence estimation on the digital RF input signal to produce a digital stream, such that after filtering by a prototype filter the produced digital stream produces a substantially minimum error. The substantially minimum error comprises a difference between a digital output of the prototype filter and the digital RF input signal. The digital stream is substantially equal to the input digital RF signal. The digital stream can be applied to an analog restitution filter, and the output of the analog restitution filter comprises an analog RF signal that approximates the digital RF input signal.