Near-maximum-likelihood detection processes for distorted digital signals

Clark, A.P.; Harvey, J.D.; Driscoll, Jack

doi:10.1049/ree.1978.0043

Cited by 15 publications

(13 citation statements)

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“…A useful reduction in the complexity of a near-maximum-likelihood detection process can be achieved by using instead of the cost \U\ 2 , which is the square of the unitary distance between R and P, associated with a vector X (eqns. [5][6][7][8], the cost wuw = \\u x \\+ where Ik 1 1 = \u aJ \ +\ub>i\ (9) (10) Fig. 1 Unit circles in the complex number plane, for different distance measures for 1 = 1,2, .…”

Section: (8)mentioning

confidence: 99%

“…\\UII will be referred to here as the 'magnitude-sum' distance between the received vector R and the corresponding vector P. It involves no operations of squaring or multiplication. Computer simulation tests on various channels have suggested (but not shown conclusively) that no serious loss in tolerance to noise is likely to result from using the alternative distance measure for the cost associated with X [9,11]. Other distance measures have in the past been considered for similar or related applications [12][13][14][15][16][17].…”

Section: (8)mentioning

confidence: 99%

“…It has been shown that the maximum-likelihood detection of a distorted digital signal can, under certain conditions, be implemented without excessive equipment complexity by means of the Viterbi algorithm [1][2][3][4][5][6][7]. This has been followed by the development of various near-maximum-likelihood detection processes which are suitable for practical implementation over a very much wider range of conditions than is the Viterbi algorithm [8][9][10]. In all these processes the detector carries out numerous operations of squaring or multiplication to determine the mean-square differences between possible received data signals, in the absence of noise, and the signal actually received.…”

Section: Introductionmentioning

confidence: 99%

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Distance measures for near-maximum-likelihood detection processes

Clark

1981

IEE Proc. E Comput. Digit. Tech. UK

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In maximum-or near-maximum-likelihood detection processes of the type frequently considered for use in applications where the digital data signal is received in the presence of both additive noise and severe intersymbol interference, the detector computes, for each of a number of possible sequences of received data symbols, the Euclidean or unitary distance between the corresponding received sequence, for the given signal distortion but in the absence of noise, and the sequence actually received. Such computations involve numerous operations of squaring or multiplication. The paper studies a general class of piecewise linear distance measures that involve no operations of squaring or multiplication, other than multiplication by the integer 2, leading to a useful simplification in the implementation of the detection process. The departure of each distance measure from the ideal is analysed theoretically for the simplest case of just one complex-valued received sample, and the results obtained are then extended to the general case of a message involving several received samples. Finally, results are presented of computer simulation tests assessing the effect of each distance measure on the tolerance of the detector to additive white Gaussian noise. List of principal symbolsreal part of complex number imaginary part of complex number magnitude of complex number according to the specified distance measure magnitude of r,-p f according to the specified distance measure A, B, C, D or E magnitude of r { -p t according to the specified distance measure 1, 2, 3 or 4 distance between the vectors R and P according to the specified distance measure number of components in Y / 17 number of levels of data-symbol s,-number of components in R possible value of q t -component row vector whose/th component is p / value of resultant (total) data-signal component in r t /^-component row vector whose /th component is g,-a possible (but erroneous) vector Q /th received samplê -component row vector whose /th component is r { /th data-symbol (n -^-component row vector whose /th component is s t the possible (but erroneous) vector S corresponding to the vector Q e possible value of w,--component row vector whose /th component is w, -Gaussian noise component in r,-n-component row vector whose /th component is w,-possible value of 5, -(n -^-component row vector whose /th component isx,-(/ + l)th component of Y sampled impulse response of channel variance of w,-

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Section: (8)mentioning

confidence: 99%

Section: (8)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distance measures for near-maximum-likelihood detection processes

Clark

1981

IEE Proc. E Comput. Digit. Tech. UK

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“…If now the data-symbol rate is increased to the Nyquist rate (a condition assumed in this report), the noise-whitened matched filter degenerates into the combination of the ideal lowpass filter, sampler and linear feedforward transversal filter just described for the nonlinear equalizer 22 • Computer-simulation tests over various linear baseband channels that introduce different levels of amplitude distortion, ranging from low to very severe, but with little or no phase distortion, have shown that the Viterbi-algorithm detector can be replaced, with no significant loss in tolerance to additive white Gaussian noise, by very much simpler detection processes that are a development of the Viterbi algOrithm 27 • No adaptive linear filter is used here ahead of the detector but an important condition that must be satisfied here is that the magnitude of the first component of the sampled impulse-response of the channel is not too small. When the first component is very small, a substantially modified detection process must be used 28 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive adjustment of receiver for distorted digital signals

Clark

Hau

1984

IEE Proc. F Commun. Radar Signal Process. UK

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“…When the received signal is sampled at the Nyquist rate a noise-whitening matched filter is no longer required. It is now sufficient to use in its place, a low pass filter with a rectangular (or nearly rectangular) transfer function and a cut-off frequency equal to the half of the sampling rate followed by the sampler and then an adaptive linear feedforward transversal filter similar to that employed in the optimum (conventional) nonlinear equalizer (see the previous section) [26,41,42). …”

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confidence: 99%