US 6,282,559 B1Granted

Method and electronic circuit for signal processing, in particular for the computation of probability distributions

AssigneeAnadec GmbH

InventorsMarkus Helfenstein, Hans-Andrea Loeliger, Felix Lustenberger, Felix Tarköy

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Abstract

A circuit module for data processing comprises several first inputs (Ix,i), several second inputs (Iy,i) and several outputs (Ii,j). First transistors (Ti,j) combine the first and second inputs. Each of the first transistors (Ti,j) is connected at its emitter or source with a first input and at its base or gate with a second input. Each second input is further connected to the base or gate and the emittor or source of a second transistor (Ti). The currents of the outputs correspond to the product of the currents through the individual inputs. By combining the outputs sum products can be calculated, especially for computing discrete probability distributions. The combination of several circuit modules allows to solve complex signal processing tasks.

Claims12

Dependent claims

A method for the calculation of at least one discrete probability distribution [p(z1), . . . , p(zk)], k≧2, from discrete probability distributions [p(x1), . . . , p(xm)], m≧2, and [p(y1) , . . . , p(yn)], n≧2, according to the formula p(z)=γΣxΣyp(x)p(y)f(x, y, z) with at least one given {0,1}-valued function f(x, y, z) and with a scaling factor γ by means of an electronic circuit, wherein said electronic circuit comprises a first and a second circuit section, the first circuit section comprising

m current inputs (Ix,1. . . Ix,m) for [p(x1), . . . , p(xm)] and n current inputs (Iy,1 . . . Iy,n) for [p(y1), . . . , p(yn)],
m·n transistors Ti,j, i=1 . . . m, j=1 . . . n, wherein an emitter or a source of transistor Ti,j is connected to the current input for p(xi) and a base or a gate of transistor Ti,j is connected to the current input for p(yj),
additional n transistors Tj, j=1 . . . n, wherein a base or gate and a collector or drain of transistor Ti is connected to the current input for p(yj) and an emitter or source is connected to a reference voltage;
wherein the second circuit section comprises k current outputs for [p(z1), . . . , p(zk)];
said method comprising the steps of
summing the collector or drain currents of said transistors Ti,j corresponding to the required product terms p(xi)p(yj), and
copying those currents that correspond to multiply used product terms.

4 more elements

A method for the calculation of at least one discrete probability distribution [p(z1), . . . , p(zk)], k≧2, from discrete probability distributions [p(x1), . . . , p(xm)], m≧2, and [p(y1), . . . , p(yn)], n≧2, according to the formula p(z)=γΣxΣyp(x)p(y)f(x, y, z) with at least one given {0, 1}-valued function f(x, y, z) and with a scaling factor γ by means of an electronic circuit, wherein said electronic circuit comprises a first and a second circuit section, the first circuit section comprising

m current inputs (Ix,1 . . . Ix,m) for [p(x1), . . . , p(xm)] and n current inputs (Iy,1 . . . Iy,n) for [p(y1), . . . , p(yn)],
m·n transistors Ti,j, i=1 . . . m, j=1 . . . n, wherein an emitter or a source of transistor Ti,j is connected to the current input for p(xi) and a base or a gate of transistor Ti,j is connected to the current input for p(yj),
additional n transistors Tj, j=1 . . . n, wherein a base or gate and a collector or drain of transistor Ti is connected to the current input for p(yj) and an emitter or source is connected to a reference voltage;
wherein the second circuit section comprises k current outputs for [p(z1), . . . , p(zk)];
said method comprising the steps of
summing the collector or drain currents of said transistors Ti,j corresponding to the required product terms p(xi)p(yj),
copying those currents that correspond to multiply used product terms and
using said method for calculating a problem selected from the group of separating interfering digital signals;
separating users in multi-access communication systems;
equalization of digital data;
combined equalization, user separation and decoding of coded, digital signals;
calculating at least some metric vectors in a sum product algorithm;
decoding an error correcting code;
decoding encoded data;
source coding;
carrying out a forward-backward algorithm in a hidden Markov model;
and carrying out probability propagation in a Bayesian network.

14 more elements

Dependencies are inferred from the claim text.