Apparatuses and methodologies for decision feedback equalization using particle swarm optimization

An apparatus for channel equalization in a communication system, the apparatus comprising:

• processing circuitry configured to
• initialize filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space,
• update the filter coefficients, wherein in response to determining that the filter coefficients are stagnant, the filter coefficients are updated using a least mean square recursion, and wherein in response to determining that the filter coefficients are not stagnant, the filter coefficients are updated using a particle swarm optimization procedure,
• repeat the updating step until a predetermined stopping criteria is met, and
• filter a received signal based on the filter coefficients,
• wherein the updating of the filter coefficients using the least mean square recursion includes applying
• pi,k+1=pi,k+μAkTei(k)
• where μ is a step size, e is an error value,
• A
• k
• =
• [
• Y
• k
• H
• D
• k
• H
• ]
• ,
• where Y is an input matrix, D is a decision matrix, pi,k is one of the filter coefficients, i is an index, and k is a time instant.

A non-transitory computer readable medium storing computer-readable instructions therein which when executed by a computer cause the computer to perform a method for channel equalization in a communication system, the method comprising:

• initializing filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space;
• updating the filter coefficients, wherein in response to determining that the filter coefficients are stagnant, the filter coefficients are updated using a least mean square recursion, and wherein in response to determining that the filter coefficients are not stagnant, the filter coefficients are updated using a particle swarm optimization procedure;
• repeating the updating step until a predetermined stopping criteria is met;
• and
• filtering a received signal based on the filter coefficients,
• wherein the updating the filter coefficients using the least mean square recursion includes applying
• pi,k+1=pi,k+μAkTei(k)
• where μ is a step size, e is an error value,
• A
• k
• =
• [
• Y
• k
• H
• D
• k
• H
• ]
• ,
• where Y is an input matrix, D is a decision matrix, pi,k is one of the filter coefficients, i is an index, and k is a time instant.