This paper deals with the minimum-error-probability(MEP)channelequalization problem and its realizations using k-nearest neighbor rule andbackpropagation(BP)neural nets.The main contributions include:(1)it shows that ...This paper deals with the minimum-error-probability(MEP)channelequalization problem and its realizations using k-nearest neighbor rule andbackpropagation(BP)neural nets.The main contributions include:(1)it shows that in thecase of the maximum possiblc value of the intcrsymbol intcrfcrcnce less than the magni-tude of the dcsircd symbol,the channcl equalization problcm is always lincarly separable;(2)the basic concepts and rclations of the MEP equalization are introduccd,and somenumcrical rcsults are providcd to indicate the performance advantage over the linear equal-izer;(3)subsequently prescntcd are the MEP adaptive equalizer implemented by k-nearestneighbor rule and the theorems regarding the asymptotic convergence and error bounds;(4)and finally it shows that the BP neural nets with appropriatc laycrs and nodes,whichtake minimization of mcan square crror(MSE)as the optimization goal,can also minimizethe error probability,thus leading to another realization of the MEP cqualizer.展开更多
文摘This paper deals with the minimum-error-probability(MEP)channelequalization problem and its realizations using k-nearest neighbor rule andbackpropagation(BP)neural nets.The main contributions include:(1)it shows that in thecase of the maximum possiblc value of the intcrsymbol intcrfcrcnce less than the magni-tude of the dcsircd symbol,the channcl equalization problcm is always lincarly separable;(2)the basic concepts and rclations of the MEP equalization are introduccd,and somenumcrical rcsults are providcd to indicate the performance advantage over the linear equal-izer;(3)subsequently prescntcd are the MEP adaptive equalizer implemented by k-nearestneighbor rule and the theorems regarding the asymptotic convergence and error bounds;(4)and finally it shows that the BP neural nets with appropriatc laycrs and nodes,whichtake minimization of mcan square crror(MSE)as the optimization goal,can also minimizethe error probability,thus leading to another realization of the MEP cqualizer.
