1. A method of controlling an operational line in a DSL system, the method comprising: constructing a Hidden Markov Model (HMM) to model one or more internal states of the DSL system, the states characterizing at least one of data activity, impulse noise, crosstalk, noise margin, maximum attainable data rate or bit distributions; collecting update operational data from the operational line; analyzing the collected update operational data, the analyzing farther comprising: estimating an observation probability, a state-transition probability matrix, and an initial state distribution of the HMM based on the collected update operational data; estimating a likelihood of at least one state of the HMM based on the collected update operational data; and controlling operation of the DSL system based on the constructed HMM and analyzed update operational data, the controlling further comprising: determining control parameters based on the estimated likelihood of at least one state of the HMM; and modifying operation of the operational line through application of the determined control parameters to reflect the information available from the HMM.
1. A method of controlling an operational line in a DSL system, the method comprising: constructing a Hidden Markov Model (HMM) to model one or more internal states of the DSL system, the states characterizing at least one of data activity, impulse noise, crosstalk, noise margin, maximum attainable data rate or bit distributions; collecting update operational data from the operational line; analyzing the collected update operational data, the analyzing farther comprising: estimating an observation probability, a state-transition probability matrix, and an initial state distribution of the HMM based on the collected update operational data; estimating a likelihood of at least one state of the HMM based on the collected update operational data; and controlling operation of the DSL system based on the constructed HMM and analyzed update operational data, the controlling further comprising: determining control parameters based on the estimated likelihood of at least one state of the HMM; and modifying operation of the operational line through application of the determined control parameters to reflect the information available from the HMM. 2. The method of claim 1 wherein the HMM is constructed to model one or more internal states characterizing at least data activity; wherein the observation probability, the state-transition probability matrix, and the initial state distribution of the HMM are estimated with the Baum-Welch algorithm; wherein the likelihood of at least one state of the HMM is estimated with the Viterbi algorithm; and wherein the control parameters are determined based on the estimated likelihood of at least one state of the HMM only for those states with data activity.