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. 21. The method of claim 1 wherein the HMM model is dependent on time and the states are modeled as a function of a time index; wherein the collected operational data pertains to data activity and CV counts over time; and wherein a forward error correction (FEC) parameter is determined as a function of the time index.