  A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a
state transition probability matrix $A$, a prior probability vector $\Pi$, and
the state dependent output symbol error probability matrix $B$. Several
processes are utilized for estimating $A$, $\Pi$ and $B$ from a given
empirically obtained or simulated error sequence. However, despite placing some
restrictions on the underlying Markov model structure, we still have a
computationally intensive estimation procedure, especially given a large error
sequence containing long burst of identical symbols. Thus, in this paper, we
utilize under some moderate assumptions, a Markov model with random state
transition matrix $A$ equivalent to a unique Block Diagonal Markov model with
state transition matrix $\Lambda$ to model an indoor software-defined power
line communication system. A computationally efficient modified Baum-Welch
algorithm for estimation of $\Lambda$ given an experimentally obtained error
sequence from the indoor PLC channel is utilized. Resulting Equivalent Block
Diagonal Markov models assist designers to accelerate and facilitate the
procedure of novel PLC systems design and evaluation.
