1) Estimation algorithms: The complexity of the estimation technique is directly related to the size of the coefficient vector.
For this, only the major processes in each stage are compared like correlogram, segmentation, pitch estimation… The complexity of computing correlograms is O (CLlogW), where W is the time frame.
As for the different channel estimation algorithms, the complexity of the considered MUDs differ significantly, as seen from Table 2.
Moreover, large particle number is usually required to obtain an accurate estimation, and the complexity of the resampling procedure is highly related to the number of particles.
One of the main difficulties in parameter estimation is the complexity of the objective function (and the derived updating rules) which requires a scoring of all the bad paths for evaluating the denominator.
In order to achieve a good tradeoff between estimation accuracy and the complexity of measurement, an appropriate choice for the sampling set of link gains is important.
Starting from three standard methods to implement the novel estimation algorithms based on sensor networks for distributed parameter systems, in the discretized version of them, we are showing what is the better method to increase the quality of estimation, related to the complexity of implementation.
The creation of reliable kinetic models involves the estimation of parameters, the complexity of this task increasing with the size of the network considered.
Then a rough estimation tells us that the complexity of the CDIT-based allocation is reduced by compared to the perfect CSIT scheme.
The negativity condition (e ii + w i ηi < 0), however, is not incorporated, partly because there is no reduction in the number of parameters to be estimated and, thus, no gain in asymptotic efficiency of the estimates, and partly to avoid introducing parametric inequality constraints that would increase the complexity of estimation.
