The first strategy is distributed and comprises a local computation step and a message passing step that are iteratively applied at each vertex until convergence.
These messages are updated at each iteration, until convergence.
Therefore, we will study through simulations the extent (and severity) of the distortion introduced in the estimates of ({ mathcal {R}(k) }_{k}) obtained, at each node, upon convergence to quantized consensus from localized exchanges of quantized versions of {y n (k)} k, ∀n∈{1,…,N}.
The vector ψ and the diagonals of W and W v are updated at each iteration until convergence.
Visual inspection of the trace plot produced by plotting a Euclidean distance at each iteration confirmed convergence of this model parameter.
The estimates achieved by each sensor at convergence of the unknown source parameters are shown for 10 independent trials of the experiment.
The capacity value is updated at each iteration and greatly improved at the convergence time.
We quantified the extent of sequence convergence at each locus by taking the mean of its ΔSSLS values, and found 824 loci with mean support for H1 and 392 for H2.
Comparing the counts of parallel substitutions with the ΔSSLS evidence for convergence at each locus, we found extremely good correspondence between the two measures; although 551 loci lacked any parallel substitutions for H1 (441 under H2) overall, only six of our 5% most convergent loci (n = 118) lacked parallel changes for H1 (seven in the most corresponding H2 comparison).
In particular, the proposed algorithm directly optimizes the WISL without limitations on the weights filling the gap in the open literature and involves the FFT operation at each iteration ensuring the convergence speed.
Starting with a maximally connected configuration in which all nodes transmit at full power, the proposed methods successively reduce the transmit power of a chosen node in order to remove one and only one link; nodes are greedily selected either in order to yield fastest convergence at each step, or if they have the largest degree in the network.
