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It is shown that the total transmit power obtained by the iterative algorithm converges to the optimality rapidly in the iteration, and the low complexity algorithm performs close to the iterative algorithm.
For a given set of data, we propose to find the optimal joint parameter estimates by the iterative algorithm details in Algorithm 1, with the superscript denoting iteration number.
Let, and let be generated by the iterative algorithm (3).
Suppose and define a sequence by the iterative algorithm (3.34).
Let be a sequence defined by the iterative algorithm: (4.4).
Let, and define a sequence by the iterative algorithm: (4.1).
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Then we can achieve the optimal solution by rewriting the iterative algorithm given in [25] as shown in Algorithm 2. The convergence of the algorithm has been proved in [25].
Under certain conditions, we obtain the existence of solutions for the system of generalized nonlinear mixed quasivariational inclusions and prove the convergence of the iterative sequences generated by the iterative algorithms.
Finally, we prove the existence of solutions for the system of generalized nonlinear mixed quasivariational inclusions and show the convergence of the iterative sequences generated by the iterative algorithms in Hilbert spaces.
By considering this function and by defining ψ = ρ′ and a weighting function by Equation (3), the iterative algorithm to estimate the unknown parameter can be defined.
By repeating this procedure, the iterative algorithm converges to the global optimal solution.
More suggestions(15)
by the iterative solution
by the iterative sequence
by the iterative convolution
by the iterative second-order
by the iterative computation
by the iterative formula
by the iterative initialization
by the iterative receiver
by the iterative solver
by the iterative process
by the iterative average
by the iterative planning
by the iterative decoding
by the iterative scheme
by the iterative application
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