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We use the MATLAB code in [16] to generate the realization of channel impulse response h(t).
The first criterion includes systems which optimize the transmission power to maximize the rate for a given realization of channel gains such as [19 21, 24, 25].
For any realization of channel matrix, there exist unique values of the Lagrange dual variables and for any Nash equilibrium of the game.
(For more on prescient bounds, see, e.g., [20].) For each realization of channel gains, the flow rate control problem reduces to the optimization problem (37).
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Figure 3 Rate results for the random realization of channels.
Note that the complexity of the proposed method is independent of the channel realizations whilst the computational complexity of SDA relies on the specific realization of channels and SNR.
Within the Monte Carlo simulation, 10 6 realizations of channel are used.
The simulations were conducted for 100 independent realizations of channel condition and data arrival processes, each over 10,000 time slots.
In the first part of our study, the resulting received SINR against η for many realizations of channel matrices and for various values of transmitted SNR is computed under different network's configurations.
The corresponding MSE is (epsilon ={sigma _{s}^{2}}-mathbf {C}_{s mathbf {y}} mathbf {C}^{-1}_{mathbf {y} mathbf {y}} mathbf {C}_{mathbf {y} s}), which depends on the specific realizations of channel responses h i 's.
Realizations of channel networks with a self-similar ramification such as those found in nature can be produced using fractals, which are continuous but non-differentiable functions, and comparison shows that optimal channel network computed from DEMs is very accurate and corresponds well with observed stream networks (Rinaldo et al., 1998).
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