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Pichice Studio – Chapter 2.rar 8.1 MB Test files are not included.21.12.2011 – 2:24 871.22.12.2011 – 11:58 Log-in less than $\gamma$ and $w^{k}_{i}$ will also not be changed unless the decision outcome is already known at this iteration.

The main drawback of the SC method lies in the difficulty of correctly optimizing the approximate solution in the last iteration, which could be more or less different than the exact solution. We do not address this problem for two reasons: (1) finding the solution of the exact problem is very hard, as formulated by Ray and Sanders [@6614941]; (2) even if we find the correct solution to the exact problem, the right way of utilizing the solution to accelerate the computation of the next iteration is not clearly discussed. We plan to deal with these problems in future research.

Simulation Results {#sec6}
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In this section, some numerical examples are provided to verify the effectiveness of the proposed SC method. The examples in this paper focus on the random channels between a transmitter and a receiver. The number of randomized parameters is denoted by $N$. The simulation parameters are shown as follows. Unless otherwise specified, the training symbol duration is set as $T=20$. The number of LUs is $L=40$ and the noise power spectral density $N_{0}=-110$ dBm. The circuit power consumption for RX or TX of each LU is set as $P_{c}=4$ mW. The unit cost for each LU is denoted by $C_{F}$. The hardware energy consumption for each state machine ($G$) is set as $E_{h}=0.1$ mJ, and the unit cost for each state machine ($C_{F}$) is set as $0.1$. Finally, the unit cost for computing in each iteration is denoted by $C_{com}$. The radius of the search space is set as \$r=