دانلود رایگان مقاله انگلیسی الگوریتم های بهینه سازی برای ارتباطات سبز چند منظوره در اینترنت اشیا - IEEE 2018

Abstract

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things. In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in Internet of Things, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the non-linear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron-Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.

CONCLUSION

In this paper, we formulated a total power minimization problem according to the Shannon capacity formula with power and SINR constraints. We convexified it by leveraging the nonnegative matrix theory to obtain a convex optimization problem with rate as the only variable. Thus, the total power minimization problem is polynomial time solvable to get an approximated value. Motivated by the upper bound of the approximation value, we obtained the lower bound by employing the convex relaxation technique. Leveraging the branch-and-bound framework, we took the convex approximation method as an inner loop to compute the global optimal value. Numerical simulations demonstrated that our proposed algorithms can achieve the efficient power and rate allocations.