In the actual deployment of a wireless communication system, the actual network planning optimization, capacity, blind spots and other studies are conducted according to the radio propagation environment.
In this paper, a bi-loop optimization framework of stiffened panels is proposed to search the global optimum, which includes an adaptive response surface method (ARSM) loop and a Gaussian global-best harmony search (GGHS) loop, aiming to improve the global optimization capacity in an efficient manner.
This study presents the development and implementation of a novel framework for optimal design of new and emerging renewable energy production systems by considering an iterative strategy which integrates the Net Present Value optimization along with detailed mechanistic modeling, simulation, and process optimization which yields optimal capacity plan, and operating conditions for the process.
Moreover, the uneven traffic distribution leads to new challenges for the coverage optimization and capacity enhancement.
It has also been applied to cellular networks, such as: self-organized and distributed interference management for femtocell networks [14], self-organized resource allocation scheme [15], cell selection scheme [16], and self-optimization of capacity and coverage scheme [17].
Three independent variables including temperature, methanol to water ratio percent, and sonication time were studied for simultaneous optimization of antioxidant capacity, total phenolic content and extraction yield.
In this scenario, the uncovered planned cell area is the coverage holes that need to be optimized by the coverage and capacity optimization [39].
We concentrate on what is known as the waterfilling-in-time strategy, since it is the optimal solution with respect to capacity optimization under an average transmit power constraint when both the AP and the mobile user have perfect and instantaneous knowledge of channel conditions [21].
In order to efficiently balance traffic demand and capacity, optimization of Air Traffic Flow Management (ATFM) relies on accurate predictions of future capacity states.
Also, in capacity optimization, the design objective for load shifting involves squaring, which benefits capacity optimization by emphasizing large shifting indexes.
