Being a promising candidate technology for the fifth-generation cellular systems, the ultradense network (UDN) is capable of improving the system throughput by deploying more small cells (SCs) in the existing macro-cells (MCs). In most cases, base stations (BSs) from both the MCs tier and SCs tier are modeled as two independent homogeneous Poisson point processes (PPPs) or other point processes derived from a PPP. In the previous works, the area spectral efficiency (ASE) and energy efficiency (EE) are evaluated from a typical user's perspective, and this method works well in single-user scenarios. However, there are multiple users sharing spectral resources in practical cellular systems, and the above-mentioned method cannot work in these scenarios. Therefore, we investigate the ASE and EE from the perspective of a typical BS. Analytical results show that the ASE increases with the increasing SCs density, which means that deploying more SCs in the MCs coverage area is a feasible way to enhance the system throughput. However, the energy consumption of the entire network is increasing as the SCs density increases. Therefore, we employ the firefly algorithm to jointly optimize the ASE and EE for the two-tier heterogeneous UDN. Finally, the optimal system parameters can be found under two fixed weights for the ASE and EE, respectively. INDEX TERMS Area spectral efficiency, energy efficiency, firefly algorithm, Poisson point process, small-cells.
In order to improve management level of mining materials, optimum loading scheme is important. Based on the analysis of bulk cargo loading problem, taking carrying capacity and effective volume as constraint conditions, maximizing transport benefits as target, mathematical model on the base of optimization method is established. And genetic algorithm is introduced to case study. The result shows that genetic algorithm in solving the optimum loading scheme of mining materials has quick convergence, short term, and higher precision. The better satisfactory answer can be obtained after 100 generations. Before 600 generations optimum loading scheme can be educed. Genetic algorithm, with good adaptability and powerful search performance, is very suitable for optimization calculation of multiple constraints problem. Genetic algorithm can make full use of carrying capacity and volume in the process of bulk cargo loading transport, that promot mining enterprise’s operation efficiency. The study is useful for management work of mining material warehousing, scheduling, transportation etc.
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