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    • Static Deployment and Energy Efficiency Optimization Strategy of UAV under LoS Probability

      2025, 52(4):91-102.

      Keywords:UAV communication;LoS probability;energy efficiency optimization;power control;height deployment
      Abstract (31)HTML (8)PDF 7.35 M (23)Favorites

      Abstract:Unmanned aerial vehicle (UAV) communication faces challenges such as path loss and intergroup interference. To meet the discrete users’ communication needs, achieve static deployment of UAV networks, and maximize energy efficiency, this paper studies a multi-ratio concave-convex fractional programming problem. A convex optimization cooperative swarm intelligence strategy is proposed, which decouples the original problem into separate power control and height optimization problems, solving them iteratively. Firstly, a line-of-sight (LoS) probability average path loss model is introduced to study the relationship between deployment height and horizontal distance, as well as the three-dimensional deployment problem through pitch angles. Secondly, a quadratic transformation is utilized to decouple the original problem, aiming to enhance system energy efficiency under the LoS probability link. Finally, a fast feedback particle swarm algorithm is proposed for accurate deployment of heights, addressing the complex multi-objective cooperative optimization problem. Simulation results demonstrate that, under the proposed model, the strategy achieves the balance between algorithm complexity and accuracy, enabling efficient and accurate deployment of UAV base stations.

    • Energy Consumption Modeling and QuantitativeCalculation of Servers in Cloud Data Center

      2021, 48(4):36-44.

      Keywords:cloud computing;data center;energy consumption model;task types;energy efficiency optimization
      Abstract (433)HTML (0)PDF 558.17 K (108)Favorites

      Abstract:Building an accurate energy-consumption model of servers can assist resource providers in predicting and optimizing energy consumption of data center. To address the problem of low accuracy of energy consumption model caused by the failure to consider load characteristics of servers in data center, a new energy consumption model and quantitative calculation method are proposed in this paper. The main ideas are summarized as follows: Firstly, we divide the tasks into three classes: CPU intensive task, transactional web task, and I/O intensive task. Then, energy consumption contributions of all components in a server are analyzed. After that, the dominant component parameters of server energy consumption are chosen by using the Principal Component Analysis (PCA), to build a power model through the multiple linear regression method and non-linear regression method. Experimental results show that the prediction accuracy of the proposed energy consumption model can achieve more than 95%. Compared with other energy consumption models, the accuracy can be improved by around 3%.

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