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( Shiyu Ji ),( Liangrui Tang ),( Chen Xu ),( Shimo Du ),( Jiajia Zhu ),( Hailin Hu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10
In future, since the user experience plays a more and more important role in the development of today’s communication systems, quality of experience (QoE) becomes a widely used metric, which reflects the subjective experience of end users for wireless service. In addition, the energy efficiency is an increasingly important problem with the explosive growth in the amount of wireless terminals and nodes. Hence, a QoE-aware energy efficiency maximization based joint user access selection and power allocation approach is proposed to solve the problem. We transform the joint allocation process to an optimization of energy efficiency by establishing an energy efficiency model, and then the optimization problem is solved by chaotic clone immune algorithm (CCIA). Numerical simulation results indicate that the proposed algorithm can efficiently and reliably improve the QoE and ensure high energy efficiency of networks.
A QEE-Oriented Fair Power Allocation for Two-tier Heterogeneous Networks
( Shiyu Ji ),( Liangrui Tang ),( Yanhua He ),( Shuxian Li ),( Shimo Du ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5
In future wireless network, user experience and energy efficiency will play more and more important roles in the communication systems compared to their roles at present. Quality of experience (QoE) and Energy Efficiency (EE) become the widely used metrics. In this paper, we study a combinatorial problem of QoE and EE and investigate a fair power allocation in heterogeneous networks. We first design a new metric, QoE-aware EE (QEE) to reflect the relationship of QoE and energy. Then, the concept of Utopia QEE is introduced, which is defined as the achievable maximum QEE in ideal conditions, for each user. Finally, we transform the power allocation process to an optimization of ratio of QEE and Utopia QEE and use invasive weed optimization (IWO) algorithm to solve the optimization problem. Numerical simulation results indicate that the proposed algorithm can get converged and efficiently improve the system energy efficiency and the QoE for each user.