Optimal resource allocation for energy efficiency in coordinated multicell OFDMA networks

2014 ◽  
Vol 28 (14) ◽  
pp. 2020-2034 ◽  
Author(s):  
Hiep H. Nguyen ◽  
Suk-Hwan Lee ◽  
Won-Joo Hwang
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiquan Bai ◽  
Tongtong Wang ◽  
Piming Ma ◽  
Yanbo Ma ◽  
Kyungsup Kwak

We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the β-fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies.


2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Fan Wu ◽  
Yuming Mao ◽  
Xiaoyan Huang ◽  
Supeng Leng

This paper focuses on radio resource allocation in OFDMA networks for maximizing the energy efficiency subject to the data rate requirements of users. We propose the energy-efficient water-filling structure to obtain the closed-form optimal energy-efficient power allocation for a given subcarrier assignment. Moreover, we establish a new sufficient condition for the optimal energy-efficient subcarrier assignment. Based on the theoretical analysis, we develop a joint energy-efficient resource allocation (JERA) algorithm to maximize the energy efficiency. Simulation results show that the JERA algorithm can yield optimal solution with significantly low computational complexity.


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