Energy-Efficient Power Control and Resource Allocation for D2D Communications in Underlaying Cellular Networks

Guan, Xiaoxiao; Zhai, Xiangping; Yuan, Jian; Hu, Liu

doi:10.1007/978-3-319-68505-2_40

Cited by 2 publications

(1 citation statement)

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“…An efficient way to improve the energy efficiency of social-aware D2D communications is to optimize the transmit power allocation of D2D nodes [24] [8]. For this purpose, in [24], the authors focused on maximizing the energy efficiency of D2D users by optimizing the transmit power and sub-channel allocation. The method jointly allocated the transmit powers and sub-channels to the D2D users using penalty function and dual-decomposition methods, respectively, while guaranteeing the QoS for cellular users.…”

Section: A Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Energy Efficiency Maximization for Social-Aware Device-to-Device Communications

Nadeem

Cho

2021

IEEE Access

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In this paper, we propose a novel energy efficiency maximization scheme for social-aware device-to-device (D2D) communications based on a genetic algorithm (GA). The proposed scheme incorporates both social and physical parameters of users to model the energy efficiency maximization problem. The formulated problem considers the spectral reuse, spectral efficiency, and the transmit power constraints of both cellular and D2D users to satisfy their quality of service requirements. Moreover, an algorithm based on the self-adaptive penalty function is applied to convert the constrained problem into an unconstrained problem. Next, GA is utilized to maximize the unconstrained problem. The feasibility of the proposed scheme is shown by computing its time complexity in terms of big-O notation. Moreover, the convergence of the proposed scheme is analyzed by comparing the maximum and average values of the overall energy efficiencies for different iterations. Likewise, the performance is evaluated in terms of overall energy efficiency and system throughput for various D2D scenarios. To demonstrate the efficiency of the proposed scheme, the results are compared with those for a static penalty-based GA algorithm. Furthermore, to demonstrate the significance of combining the two types of parameters (i.e., social and physical), the performance of the proposed scheme is compared with schemes based on only social or physical parameters.

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Section: A Related Workmentioning

confidence: 99%