Energy Efficient Resource Allocation Algorithm in Energy Harvesting-Based D2D Heterogeneous Networks

Kuang, Zhufang; Liu, Gang; Li, Gongqiang; Deng, Xiaoheng

doi:10.1109/jiot.2018.2842738

Cited by 143 publications

(66 citation statements)

References 51 publications

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“…The terminal handover in SDNbased WLANs can effectively improve the QoS and the resource utilization of WLANs. As a new computing paradigm, software-defined networking can also be applied to cellular networks [10], D2D heterogeneous networks [11], MESH networks [12], cloud computing [13], and edge computing [14] to facilitate resource allocation, provide service quality assurance, and improve user experience.…”

Section: Background and Motivationmentioning

confidence: 99%

Terminal handover in software-defined WLANs

Chen

Duan

Zhang

2020

J Wireless Com Network

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With the development of wireless LAN (WLAN) technology, wireless access points (APs) are deployed increasingly more and the number of mobile terminals is increasing. It is normal for a mobile terminal to hand over its AP. The traditional terminal handover method cannot effectively control the handover delay and balance the network load due to the inherent drawbacks such as closed encapsulation and the lack of centralized control. In this paper, we introduce the innovative network architecture of software-defined networking (SDN), propose a software-defined WLAN architecture, and propose a seamless handover method and a load balancing handover method of the softwaredefined WLAN. Experimental results show that the proposed handover method effectively reduces the handover delay, balances the network load, and improves the quality of service (QoS) and resource utilization of the network.

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Section: Background and Motivationmentioning

confidence: 99%

Terminal handover in software-defined WLANs

Chen

Duan

Zhang

2020

J Wireless Com Network

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“…Constraints in (4) imply that a maximum of one RB can be allocated to each D2D pair. Since CUEs are the primary users of the frequency band, the transmission quality of CUEs should be ensured, constraints in (5) imply that the SINR of CUEs should satisfy a predefined constraint, where ξ c min represents the SINR threshold of the CUE.…”

Section: B Problem Formulationmentioning

confidence: 99%

A Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation Framework for D2D Communications

Guo

Xuan

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Device-to-device (D2D) communication has been recognized as a promising technique to improve spectrum efficiency. However, D2D transmission as an underlay causes severe interference, which imposes a technical challenge to spectrum allocation. Existing centralized schemes require global information, which can cause serious signaling overhead. While existing distributed solution requires frequent information exchange between users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named Neighbor-Agent Actor Critic (NAAC). NAAC uses neighbor users' historical information for centralized training but is executed distributedly without that information, which not only has no signal interaction during execution, but also utilizes cooperation between users to further optimize system performance. The simulation results show that the proposed framework can effectively reduce the outage probability of cellular links, improve the sum rate of D2D links and have good convergence.

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“…In [14][15][16][17], the D2D users harvest energy from the surrounding environments. Authors in [14] utilize the Paretooptimal boundary and a subcarrier allocation method to allocate power and channel for D2D users under the energy causality constraint and battery overflow constraint.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], authors propose a lowcomplexity energy-aware space matching method to solve the channel and power allocation problems. In [17], an iterative algorithm based on Dinkelbach and Lagrangian constraint optimization is proposed to maximize the average energy efficiency of D2D users. However, authors [14][15][16][17] assume that the full system information is available before the transmission process and propose the offline algorithm.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Algorithm for NOMA-Enhanced D2D Communications with Energy Harvesting

Zhu

Wang

2020

Mobile Information Systems

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In this work, we propose a channel allocation and power control algorithm for energy harvesting (EH) device-to-device (D2D) communication based on nonorthogonal multiple access (NOMA). e algorithm considers users' quality of service (QoS) and energy causality constraint to maximize the total capacity of D2D groups. e optimal offline allocation of channel and power is realized firstly. en, the offline optimization results are taken as the training dataset to train the neural network to obtain the optimal model of the transmission power. e online power allocation optimization algorithm is further proposed. Simulation results show that the offline algorithm can improve the total capacity of D2D groups, and the performance of the online algorithm is close to the offline algorithm.

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Energy Efficient Resource Allocation Algorithm in Energy Harvesting-Based D2D Heterogeneous Networks

Cited by 143 publications

References 51 publications

Terminal handover in software-defined WLANs

Terminal handover in software-defined WLANs

A Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation Framework for D2D Communications

Resource Allocation Algorithm for NOMA-Enhanced D2D Communications with Energy Harvesting

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