Distributed resource allocation for D2D communication networks using Auction

Zaki, Fayez W.; Kishk, Sherif; Almofari, N. H.

doi:10.1109/nrsc.2017.7893487

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…But with the increasing number of devices, the base stations are suffering from computational overhead. To deal with this issue many distributed resource allocation algorithms [89]- [91] have been proposed in which the cellular and D2D users independently and opportunistically utilize the resources without involving base station in global information calculation. These schemes have the advantage in big network designs where interference mitigation is the concern.…”

Section: ) Rm In D2d Using Conventional Methodsmentioning

confidence: 99%

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

et al. 2021

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With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This paper gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

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Section: ) Rm In D2d Using Conventional Methodsmentioning

confidence: 99%

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

et al. 2021

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“…Distributed schemes can work well to large networks, but require complicated interference avoidance algorithms to ensure high quality cellular communications and reliable D2D communications. Some distributed algorithms are based on game theory [12]- [14]. Game theory is used to model D2D pairs sharing spectrum resources with CUEs as an auction mechanism in [12].…”

Section: Arxiv:191209302v1 [Csni] 18 Dec 2019mentioning

confidence: 99%

“…Some distributed algorithms are based on game theory [12]- [14]. Game theory is used to model D2D pairs sharing spectrum resources with CUEs as an auction mechanism in [12]. A distributed resource allocation has been proposed in [13] to guarantee a minimum data rate for CUEs and to maximize the D2D average data rate.…”

Section: Arxiv:191209302v1 [Csni] 18 Dec 2019mentioning

confidence: 99%

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

Guo

2020

IEEE Trans. Veh. Technol.

123

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Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation. Existing centralized schemes require global information, which causes a large signaling overhead. While existing distributed schemes requires frequent information exchange among D2D users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multiagent deep reinforcement learning is proposed, named multiagent actor critic (MAAC). MAAC shares global historical states, actions and policies during centralized training, requires no signal interaction during execution and utilizes cooperation among users to further optimize system performance. Moreover, in order to decrease the computing complexity of the training, we further propose the neighbor-agent actor critic (NAAC) based on the neighbor users' historical information for centralized training. The simulation results show that the proposed MAAC and NAAC can effectively reduce the outage probability of cellular links, greatly improve the sum rate of D2D links and converge quickly.Index Terms-Device-to-device (D2D) communications, multiagent deep reinforcement learning, spectrum allocation.

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“…Penugasan resource untuk agen bertujuan untuk memaksimalkan nilai net [2]. (8) Persamaan ( 8) menunjukkan bahwa agen i ditugaskan ke resource yang memberikan nilai net maksimum.…”

Section: Algoritma Lelang (Auction) Untuk Alokasi Resourceunclassified

“…Hal tersebut dikarenakan kebutuhan manusia terhadap teknologi semakin tinggi untuk membantu dan memudahkan pekerjaan manusia. Perkembangan teknologi seluler di Indonesia saat ini telah memasuki era 4G dengan jaringan pita lebar 4G LTE pada pita frekuensi 1800 MHz di kuartal pertama tahun 2015 berdasarkan keterangan dari Menteri Komunikasi dan Informatika, Rudiantara [8].…”

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Radio Resource Allocation For Device to Device Network Using Auction Algorithm

Ramadhan¹,

Sigit

Fahmi

2019

tiarsie

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One application of communication from the 5G network in the future is to implement Device to Device (D2D) into heterogeneous multi-tiered communication networks consisting of small cell communications between eNB, cellular and D2D. The application of D2D is useful for the future even though it has several problems with one of them being interference with the frequency of other devices in the same cell. This can affect Quality of Service (QoS) in D2D communication so that it requires the application of a resource allocation distribution that can increase data rate and reduce interference. One of the algorithms used for the distribution of resource allocation in communication network systems is the Auction allocation algorithm. The auction allocation algorithm introduced in this journal provides a solution to divide resources fairly for all D2D pairs. The data rate increases by increasing the number of resource blocks and decreasing the cell radius.

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Distributed resource allocation for D2D communication networks using Auction

Cited by 20 publications

References 15 publications

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

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

Radio Resource Allocation For Device to Device Network Using Auction Algorithm

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