Adaptive Resource Sharing Algorithm for Device-to-Device Communications Underlaying Cellular Networks

Esmat, Haitham H.; Elmesalawy, Mahmoud M.; Ibrahim, I. I.

doi:10.1109/lcomm.2016.2517012

Cited by 57 publications

(57 citation statements)

References 8 publications

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“…Two metrics are used to estimate the performance of the suggested algorithm. The first one is the access rate that is defined as the ratio of the number of SUEs that achieved the target QoS to the total number of SUEs that this metric is used in several previous research works to evaluate the performance of such algorithms –. Then, the total achieved throughput of SCs is used as the second metric.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Joint radio resource and power allocation using Nash bargaining game for H‐CRAN with nonideal fronthaul links

Esmat

Elmesalawy

Abdelhakam

2018

Trans Emerging Tel Tech

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Heterogeneous cloud radio access network (H‐CRAN) is introduced as a novel network architecture that takes the full rewards of both heterogeneous cellular network (HCNs) and cloud radio access network (CRAN). In H‐CRAN architecture, the remote radio heads of small cells (SCs) are overlaid within the coverage of macro cell and linked to the baseband unit pool through nonideal fronthaul connections. To improve the spectral efficiency of this network, the SCs are developed to reuse the same resources used by the macro cell. However, this will introduce inter‐tier interference between macro cell and SCs and intra‐tier interference among SCs that need to be efficiently managed. In this paper, the joint problem of radio resource and power allocation in H‐CRAN is formulated using cooperative Nash bargaining game. This optimization problem is designed to maximize the total throughput of SCs, while considering the fairness between user equipments (UEs) in each SC, interference protection requirements for macro UEs, outage probability requirements, and the capacity limitation of nonideal fronthaul connections. Then, by using Lagrangian dual decomposition, the near‐optimal bargaining allocation strategy for radio resources and power of each SC UE is determined. Simulation results illustrate that the performance of the suggested algorithm outperforms the other comparable algorithm in terms of the SCs' achieved throughput and fairness. Moreover, the effect of nonideal fronthaul capacity constraint and the interference protection limit of macro UEs on the performance of the suggested scheme are examined.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Substitute (36) into (35), the (W, A, , , , ) w q,n,k = E q,n,k − q,n,k (37) SUE that has the highest E q,n,k in RRH q will use the channel k, 31,32 that is,…”

Section: Bargaining Solution For Radio Resource and Pamentioning

confidence: 99%

Joint radio resource and power allocation using Nash bargaining game for H‐CRAN with nonideal fronthaul links

Esmat

Elmesalawy

Abdelhakam

2018

Trans Emerging Tel Tech

Self Cite

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“…Resource allocation in D2D communication is such an application. Here, we describe at first some classical approaches [7][8][9][10][11][12][13][14][15][16] followed by existing RL-based resource allocation algorithms [17,18].…”

Section: Related Workmentioning

confidence: 99%

“…However, their proposed method is not adaptive regarding power allocation to the users. In [14], the authors propose a two-phase optimization algorithm for the adaptive resource allocation which provides better results for system throughput. They propose Lagrangian dual decomposition (LDD) which is computationally complex.…”

Section: Related Workmentioning

confidence: 99%

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Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

et al. 2017

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Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.

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Semi‐distributed resource management for underlay D2D communication with user's cooperation

Gour

Tyagi

2019

Int J Communication

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Device-to-device (D2D) communication is a viable solution proposed by the Third Generation Partnership Project (3GPP) to handle the enormous number of devices and expected data explosion in 5G. It is competent in enhancing the system performances such as increased data rate, reduced delay, and less power consumption while maintaining a low load on the base station (BS). In this paper, channel assignment and power control scheme is proposed for underlay D2D system where one cellular channel is allowed to be shared among multiple D2D pairs. This will lead to enhanced spectral efficiency on the cost of additional interferences introduced among the D2D and cellular users (CUs). Our aim is to maximize the D2D throughput without degrading the performance of existing CU that is sharing the channel with D2D. This is achieved by maintaining a threshold signal-to-interference-plus-noise ratio (SINR) for each CU. A centralized channel assignment algorithm based on the well-known two-sided preference Gale-Shapley algorithm is proposed, named as RAbaGS-HR. Further, suboptimal distributed power control (DPC) algorithms are proposed for both uplink and downlink D2D. The novelty of the work lies in the facts that a channel is shared among multiple D2D users and the optimal power is calculated for all the users sharing the same channel under the full consideration of all kinds of interferences unlike most of the existing work that either assumed the fixed CU power or ignored the interferences among the D2D users. Numerical results show the efficacy of the proposed algorithms in terms of significant gain in throughput with a very low computational cost. In addition to this, the energy efficiency (EE) is also analyzed for different D2D user density, with respect to average circuit power consumption and D2D maximum transmit power.

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Adaptive Resource Sharing Algorithm for Device-to-Device Communications Underlaying Cellular Networks

Cited by 57 publications

References 8 publications

Joint radio resource and power allocation using Nash bargaining game for H‐CRAN with nonideal fronthaul links

Joint radio resource and power allocation using Nash bargaining game for H‐CRAN with nonideal fronthaul links

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Semi‐distributed resource management for underlay D2D communication with user's cooperation

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