Device-to-device resource allocation in LTE-advanced networks by hybrid particle swarm optimization and genetic algorithm

Sun, Shijie; Kim, Kwang-Yul; Shin, Oh‐Soon; Shin, Yoan

doi:10.1007/s12083-015-0424-1

Cited by 27 publications

(23 citation statements)

References 8 publications

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“…Sun et al 27 have presented a device-to-device (D2D) resource allocation in LTE-A network using hybrid PSO genetic algorithm (GA). For maximizing the throughput of the system, this approach shares the same frequency resource with one cellular UE (CUE), which allows two D2D pairs only.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Trans Emerging Tel Tech

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Nowadays, Long-Term Evolution Advanced (LTE-A) network is the primary innovation in 4G networks. The LTE-A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE-A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0-1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0-1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)-based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE-A network than state-of-art methods in the proposed strategy.Trans Emerging Tel Tech. 2019;30:e3748.wileyonlinelibrary.com/journal/ett

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Section: Related Workmentioning

confidence: 99%

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Trans Emerging Tel Tech

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“…It is too subjective and inefficient, and cannot alter weighting factors in a timely manner according to the changes of the network to satisfy the QoS requirements well. To address these problems, RPL-CGA uses a chaotic genetic algorithm [23,24] to optimize the weighting factors of routing metrics in the composition metric to evaluate candidate parents comprehensively when selecting preferred parents. In this way, RPL-CGA can easily choose the optimum candidate parent (neighbor) as the preferred parent (the next hop), and achieves significant improvement on the network performance of LLNs in the aspect of average end-to-end delay, average success rate, etc.…”

Section: Rpl-cgamentioning

confidence: 99%

A Novel RPL Algorithm Based on Chaotic Genetic Algorithm

Cao

Muqing

2018

Sensors

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RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc.

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“…In theory, when channel state information (CSI) is assumed to be known by the decision center, several optimization methods could be proposed to solve the problem of channel and power allocation in D2D communications . In the work of Chien et al, a joint optimization problem for both mode selection and resource allocation is formulated to maximize the sum rate by considering intracell traffic based on mixed‐integer nonlinear programming.…”

Section: Introductionmentioning

confidence: 99%

“…In the work of Le, a fair resource allocation scheme for D2D communications in orthogonal frequency‐division multiple‐access–based wireless cellular networks is introduced, in which for resource allocation in both uplink and downlink, the max‐min fairness is considered. In the work of Sun et al, two resource allocation algorithms based on particle swarm optimization and hybrid particle swarm optimization‐genetic algorithm are proposed to maximize the system throughput, in which the maximum number of two D2D pairs are allowed to share the same frequency resources with one cellular user.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

Rashed

Shahbazian

Ghorashi

2017

Trans Emerging Tel Tech

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In device-to-device (D2D) communications, D2D users establish a direct link by utilizing the cellular users' spectrum to increase the network spectral efficiency. However, due to the higher priority of cellular users, interference imposed by D2D users to cellular ones should be controlled by channel and power allocation algorithms. Due to the unknown distribution of dynamic channel parameters, learning-based resource allocation algorithms work more efficient than classic optimization methods. In this paper, the problem of the joint channel and power allocation for D2D users in realistic scenarios is formulated as an interactive learning problem, where the channel state information of selected channels is unknown to the decision center and learned during the allocation process. In order to achieve the maximum reward function by choosing an action (channel and power level) for each D2D pair, a recency-based Q-learning method is introduced to find the best channel-power for each D2D pair. The proposed method is shown to achieve logarithmic regret function asymptotically, which makes it an order optimal policy, and it converges to the stable equilibrium solution. The simulation results confirm that the proposed method achieves better responses in terms of network sum rate and fairness criterion in comparison with conventional learning methods and random allocation.

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Device-to-device resource allocation in LTE-advanced networks by hybrid particle swarm optimization and genetic algorithm

Cited by 27 publications

References 8 publications

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

A Novel RPL Algorithm Based on Chaotic Genetic Algorithm

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

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