Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system

Gatti, Ravi; Shankar, Shiva

doi:10.1007/s12652-020-02084-x

Cited by 41 publications

(3 citation statements)

References 23 publications

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“…The relevance of a research constituent, such as author, publication, source, institution, and so on, is measured by the frequency of document production and/or citations. Authors Title Scheduling Algorithms Proposed/Applied/Reviewed/Hybridized Source Title Total Citation Research Area ( Chen et al., 2021 ) Uncertainty-Aware Online Scheduling for Real-Time Workflows in Cloud Service Environment unceRtainty-Aware Online Scheduling Algorithm (ROSA) IEEE Transactions on Services Computing 54 Cloud Computing | Scientific Workflow | Execution Costs ( Goudarzi et al., 2021 ) An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments Weighted Cost Model IEEE Transactions on Mobile Computing 43 Edge Computing | Internet of Things | Location Awareness ( Lv et al., 2021 ) Intelligent edge computing based on machine learning for smart city Alternating Direction Method of Multipliers (ADMM) distributed task scheduling algorithm Future Generation Computer Systems 39 Edge Computing | Internet of Things | Location Awareness ( Gatti and Shivashankar, 2021 ) Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system Improved Resource Allocation Scheme (RAS) Journal of Ambient Intelligence and Humanized Computing 31 Long Term Evolution (LTE) | Local Thermodynamic Equilibrium | Packet Scheduling ( Yuan et al., 2021 ) Be objective Task Scheduling for Distributed Green Data Centers Simulated-annealing-based Bioobjective Differential Evolution (SBDE) Algorithm IEEE Transactions on Automation Science and Engineering 31 Virtual Machine | Data Center | Cloud Computing ( Yang et al., 2020a ) Scheduling Policies for Federated Learning in Wireless Networks Random Scheduling (RS), Round-Robin (RR), and Proportional-fair (PF) IEEE Transactions on Communications 142 Distributed Machine Learning | Function Computation | Federated Learning ( Liu et al., 2020 ) Distributed Set-Membership Filtering for Multirate Systems under the Round-R...…”

Section: Resultsmentioning

confidence: 99%

Examining the developments in scheduling algorithms research: A bibliometric approach

Omotehinwa¹

2022

Heliyon

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Section: Resultsmentioning

confidence: 99%

Examining the developments in scheduling algorithms research: A bibliometric approach

Omotehinwa¹

2022

Heliyon

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“…However, it's worth noting that their approach did not utilize mathematical expressions, such as rate equations, and predominantly relied on heuristic concepts like SFR. In their work [13], the authors presented a resource allocation scheme aimed to enhance the performance of CEUEs within the context of Long Term Evolution-Advanced (LTE-A) systems. Their proposed algorithm focused on the optimal assignment and allocation of Carrier Components Resource Blocks (RBs), and Modulation and Coding Scheme indices to UEs.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks

2024

IJIES

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The deployment of small cells (SCs) in heterogeneous beyond 5G (B5G) networks holds immense promise in meeting the ever-growing demand for data rates and ensuring the desired Quality-of-Service (QoS) for cell-edge users equipment (CEUEs) in densely populated B5G networks. In our pursuit of jointly maximizing the sum-rate while minimizing interference for CEUEs, we present an optimization-based approach with a specific focus on fulfilling the minimum QoS requirements of CEUEs. Our innovative two-step algorithm begins with a reinforcement learning (RL)-based matching process among UEs and available resources, followed by optimal power allocation to UEs based on the matched pairs. At the first step, by leveraging a Q-learning-based method, our algorithm identifies the optimal UEs-resources pairing. The learning process utilizes the achieved sum-rate of each pairing among UEs and resources at each step and converges to a sub-optimal pairing among them. In the second step, power allocation for the selected pairing of the first step is solved in optimal manner. Achieving optimal power allocation is facilitated by exploiting the difference of concaves form of the objective function and harnessing the majorization-minimization (MaMi) technique considering the minimum required QoS of CEUEs.Our numerical results showcase the effectiveness of the proposed scheme, demonstrating near-optimal performance. The results show the employed RL approach effectively converges to the near-optimal pairing among UEs and resources in a dense environment. Additionally, it is evident that the optimal power allocation not only maximizes the sum-rate but also minimizes interference for CEUEs. Considering different values of macro cell (MC) transmission power and SC radius, the proposed schemes achieve a sum-rate enhancement of at least 10% and 25% compared to other existing matching and power allocation methods, respectively.

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“…In this work, seventy percent of the actual traffic load is set as a threshold to identify the congested cell, which will be selected for performing the user offloading. This threshold value has been studied and proposed in [28]. In this work, an improved version of the greedy algorithm is developed and proposed to be used as a cluster formation solution.…”

Section: Proposed Load-aware Greedy Dynamic Comp Clustering Mechanismmentioning

confidence: 99%

Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

Chantaraskul

Tarbut

Nuanyai

2023

Int J Netw Distrib Comput

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Cooperative communications offered by the Coordinated Multipoint (CoMP) technique have been introduced and become an important technique in the 5G mobile networks to support very high system capacity and improve the end-user service quality, especially for the cell-edge users via the dynamic coordination of transceivers at different geographical sites. One major issue driving the performance of CoMP operation is the decision on the coopering clusters or the cluster formation strategies. In this work, an approach of CoMP clustering has been studied and proposed, the so-called load-aware greedy dynamic CoMP clustering mechanism. Our proposed CoMP clustering technique is deployed on top of the actual traffic-based load-aware Dynamic Point Selection CoMP (DPS CoMP) mechanism, which is our previously proposed mechanism. The approach is aimed at maximizing spectral efficiency with appropriate cell coordination based on well-balancing cell loads within the coordinating area of the 5G networks. System performance is numerically studied and observed for the 5G Non-Orthogonal Multiple Access (NOMA) systems embedded with our proposed mechanisms for the realistic scenarios, i.e., homogeneous network and Heterogeneous Network (HetNet) cases. Numerical results illustrate the benefit brought by our approaches in comparison with others CoMP clustering techniques as well as the potential for further improvement.

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Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system

Cited by 41 publications

References 23 publications

Examining the developments in scheduling algorithms research: A bibliometric approach

Examining the developments in scheduling algorithms research: A bibliometric approach

Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks

Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

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