An Analysis of Distributed Joint Spectrum Resource Allocation With Power Control in Ultra-Dense Cellular Networks

Liu, Qiaoshou; Zou, Jianwen; Gu, Yingchen

doi:10.1109/tvt.2022.3189541

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…This algorithm does not consider the network EE. The coordination of interference between the cells of the same tier is analyzed in [41]. An ultra dense scenario composed of only small cells is simulated.…”

Section: Related Workmentioning

confidence: 99%

On optimizing resource allocation: A comparative performance evaluation of resource allocation strategies in 5G

Dobruna,

Fazliu,

Humar

et al. 2024

Preprint

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Resource allocation continues to be a challenging issue in communication networks and its complexity is continuously increasing with the densification of the networks. Resource allocation strategies are numerous and varied, and the goal is to improve any or a number of targets, such as spectral and energy efficiency and overall throughput, among others. In this paper, selected algorithms for resource allocation in wireless communication networks are analyzed and simulations are executed to compare and highlight the importance of the way the resource algorithms are implemented to achieve efficient usage of the available spectrum. In addition, the performance of selected algorithms is evaluated in a multi-cell heterogeneous network and compared to eICIC, a widely-used interference mitigation technique used by communication networks. The results suggest that there is no single algorithm that visibly outperforms other candidates in terms of all performance criteria. Instead, their efficiency is always a consequence of a strategic choice of goals, and the targeted parameters are optimized at a price. Thus, the development and implementation of resource allocation algorithms must follow concrete usage scenarios and needs of the network and depend heavily on network performance requirements and parameters criteria.

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Section: Related Workmentioning

confidence: 99%

On optimizing resource allocation: A comparative performance evaluation of resource allocation strategies in 5G

Dobruna,

Fazliu,

Humar

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Various new requirements for B5G networks are put forward by emerging application scenarios, such as high resource-efficient performance, ultra-low latency, high data rates and high reliability [3], [4]. The network resource efficiency was improved through devices' dense deployment (i.e., form a dense network) in massive IoT, which increases network throughput and provides better quality of service (QoS) for more users [5]. Multiplexing resources has become a fundamental phenomenon in massive IoT networks due to the large-scale dense connectivity of terminal devices (TD).…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT

Yang,

Huang

et al. 2023

IEEE Open J. Commun. Soc.

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Beyond 5G (B5G) networks rapidly growing to connect billions of Internet of Things (IoT) devices and the dense deployment of IoT devices leads the large-scale network conflict and obstacles the resource-efficient, which brings a great challenge for network resource management (NRM). To tackle this problem, hypergraph based resource-efficient collaborative reinforcement learning (CRL) was proposed for B5G massive IoT. Firstly, the hypergraph theory based network conflict model was formulated to quantify the conflict degree of the B5G massive IoT. Then, since the conflict-free resource management problem is a combinatorial optimization problem with NP-hard, the resource management based Markov decision process (MDP) model was built for NRM in B5G massive IoT. To reduce the computational load by distributing the training overhead throughout the entire B5G massive IoT and achieve distributed collaborative learning, the federated averaging advantage Actor-Critic (FedAvg-A2C) based resource management is proposed to handle the network conflict-free resource management problem and accelerate the training process. Simulation results show the proposed scheme has high network throughput and the resource-efficient in B5G massive IoT.

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“…Stochastic geometry and its inherent point process theory have succeeded as a powerful mathematical tool in the realistic modeling and analysis of wireless networks [4], [5], [6]. The most preferred Poisson point process (PPP) model, which is used for deriving analytical expressions, helps to revise and redefine EE in the whole one-tier network having a distributed joint resource allocation strategy with power control [7]. In [8], a 3-D network model is proposed for a multi-tier heterogeneous network (HetNet) with multi-antenna BSs with different heights and densities.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Analysis in Heterogeneous Networks: A Stochastic Geometry Perspective

Ardanuc

Başaran

Hmamouche

et al. 2023

IEEE Open J. Veh. Technol.

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In this paper, we present the energy efficiency (EE) analysis of a multi-tier cellular network, where intra-and inter-tier dependence in the base station (BS) locations are captured via point processes, i.e., three variants of hard-core point processes (HCPPs) and the Poisson hole process (PHP). The analytical expression for EE is derived by approximating both the average power consumption of BSs and the coverage probability using an approximate signal-to-interference ratio analysis based on the Poisson point process (PPP). It is demonstrated that the proposed Matérn HCPP Type-I-PHP model provides better EE results compared to the other HCPP-PHP and PPP models.INDEX TERMS Area spectral efficiency, energy efficiency, hard-core point processes, heterogeneous networks, Poisson hole process, stochastic geometry.

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An Analysis of Distributed Joint Spectrum Resource Allocation With Power Control in Ultra-Dense Cellular Networks

Cited by 7 publications

References 35 publications

On optimizing resource allocation: A comparative performance evaluation of resource allocation strategies in 5G

On optimizing resource allocation: A comparative performance evaluation of resource allocation strategies in 5G

Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT

Energy Efficiency Analysis in Heterogeneous Networks: A Stochastic Geometry Perspective

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