Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Tinh, Bui Thanh; Nguyen, Long D.; Kha, Ha Hoang; Duong, Trung Q.

doi:10.1109/access.2022.3146335

Cited by 22 publications

(8 citation statements)

References 123 publications

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“…Tinh et al 23 developed a realtime optimization‐based game theory in UDN (ROG‐UDN) for enhancing UDN performance and achieving 6G requirement. The joint optimal approach was used to solve the large scale UDN's optimization problem with low complexity.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

M¹,

Shobanadevi²

2023

Int J Communication

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Summary The ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s.

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Section: Literature Surveymentioning

confidence: 99%

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

M¹,

Shobanadevi²

2023

Int J Communication

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“…The 5G and beyond mobile networks can be characterized by their dense nature. This density necessitates the use of smaller BSs (µBS) that utilize high-frequency signals (mmWaves), differentiating them from prior generations [19].…”

Section: A Motivations and Paper Contributionsmentioning

confidence: 99%

Optimal Slicing of mmWave Micro Base Stations for 5G and Beyond

Fayad,

Cinkler

2023

J-NaNA

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5G and beyond 5G mobile networks are expected to cater to diverse needs by efficiently allocating network resources based on demand. Network slicing is a fundamental approach that involves segregating and allocating network resources distinctly to a group of users based on their individual needs, and it is widely recognized as an essential concept that caters to various requirements. Allocating such slices will encounter conflicting requests, and effectively implementing network slicing presents multiple challenges. Effective network slicing necessitates efficient management of priority levels among diverse slices. Network slicing necessitates efficient management of priority levels across various slices, specifically focusing on three distinctive categories: Ultra-Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). This paper proposes an optimization framework utilizing a Mixed Integer Linear Program (MILP) to allocate network resources for multiple slices efficiently. Our framework aims to maximize user satisfaction while ensuring that the specific requirements of each slice are met. We categorize the slices into three priority levels: the URLLC slice holds the highest priority, followed by the eMBB slice, and finally, the mMTC slice receives the least priority. By leveraging our proposed MILP-based approach, we dynamically assign network resources to different slices, considering their priority levels. This allocation strategy enables us to optimize resource utilization and effectively meet the diverse demands of users across various slices. Our framework provides a balance between meeting the stringent requirements of the URLLC slice, delivering high-quality services to the eMBB slice, and accommodating the massive connectivity needs of the mMTC slice.

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“…Their ability to explore a vast solution space and adaptability to different problem structures make them an attractive choice for the task at hand. In the context of 5G and future 6G mmWave networks, the GA can be designed to adaptively allocate users and power among various BSs, ensuring optimal network performance and minimal energy consumption [17].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Joint User and Power Allocation in 5G Millimeter Wave Networks: A Genetic Algorithm-Based Approach

Fayad,

Cinkler

2024

IEEE Access

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Reducing power consumption is a pivotal challenge in 5G millimeter wave (mmWave) networks due to the density of the base stations (BSs) in these networks. In this paper, we focus on the joint user and power allocation problem in 5G mmWave networks, aiming to minimize power consumption while maintaining the user Quality of Service (QoS), considering the BSs switching on/off strategy. Initially, we formulate the problem as an Integer Linear Program (ILP), aiming to obtain the optimal solution. Due to the NP-hardness of the problem, we propose a Genetic Algorithm (GA)-based heuristic strategy. Extensive simulations are conducted to evaluate the performance of the GA. The obtained results demonstrate the efficiency of the proposed GA in providing close to optimal solutions, even in scenarios with a large number of users, when compared to the ILP. Additionally, the proposed GA outperforms the benchmark algorithms in terms of network power consumption, network throughput, energy efficiency, and the number of switched off BSs. Moreover, when comparing the running time of the different methods, the proposed GA shows a significantly reduced time compared to the optimal solution obtained by the ILP method. At the same time, it requires a running time close to the running times of the benchmark solutions. Further insights from power consumption patterns in residential and office areas affirm the consistent energy savings achieved by the proposed GA, emphasizing its applicability in real-world scenarios. In our case, the proposed GA yields energy savings up to 22.34% and 33.12% in residential and office areas, respectively.

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Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Cited by 22 publications

References 123 publications

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

Optimal Slicing of mmWave Micro Base Stations for 5G and Beyond

Energy-Efficient Joint User and Power Allocation in 5G Millimeter Wave Networks: A Genetic Algorithm-Based Approach

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