A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Alhashimi, Hayder Faeq; Hindia, Mohammad Nour; Dimyati, Kaharudin; Hanafi, Effariza; Safie, Nurhizam; Qamar, Faizan; Azrin, Khairul; Nguyễn, Quang Ngọc

doi:10.3390/electronics12030647

Cited by 35 publications

(10 citation statements)

References 161 publications

(196 reference statements)

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“…In Fig. 7, the average data rate is demonstrated for various number of channels (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The proposed method clearly achieves a higher average data rate in the proposed scenario S2 when compared to proposed scenario S1, proposed scenario S3, and benchmark algorithms.…”

Section: Table I Simulation Parametersmentioning

confidence: 98%

“…Additionally, the gNB must choose the optimal D2D mode in order to maximize the data rate [12]. In terms of channel allocation, D2D communication may use either the licensed spectrum or the unlicensed spectrum to establish direct links, often known as the inband and the outband D2D communications [13], [14] respectively. There are two classifications for inband D2D communication: underlay and overlay.…”

Section: Introductionmentioning

confidence: 99%

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Joint Mode Selection and Resource Allocation for Underlaying D2D Communications: Matching Theory

ATTAR,

MAHYUDDIN,

Hindia

2024

Preprint

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Device-to-Device (D2D) communications, regarded as a crucial technology for the Beyond Fifth-Generation (B5G) wireless networks, provide substantial benefits including elevated spectrum effectiveness, improved coverage, and traffic offloading. The mode selection and channel allocation play an Important role in ensuring the Quality of Service (QoS) and enhancing user experience in D2D communications. The dynamic switching of communication modes by D2D User Equipments (UEs) depends on the extent of shared resources among D2D pairs. In this paper, we examine the issue of joint mode selection and resource allocation for D2D communication within a cellular network in an uplink scenario. The main objective of this study is to optimize the overall sum rates of the network, while simultaneously guaranteeing the QoS requirements. Three communication modes are considered which are Direct Mode (MD), Relay-assisted Mode (RM), and Local route Mode (LM). In addition, each D2D pair has the option to be allocated either a dedicated channel or a reused channel. We present an innovative approach for the simultaneous determination of mode selection and channel allocation in D2D communication. The proposed approach is based on greedy strategy and a modified many-to-many matching technique that effectively selects the best communication mode and assigns the optimal channel for each D2D pair, respectively. The simulation results illustrate that the presented approach exhibits a significant improvement in the network performance when compared to the benchmark algorithms.

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Section: Table I Simulation Parametersmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Joint Mode Selection and Resource Allocation for Underlaying D2D Communications: Matching Theory

ATTAR,

MAHYUDDIN,

Hindia

2024

Preprint

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“…Alhashimi et al suggested combining training and optimizing methods to solve mixedinteger concave programming problems in satellite RRM at satellite communications [7]. The intricate issue is divided into two categorization jobs and a power control challenge.…”

Section: Related Workmentioning

confidence: 99%

Machine learning-inspired intelligent optimization for smart radio resource management in satellite communication networks to improve quality of service

Devika,

Sashidhar Reddy,

Parasa

et al. 2024

MATEC Web Conf.

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Satellite communication networks are seeing a significant surge in traffic requirements. Nevertheless, the rise in traffic requirements is inconsistent across the service region because of the unequal distribution of consumers and fluctuations in traffic requirements during the day. Variable payload designs solve this issue by enabling the uneven allocation of payload resources to match the traffic requirement of each beam. Optimization-based Radio Resource Management (ORRM) has its high substantial efficiency demonstrated computational difficulty hinders its real-world deployment. This work explores the structure, execution, and uses of Machine Learning (ML) for resource allocation in satellite systems. The primary emphasis is on two systems: one that offers power, capacity, and beamwidth adaptability and provides temporal flexibility via beam hopping. The research examines and contrasts several ML methods suggested for these structures. The research determines whether training must be done online or offline depending on the features and needs of each ML method. The study analyzes the most suitable system structure and the pros and cons of each strategy.

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“…Furthermore, resource optimization is a crucial question that should not be left in developing the 6G IoT system. [9]. The existing resource optimization technologies cannot match the QoS demands in 6G IoT networks [10].…”

Section: Introductionmentioning

confidence: 99%

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

Liwen,

Qamar,

Liaqat

et al. 2024

IEEE Access

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The next generation (6G) wireless communication technology has super advantages in high transmission rates scenarios. Internet of Things (IoT) has been applied in recent years due to its wide connection. However, effective resource optimization methods must be analyzed to meet the high requirements of key performance indicators in 6G wireless IoT networks. This paper discusses a general investigation of the resource optimization strategies in the 6G IoT system. The study aims to find the main solutions to optimize 6G IoT network performance. First, an overall summary of current research is preferred resource optimization in latency, reliability, Energy Efficiency (EE), Spectrum Efficiency (SE), bandwidth utilization efficiency, rate, and power efficiency. Second, we propose the multi-indicator tradeoff strategies associated with the latest resource optimization approaches and investigate optimal strategies. Furthermore, we show the limitations of the current resource optimization methods and discuss the future works of resource optimization for IoT devices in 6G communication. Our survey aims to help researchers optimize 6G IoT network performance using advanced techniques.

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A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Cited by 35 publications

References 161 publications

Joint Mode Selection and Resource Allocation for Underlaying D2D Communications: Matching Theory

Joint Mode Selection and Resource Allocation for Underlaying D2D Communications: Matching Theory

Machine learning-inspired intelligent optimization for smart radio resource management in satellite communication networks to improve quality of service

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

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