A review on energy management issues for future 5G and beyond network

Malathy, S.; Jayarajan, P.; Ojukwu, Henry; Qamar, Faizan; Hindia, Mohammad Nour; Dimyati, Kaharudin; Noordin, Kamarul Ariffin; Amiri, Iraj Sadegh

doi:10.1007/s11276-021-02616-z

Cited by 16 publications

(10 citation statements)

References 197 publications

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“…Power control has many benefits, such as reducing power consumption and improving the system throughput [112]. Many parameters must be considered to obtain an optimal power allocation, such as the number of RBs, channel path loss, maximum transmit power, and battery life [113]. The following is a compilation of current research on the schemes of power allocation in D2D networks for different access strategies.…”

Section: A Power Allocationmentioning

confidence: 99%

A Survey of Resource Management in D2D Communication for B5G Networks

et al. 2023

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Due to the vast rise of users, the variety of application requirements, and the enormous necessity for ubiquitous connectivity anytime and anywhere, integration of new technologies and ideas are required. Therefore, Device to Device (D2D) communication technology has been regarded as a critical concern for Beyond Fifth Generation (B5G) networks. In the D2D technology, devices in close proximity may connect without using a Base Station (BS). Consequently, improving the capacity and performance of traditional networks. The major objectives are to accomplish a high geared data rate, increase the network coverage with low latency, and improve Energy Efficiency (EE) and Spectrum Efficiency (SE). However, the coexistence of D2D technology in the traditional network can pose many issues and challenges, such as power control, interference, spectrum access, security, and mode selection. Consequently, a comprehensive survey that includes an investigation of the current issues in D2D technology is required. In this survey, we present a new categorization of the most important aspects of resource management in B5G D2D communication, such as power allocation, spectrum allocation, interference management, and mode selection. Furthermore, we discuss the limitations and open issues of the current techniques and propose a model approach and future directions for D2D communication in B5G wireless networks. Our survey aims to assist researchers in exploring and improving D2D communication within the framework of forthcoming wireless networks.

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Section: A Power Allocationmentioning

confidence: 99%

A Survey of Resource Management in D2D Communication for B5G Networks

et al. 2023

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“…This tremendous increase in mobile data traffic with high data rates raises new challenges in terms of increasing energy efficiency (EE) and providing high-quality services for various traffic types in 5G networks. [1], [2], [3]. Cloud radio access networks (C-RANs), massive multiple input multiple output (MIMO) and heterogeneous networks (HetNets) are three key technologies suggested for 5G that can significantly enhance EE [1], [2], [3].…”

Section: A Motivationsmentioning

confidence: 99%

Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks

2023

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In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femtocell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions.To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30% and increases throughput index by 25% which in turn leads to greater energy efficiency (EE).INDEX TERMS 5G, multi-objective optimization problem, elastic-constraints method.

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“…Reinforcement learning (RL) is one of the methods to alleviate interference in HetNet, and it provides many advantages for solving the best solution problem in integrated methods. This article introduced our vision for 6G wireless deployment solutions, performance indicators, and future cutting-edge technologies [3]. With the beginning of next-generation networks, the use of second-generation (2G) services is decreasing.…”

Section: Figure1 Evolution Of Mobile Technologies From 1g To 6g [11]mentioning

confidence: 99%

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

Bashir

Ali²,

Almasi‐Hashiani³

et al. 2022

Research Journal Of Computer Science And Information Technology

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This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate.

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A review on energy management issues for future 5G and beyond network

Cited by 16 publications

References 197 publications

A Survey of Resource Management in D2D Communication for B5G Networks

A Survey of Resource Management in D2D Communication for B5G Networks

Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

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