Energy efficient transmission trends towards future green cognitive radio networks (5G): Progress, taxonomy and open challenges

Srivastava, Akanksha; Gupta, Mani Shekhar; Kaur, Gurjit

doi:10.1016/j.jnca.2020.102760

Cited by 66 publications

(33 citation statements)

References 86 publications

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“…1. Survey works in the area of the spectrum sensing in CRN were mainly focused on the pros and cons of associated with the conventional spectrum sensing tech- [22] nique, along with their brief description [33,35,80,89,150]. Through this survey work, attempt is made on the complete exploration of the soft computing techniques for the spectrum sensing in a CRN.…”

Section: Motivational Backgroundmentioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

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Section: Motivational Backgroundmentioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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“…The next-generation 5G networks promises to deliver an attractive deal with energy challenges, enhanced quality of service and wide range of potential solutions [1]. According to [2], the internet subscribers will be uplifted to 59.7% globally by 2022 from 44.3% in 2018 and cellular data traffic will be three times from 2018 to 2021, about 49 exabytes increasing data demand. In addition, 8 billion mobile users are expected to increase by 2030 and over 10% global 5G mobile devices will be connected by 2023, which put an immense pressure to the existing networks and conventional utility grid as well [3].…”

Section: Introductionmentioning

confidence: 99%

“…The price of grid electricity increased dramatically because the power generating station depends on fossil fuels which is another key disadvantages apart from atmospheric pollution. However, the electrical energy expenditure by the ICT industry will be raised from 611 TWh to 1852 TWh by 2030 [2]. Therefore, the tremendous rate of energy consumption to tackle the unprecedented growth of mobile traffic in wireless communications is a paramount issue in the near future if no proper steps are taken to address the existing trend.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks

et al. 2021

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With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of internet access and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing on achievable throughput and energy efficiency by reducing utility grid consumption from a network level perspective. This paper investigate the radio efficiency, energy efficiency (EE) and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of networks setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination in a cluster. Provision of cell sleep approach is contemplated for energy saving by turning off lightly base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enable adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterwards, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.INDEX TERMS Energy efficiency, coordinated multipoint, hybrid power supply, centralized RAN, load balancing, cell zooming, green communication.

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“…Recently, the green communication in wireless networks, especially in the upcoming 5G networks, has become an urgent need [38][39][40][41][42]. Unfortunately, despite its complete spectrum efficiency studies, the energy-efficient architectures in NOMA systems have not yet been well developed.…”

Section: Introductionmentioning

confidence: 99%

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

et al. 2021

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Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

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Energy efficient transmission trends towards future green cognitive radio networks (5G): Progress, taxonomy and open challenges

Cited by 66 publications

References 86 publications

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

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