Energy-efficient scheduling of small cells in 5G: A meta-heuristic approach

doi:10.1016/j.jnca.2021.102986

Journal of Network and Computer Applications

Volume 178, 15 March 2021, 102986

https://doi.org/10.1016/j.jnca.2021.102986 Get rights and content

Abstract

Scheduling of small cells in Fifth-Generation (5G) mobile network is highly important for achieving energy-efficiency and providing Quality of Service (QoS) to the applications users. Minimization of energy consumption hampers QoS. This problem has been further complicated due to exponential increase of mobile application users demanding high data rate. The performances of energy-saving approaches in the literature are limited by the fact that they exploit mere historical data-driven two state operation modes of small cells. This paper formulates the problem of scheduling small cells as a non-linear optimization problem. It then offers a meta-heuristic evolutionary algorithm to solve the problem in polynomial time. The proposed algorithm takes into account four operation states of small cells to minimize the energy consumption while satisfying the users’ QoS. The results of our performance analysis depict that the proposed algorithm outperforms the state-of-the-art works in terms of energy-saving, switching delay, etc.

Introduction

With the increase of base station deployment density, reduction of energy consumption becomes one of the key challenges in mobile network and communication technology. According to researchers, around 4.7% of the world electricity production is used by the Information and Communication Technology (ICT) sector (Gelenbe, Caseau) and 80–90% of this power is consumed by network base stations (BSs) and around 10% is consumed by user equipments (UE) (Schaefer et al., 2003; Son et al., 2012; Chien et al., 2019; Afrin et al., 2017). In addition, communication technology has a great impact on global climate change because it causes approximately 2% of the world's total CO₂ emission (Gelenbe, Caseau) (Fang et al., 2020; Fu et al., 2020). Moreover, a recent study states that high data rate demand in 5G network requires high connection density (up to 1 Million connections per km²) which drives the network operators to deploy large number of small cells (Jiang and Liu, 2017). Another study (Cisco Visual Networking I, 2017) performed by Cisco Visual Networking Index reveals that the monthly global mobile data traffic will be 77 exabytes by 2022 which will require large deployment of small cells or access points. In consequence, energy consumption by the small cells as well as the amount of CO₂ emission will increase by a huge margin over the next few years (Casadei et al., 2019; Zhang et al., 2018; Aloi et al., 2017).

From the environmental perspective, considering the harmful effects of CO₂ emissions and non-renewable energy, eco-friendly system models are needed in the information and communication technology as required in other sectors. And the economical aspects is also needed to be kept in consideration as global electricity cost for the cellular networks has already increased up to $22 billion (Lu, 2018). To meet the challenge of high data demand and reduction of power consumption, small cell networks (SCNs) are being explored as one of the key technologies as it enables signal transmission over the millimeter-wave spectrum. The goal of SCN technology is to strengthen the network signal and improve the data transfer rate in a small area. The coverage area of a small cell is typically ranging from 10m to 2km (Guo et al., 2017). Moreover, different types of methodologies have been addressed in recent literature. Explicitly the works in literature can be divided into two categories-designing efficient hardware components and developing energy-efficient scheduling algorithm for small cells.

In Brubaker (2009), authors have focused on efficient hardware component Power Amplifier (PA) to increase energy-efficiency, while the concept of energy-efficient transmission infrastructure is demonstrated in Yang et al. (2010). In Rizvi et al. (2017), authors have proposed heterogeneous network architecture for 5G using different kinds of small cells (e.g., micro/pico/femto cells) to fulfil users high data demand while energy consumption can be reduced using these less energy-hungry small cells instead of BSs.

On the other hand, scheduling methods of small cells based on the dynamic traffic load behaviour on different periods are addressed in Mowla et al. (2017), Mwanje and Ali-Tolppa (2018), Oh et al. (2013). For observing this aforementioned load behaviour, we consider a scenario in our University of Dhaka area for observing traffic load pattern which shows that load varies in different areas over different time periods and this characteristic is usable for energy efficiency. For example, in office hours, network traffic becomes high in a commercial area, whereas at that moment traffic becomes low in the residential area. On the contrary, at non-office hour network load pattern changes in reverse way. Fig. 1 shows the load distribution of the aforementioned area. From Fig. 1(a), it is clear that at time duration 10AM to 5PM, i.e., at working hours users are almost uniformly distributed in both residential and academic areas. On the other hand, at time duration 10PM to 6AM user load is non-uniformly distributed as the number of users in residential area is much higher than that of academic area as shown in Fig. 1(b). In addition, traffic load varies for each zone in holidays and working days. Network load on working days in an industrial zone is very much higher compared to holidays and vice versa. From the above mentioned discussion, it can be concluded that network traffic load is region and time dependant. Considering this traffic changing behavior, authors in Mowla et al. (2017) have introduced an approach of scheduling the small cells using active and sleep states based on the historical load data, whereas ON/OFF switching model for small cells is introduced in Mwanje and Ali-Tolppa (2018) and Oh et al. (2013). Though these two states switching approaches are introduced to mitigate the challenge of energy consumption, but state switching using two states is time consuming and also cells in sleep mode consumes significant amount of power which causes high total power consumption. The aforementioned analysis leads us to develop a network cells scheduling method using two additional states that only keeps required numbers of small cells in ON state depending on the load dynamics.

In this work, we considered two additional working states- Waiting (WA) and Deep Sleep (DS) (to be explored in Section 3.3) for small cells scheduling. The concentration of this work is on reducing the energy consumption and switching power of small cells that causes overall heavy energy consumption, reducing the switching delay of small cells which affects QoS immensely. In this paper, we have focused on designing a scheduling model for the ultra dense multi-tiered 5G Heterogeneous Network to find the minimum number of required small cells those are needed to be kept active to serve the users demand.

Our work extended the contribution in the field on the following specific areas:

•
The problem of optimizing the number of activated small cells in a 5G network while satisfying the users minimum required services has been formulated as multi-objective non-linear optimization problem.
•
Due to NP-hardness of the aforementioned optimal solution, we then design a meta-heuristic evolutionary algorithm for scheduling the small cells to achieve energy-efficiency (EE).
•
Our performance analysis depicts that the proposed system outperforms existing methods by increasing the EE around 10% and by reducing the switching delay up to 25 s.

The rest of the paper is organized as follows. Section 2 reviews the related literature and research contributions. Section 3 represents the system model for 5G HetNet. In section 4, Optimal Scheduling of SCNs is presented and section 5 unfolds the Meta-heuristic Scheduling of small cells. Performance results and discussions are presented in section 6. Finally, we draw the conclusion and illustrate our future research direction in section 7.

Section snippets

Related works

According to (Cisco Visual Networking I, 2017), data demand is increasing rapidly which requires a different network architecture rather than the existing one. And further this higher data demand poses a threat to the environment in addition to increasing energy consumption. Thus, the several recent studies have introduced different solutions based on efficient hardware components and small cells scheduling. Some works focused on efficient hardware (Brubaker, 2009; Yang et al., 2010; Tabassum

5G HetNet network model

In the following subsections, we demonstrate the HetNet architecture of 5G, Power consumption and Working states of small cells.

Optimal Scheduling of 5G small cells

The main objective of this work is to minimize the total power consumption while maintaining the minimum required quality of service for the users. The main difficulties behind achieving this goal are dynamic traffic load demands from users and heterogeneous coverage areas of small cells. In order to attain this goal, we have designed an optimization model.

To determine the availability of cell capacity, we calculated the demand D_c,t of each cell $c \in C$ at time instant t as, $D_{c, t} = \sum_{u \in U} d_{u} \cdot α_{u, t}^{c},$ where,

Meta-heuristic scheduling of small cells

Since the proposed optimization model presented in previous section is intractable for a large network, we propose a meta-heuristic solution based on Genetic Algorithm. Genetic algorithm (GA) is a stochastic search algorithm for solving non-linear problems, inspired by biological genetic mechanism. The following subsections unfold the steps of the proposed GA based solution for the scheduling problem.

Performance evaluation

In this section, we implement the proposed MHGA (Meta-heuristic Genetic Algorithm) system using Python 3.6.8 (Python. https://docs.pyth, 2018) and compare the obtained results with those of some existing models: All-ON (Rizvi et al., 2017), Sleep-Awake (Mowla et al., 2017) and ON-OFF (Oh et al., 2013). In implementing All-ON (Rizvi et al., 2017), no state changes are occurred, i.e., all the small cells are kept in ON state during the whole simulation period. Similarly, in implementing Sleep-

Conclusion and future work

In this work, we designed an optimization model to schedule the small cells using four states so as to minimize the power consumption. We carried out theoretical Proof of NP-hardness of the optimal solution. The proposed meta-heuristic scheduling algorithm minimized energy consumption as well as reduced switching delay while maintaining user QoS. We simulated the proposed model and the obtained results depict that our proposed model outperforms the existing method by reducing the number of

Credit author statement

Md Shahin Alom Shuvo: Conceptualization, Methodology, Investigation, Software. Md. Azad Rahaman Munna: Software, Writing- Reviewing and Editing. Sujan Sarker: Data curation, Writing- Reviewing and Editing. Tamal Adhikary: Writing- Reviewing and Editing. Md. Abdur Razzaque: Supervision, Validation, Writing- Reviewing and Editing. Mohammad Mehedi Hassan: Supervision, Writing- Reviewing and Editing. Gianluca Aloi.: Data curation, Writing- Reviewing and Editing. Giancarlo Fortino: Writing-

Declaration of competing interest

No.

Acknowledgment

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. Mohammad Mehedi Hassan is the corresponding author of this paper.

Md Shahin Alom Shuvo is currently a student of Department of Computer Science and Engineering, University of Dhaka, where he is a member of Green Networking Research Group. His current research interests include wireless sensor networks, IoT and mobile cloud. Email: [email protected]

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Md. Azad Rahaman Munna is currently a student of Department of Computer Science and Engineering, University of Dhaka, where he is a member of Green Networking Research Group. His current research interests include wireless sensor networks, IoT and fog computing. Email: [email protected]

Sujan Sarker received the B.S. and M.S. degrees from the Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh, in 2013 and 2017, respectively. He is currently a Lecturer with the Department of Robotics and Mechatronics Engineering, University of Dhaka, where he is a member of Green Networking Research Group. His current research interests include sensor networks, participatory sensing, mobile crowdsourcing, human robot interaction, and swarm robotics. Mr. Sarker is a member of the IEEE Computer Society and the IEEE Robotics and Automation Society. Email: [email protected]

Tamal Adhikary received his BS and MS from the Department of Computer Science and Engineering, University of Dhaka, Bangladesh in 2013 and 2015, respectively. He is now working as a lecturer in the Department of Computer Science and Engineering, University of Dhaka, Bangladesh. He is one of the instructors of Green Networking Research Group (http://www.cse.du.ac.bd/research-group-details/?group_id=40) of the same department. His research interests include Cloud Computing, Mobile Cloud Computing, Green Cellular Network, Vehicular Cloud, Pervasive Healthcare Systems, etc.

Md. Abdur Razzaque received his B.S. and M.S. degrees from the University of Dhaka, Bangladesh in 1997 and 1999, respectively. He obtained his PhD degree in Wireless Networking from the Department of the Computer Engineering, School of Electronics and Information, Kyung Hee University, South Korea in 2009. He was a research professor, in the same university during 2010–2011. He is now working as a Professor in the Department of Computer Science and Engineering, University of Dhaka, Bangladesh. He is the group leader of Green Networking Research Group (http://gnr.cse.univdhaka.edu) of the same department. His research interest is in the area of modeling, analysis and optimization of wireless networking protocols and architectures, Wireless Sensor Networks, Body Sensor Networks, Cooperative Communications, Sensor Data Clouds, Internet of Things, Cognitive Radio Networks, etc. He has published a good number of research papers in international conferences and journals. He is an editorial board member of International Journal of Distributed Sensor Networks, TPC member of IEEE HPCC 2013–2015, ICOIN 2010–2015, ADM 2014, NSysS 2015. He is a senior member of IEEE, member of IEEE Communications Society, IEEE Computer Society, Internet Society (ISOC), Pacific Telecommunications Council (PTC) and KIPS.

Mohammad Mehedi Hassan is currently a Full Professor of Information Systems Department in the College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Kingdom of Saudi Arabia. He received his Ph.D. degree in Computer Engineering from Kyung Hee University, South Korea in February 2011. He has authored and coauthored around 180+ publications including refereed IEEE/ACM/Springer/Elsevier journals, conference papers, books, and book chapters. Recently, his 4 publications have been recognized as the ESI Highly Cited Papers. He has served as chair, and Technical Program Committee member in numerous reputed international conferences/workshops such as IEEE CCNC, ACM BodyNets, IEEE HPCC etc. He is a recipient of a number of awards including Best Journal Paper Award from IEEE Systems Journal in 2018, Best Paper Award from CloudComp in 2014 conference, and the Excellence in Research Award from King Saud University (2 times in row, 2015 & 2016). His research interests include Cloud computing, Edge computing, Internet of things, Body sensor network, Big data, Deep learning, Mobile cloud, Smart computing, Wireless sensor network, 5G network, and social network. He is a Senior Member of the IEEE.

Gianluca Aloi received his Ph.D. degree in Systems Engineering and Computer Science from the University of Calabria in 2003. In 2004, he joined the University of Calabria, where he is currently an Assistant Professor in Telecommunications with the Department of Informatics, Modeling, Electronics and System Engineering (DIMES). Since 2010, he is the director of TITAN Laboratory where he conducts his research on topics including spontaneous and reconfigurable wireless networks, cognitive and opportunistic networks, sensor and self-organizing wireless networks, 5G networks and Internet of Things technologies.

Giancarlo Fortino is Full Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer Engineering from Unical in 2000. He is also guest professor at Wuhan University of Technology (Wuhan, China), high-end expert at HUST (China), and senior research fellow at the Italian National Research Council ICAR Institute. He is the director of the SPEME lab at Unical as well as co-chair of Joint labs on IoT established between Unical and WUT and SMU Chinese universities, respectively. His research interests include agent-based computing, wireless (body) sensor networks, and Internet of Things. He is author of over 400 papers in int'l journals, conferences and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of many int'l journals such as IEEE TAC, IEEE THMS, IEEE IoTJ, IEEE SJ, IEEE SMCM, Information Fusion, JNCA, EAAI, etc. He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems. Fortino is currently member of the IEEE SMCS BoG and of the IEEE Press BoG, and chair of the IEEE SMCS Italian Chapter.

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