Elsevier

Computer Networks

Volume 217, 9 November 2022, 109333
Computer Networks

Energy aware resource control mechanism for improved performance in future green 6G networks

https://doi.org/10.1016/j.comnet.2022.109333Get rights and content

Highlights

  • Energy crisis owing to billions of connected devices is main challenge in 6G networks.

  • Need is to develop energy sustainable network with energy aware resource control.

  • Cell-free networks offers optimum power utilization with AP subsets.

  • MIPA-MCAS algorithm offers 3.39% improvement in SE at 95% of user locations.

  • In the end, comparison with RRPA-MCAS and RPA-MCA algorithms is given.

Abstract

The increasing power consumption and the energy crisis owing to several billion of connected devices raises concern. The battery powered devices or mobile terminals consume a lot of power and led to huge energy overhead. Thus, the need is to design energy sustainable networks which aims for optimum utilization of power resources. 6G enabled networks offer enhanced network coverage along with green communication capabilities and energy-aware resource management. Cell-free Massive MIMO is a promising 6G technology which offers power optimization by allowing only a subset of access points (APs) to serve a particular user. In this paper, the performance of the proposed network is evaluated for different power control methods. The average mean square error (MSE), average signal-to-noise ratio (SNR) and spectral efficiency (SE) are obtained for different users,transmit powers and pilot lengths. An efficient resource management algorithm namely, MIPA-MCAS is proposed which offers Minimum Interference Power Allocation and Maximum Channel gain AP Selection. It is observed that the spectral efficiency obtained at 95% of all user locations improves by 3.39% with the proposed algorithm. In the end, a comparative analysis is carried out with two other algorithms namely, Round-Robin Power Allocation and Maximum Channel gain AP Selection (RRPA-MCAS) and Random Power Allocation and Maximum Channel gain AP Selection (RPA-MCAS) for the proposed and conventional system models.

Introduction

With the rapid proliferation of Internet-of-Things (IoT) devices, sensor nodes, smart devices and to ensure intelligent connection between these communicating nodes, future green wireless networks are envisioned [1]. These networks aim for ubiquitous service capabilities, massive connectivity, low latency, extreme traffic handling ability and ultra-reliable computational capabilities [2], [3], [4]. Green communication enables reduced energy overhead owing to the limited lifespan of these battery powered IoT devices. To support the massive connected devices and the huge traffic demand with reduced energy consumption, sixth generation (6G) technology is emerging promisingly due to limited capabilities of fifth generation (5G) or beyond 5G technology [5], [6]. 6G enabled green wireless networks provide energy efficient and reliable communication between the massive IoT devices and thus help in achieving the sustainable development goals (SDGs) [7], [8], [9]. The promising technologies used in 6G wireless networks to guarantee seamless network coverage and peak data rates include millimeter wave (mmWave), multiple antenna technologies and cell-densification. The use of intelligent reflecting surfaces (IRSs) to enable line-of-sight (LoS) communication is another key technology [10], [11]. MmWave technology suffers from the limitations of signaling overhead, reduced coherence time and shorter wavelengths [12], [13]. The use of multiple antennas in massive multiple-input–multiple-output (mMIMO) though increases the data rate [14] but the increased transmit power and energy overhead are the challenges. The radio-frequency (RF) transceiver chains associated with the antenna elements consume power. Cell densification leading to small cells offers improved data rates but a threshold should be maintained in the density of small cells. Reducing the cell density below a particular level leads to performance errors [15], [16]. The key alternative for the promising 6G technology is the cell-free approach. Cell-free Massive MIMO as the name suggests that no cell boundaries, the users are distributed in a given geographical area and a large number of access points (APs) serve them [17]. The APs are distributed in the coverage area which cooperate among themselves to serve the users through joint coherent transmission and reception. Thus, by removing the cell edges, it overcomes the mediocre cell edge performance in conventional cellular massive MIMO networks [18]. In contrast to conventional cellular networks in which one AP serves all the users, in cell-free network, each user is served by a subset of APs defined by the two approaches, namely network-centric and user-centric. In network centric approach,where network is divided into clusters with few APs in each cluster [19]. This is in contrast to user-centric approach where the user decides which set of APs best connect with it with minimum interference [20], [21]. However, the handover and interference issues at the cluster edges remain unaddressed which are actively taken care in the cell-free user centric approach. In the 6G enabled IoT networks, the use of cell-free massive MIMO has gained popularity as it offers massive connectivity and improved cell-edge performance. Massive MIMO systems though support large number of IoT devices but the high complexity and implementation overhead in real-time environments are the challenges which limit its use. Each distributed AP in the cell free network transmits and receives coherently leading to higher signal-to-noise ratio (SNR). The APs are connected to the cloud–edge processors called central processing units (CPUs) [22]. CPUs gather the information from the APs for centralized encoding and decoding. The literature contains number of papers that evaluate the performance of cell-free massive MIMO networks. Ngo et al. [23] considers the spectral efficiency of the cell-free system model where each user and AP is equipped with single antenna. Using the cell-free approach, the spectral efficiency achieved by 95% of the users has increased to five times. This can be increased further by equipping multiple antennas at each user and AP [24], [25], [26]. The signal processing in cell-free networks are explained in the literature with channel estimation in [27], precoding in [28], beamforming in [29], pilot assignment and receive combining in [30], [31]. Using blind channel estimation, [32] aims to improve the system spectral efficiency and reduce the mean square error. Zhang et al. [33] proposes an dynamic framework based on compute-and-forward for improved sum rate performance of cell-free systems. Ye et al. [34] aims to estimate the channel covariance matrix using pilot assignment and phase shifted pilots for maximum spectral efficiency.

The increasing power consumption and the energy crisis owing to several billion of connected devices raises concern. The battery powered devices or mobile terminals consume a lot of power and led to energy related pollution. Thus, the need is to design energy sustainable networks which aims for optimum utilization of power resources [35]. Cell-free massive MIMO network is advantageous in this regard as it considers energy efficiency optimization and reduces power consumption by allowing only a subset of APs to communicate with a user [36]. This is in contrast to conventional cellular system in which all APs serve all the users. This is further enhanced through optimum power allocation strategies [37]. Chakraborty et al. [38] proposes two power allocation algorithms for cell-free massive MIMO system which aims for high performance and reduced computation time. Yan et al. [27] exploits the channel knowledge to achieve power control in an IoT network. The proposed algorithm reduces the computational time and results in an energy-efficient scalable network. Van Chien et al. [39] allocates optimum transmit powers to the APs which are activated according to the traffic load thereby reducing the power consumption. The AP scheduling and power control are jointly optimized in [40] for the uplink of cell-free network.  Myung et al. [41] proposes a transmit power control mechanism for maximum spectral efficiency in the downlink of cell-free massive MIMO. For the purpose of energy sustainability [42] evaluates the performance of 6G wireless network with the proposed power optimization model. To overcome the total energy overhead, [43] uses the concept of energy harvesting to achieve sustainable 6G Internet-of-Everything (IoE) networks. Clerckx et al. [44] considers wireless power transfer to reduce the battery requirements of mobile devices in future networks. Wireless information and power transfer in the uplink and downlink of cell-free network is considered in [45]. Zhang et al. [46] considers simultaneous wireless information and power transfer (SWIPT) for cell-free networks in [46] for applications in IoT networks. The optimization of energy efficiency in cell-free networks are considered in [47], [48] for efficient power control.

In this paper a 6G enabled green network is proposed which reduces the power consumption and the energy overhead owing to the massive number of battery powered connected devices. Cell-free massive MIMO is a promising technology which offers energy optimization by allowing a subset of APs to serve a particular user. The proposed network is evaluated for spectral efficiency, average SNR and average MSE (see Fig. 1).

The increasing power consumption and energy overhead owing to the large number of connected devices are the main challenges of future wireless networks. To meet the huge energy requirements of these battery limited devices, the need is to design energy sustainable networks which offer green communication through efficient power control. This paper proposes an energy aware 6G enabled cell-free network with improved performance. The novel contributions of the paper are

  • An energy aware cell-free communication model is proposed in which large number of APs are deployed in a given coverage area to serve the users. The power optimization is obtained by allowing a subset of APs to communicate with a given user in contrast to all APs serving a user in conventional system.

  • An algorithm, MIPA-MCAS, is proposed which offers efficient resource management through pilot allocation and AP selection. The parameters considered are minimum interference and maximum channel gains.

  • The proposed system is evaluated for different power control methods for efficient power management. The efficiency of proposed algorithm is also compared with RRPA-MCAS and RPA-MCAS algorithms.

  • The performance of the system is evaluated for average SNR, average MSE and spectral efficiency for different user locations, transmit powers and pilot lengths using signal processing at the transmitting and receiver ends, namely channel estimation and receive combining.

  • In the end, comparative analysis of proposed system is carried out with two other conventional models.

Table 1 lists the summary of notations used throughout the paper.

Section snippets

System model

Consider an ultra dense network in which there are many more access points (APs) than user equipments (UEs). A number of N APs are deployed in a given coverage area to serve a number of K users. A subset of APs serve each user through joint coherent transmission and reception. Each AP has M antennas while each user has single antenna. The APs are deployed above the plane containing the UEs. The selection of APs for each user depends on the user needs. The APs are assumed to be connected to the

Power control

This section presents the power optimization methods and their performance based on the proposed framework. Power control refers to allocation of optimized transmit powers to the users such that the system performance is improved. If a particular user transmits with a transmit power pk which lies between 0 to maximum power pmax, the power control methods suggest ways how this power can be cut down for improved performance. In this paper, an algorithm is proposed which aims to improve the

Proposed algorithm

For the system scenario considered in Section 2, this section proposes an algorithm which aims for allocation of pilots to the users and selection of subsets of APs to serve the users. This algorithm uses two parameters for the same, minimum interference to allocate the pilots and maximum channel gain to select the serving APs. The algorithm is named as Minimum Interference Pilot Allocation-Maximum Channel gain AP Selection (MIPA-MCAS).

Results and discussion

The considered communication scenario is evaluated here for performance through simulations. The simulations are carried out in MATLAB with 104 number of realizations whose results are presented in this section. The simulation setup considers a 1000 m x 1000 m geographical area in which the users are distributed randomly. A large number of APs are deployed in the given network to give service to the users. Other parameters for simulations are tabulated in Table 2. which are access points For

Conclusion

Due to exceptional proliferation of Internet-of-Things (IoT) devices and the communicating nodes, there is increased power consumption leading to huge energy overhead. To support intelligent communication capabilities among these battery powered devices with limited lifespan, the need for sustainable green future networks arises. In this paper, an energy aware cell-free communication model is proposed which offers reduced power consumption and extended network coverage. A resource management

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through the Vice Dean- ship of Scientific Research Chairs: Research Chair of New Emerging Technologies and 5G Networks and Beyond.

Ashu Taneja is Ph.D in Electronics and Communication Engineering with 11 years of experience in academics and industrial research. She is working as Assistant Professor with Chitkara University, Punjab, India. Her research interests include 6G enabled IoT networks, 5G Communication, IRS assisted UAV communication, Co-operative MIMO, Energy Harvesting and Vehicular communications. She has number of research publications and patents to her credit.

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  • Cited by (3)

    Ashu Taneja is Ph.D in Electronics and Communication Engineering with 11 years of experience in academics and industrial research. She is working as Assistant Professor with Chitkara University, Punjab, India. Her research interests include 6G enabled IoT networks, 5G Communication, IRS assisted UAV communication, Co-operative MIMO, Energy Harvesting and Vehicular communications. She has number of research publications and patents to her credit.

    Dr. Shalli Rani (Senior Member, IEEE)is Associate Professor in CSE with Chitkara University (Rajpura (Punjab)), India. She has 15+ years teaching experience. She received MCA degree from Maharishi Dyanand University, Rohtak in 2004 and the M.Tech. degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur in 2007 and Ph.D. degree in Computer Applications from Punjab Technical University, Jalandhar in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks and Internet of Things. She has published/accepted/presented more than 40+ papers in international journals/conferences (SCI+Scopus) and edited/authored five books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters. She is serving as a guest editor in IEEE Transaction on Industrial Informatics and Elsevier IoT Journals. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley She has worked on Big Data, Underwater Acoustic Sensors and IoT to show the importance of WSN in IoT applications. She received a young scientist award in Feb. 2014 from Punjab Science Congress, Lifetime Achievement Award and Supervisor of the year award from Global Innovation and Excellence, 2021.

    Sahil Garg received the Ph.D. degree from the Thapar Institute of Engineering and Technology, Patiala, India, in 2018. He is currently a Research Associate at Resilient Machine learning Institute (ReMI) in correlation with École de technologie supérieure (ÉTS), Montréal. Prior to this, he worked as a Postdoctoral Research Fellow at ÉTS, Montreal and MITACS Researcher at Ericsson, Montreal. He has many research contributions in the area of Machine Learning, Big Data Analytics, Knowledge Discovery, Cloud Computing, Internet of Things, Software Defined Networking, and Vehicular Ad-hoc Networks. He has over 80 publications in high ranked Journals and Conferences, including 50+ top-tier journal papers and 30+ reputed conference articles. He is currently a Managing Editor of Springer’s Human-centric Computing and Information Sciences (HCIS) journal; and an Associate Editor of IEEE Network Magazine, IEEE Transactions on Intelligent Transportation Systems, Elsevier’s Applied Soft Computing (ASoC), and Wiley’s International Journal of Communication Systems (IJCS). In addition, he also serves as the Workshops and Symposia Officer for the IEEE ComSoc ETI on Aerial Communications. He guest edited a number of special issues in top-cited journals including IEEE T-ITS, IEEE TII, IEEE IoT Journal, IEEE Network Magazine, FGCS, NCAA, etc. He also served as the TPC Co-Chair/Publicity Co-chair/Special Sessions Chair/Publication Chair for several conferences. He also served as the workshop co-chair for different workshops in IEEE/ACM conferences including IEEE Infocom, IEEE Globecom, ACM MobiCom, etc. He is a member of IEEE, IEEE Communications Society, IEEE Industrial Electronics Society, IEEE Software Defined Networks Community, IEEE Smart Grid Community, ACM, and IAENG.

    Mohammad Mehedi Hassan [SM’18] received his Ph.D. degree in computer engineering from Kyung Hee University, Seoul, South Korea, in February 2011. He is currently a professor with the Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. He has authored and coauthored around 210+ publications including refereed IEEE/ACM/Springer/Elsevier journals conference papers, books, and book chapters. His research interests include edge/cloud computing, the Internet of Things, cyber security, deep learning, artificial intelligence, body sensor networks, 5G networks, and social network.

    Salman Alqahtani is currently a Professor in the Department of Computer Engineering, King Saud University, Riyadh. His current research interests are in the area of 5G networks, broadband wireless communications, radio resource management for 4G and beyond networks (call admission control, packet scheduling and radio resource sharing techniques), cognitive and cooperative wireless networking, small cell and heterogeneous networks, self-organizing networks, SDN/NFV, 5G network slicing, smart grid, intelligent IoT solutions for smart cities, dynamic spectrum access, co-existence issues on heterogeneous networks in 5G, industry 4.0 issues, Internet of Everything, mobile edge and fog computing, and Cyber sovereignty. In addition, his interests also include performance evaluation and analysis of high-speed packet switched networks, system model and simulations and integration of heterogeneous wireless networks. Mainly his focus is on the design and optimization of 5G MAC layers, closed-form mathematical performance analysis, energy-efficiency, and resource allocation and sharing strategies. He has authored two scientific books and authored/co-authored around 76 journal and conference papers in the topic of his research interests since 2004. He serves as a reviewer for several national and international journals.

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