Resource Allocation Schemes for 5G Network: A Systematic Review
Abstract
:1. Introduction
2. Background
3. Related Work
4. Design of Research
4.1. Research Questions
- What are the existing state-of-the-art challenges in 5G?
- What is the importance of resource allocation in 5G?
- Which current policies, strategies, and algorithms are being used for resource allocation in 5G?
- Which metrics and parameters are considered during resource allocation in 5G?
- Which open issues and research trends are unaddressed in resource allocation in 5G?
4.2. Search Criteria
4.3. Data Sources
4.4. Article Selection Process
4.5. Inclusion and Exclusion Criteria
5. Discussion
5.1. Q1. What Are the Existing State-of-the-Art Challenges in 5G?
- Deployment of MIMO: 5G will require a paradigm shift that incorporates a huge bandwidth having very high-frequency spectra as a carrier, excessive densities of a base station, and a remarkable number of antennas to provide for the massive growth on the behalf of the increased amount of traffic.
- mm-Wave: Millimeter waves are transmitted with frequencies between 30 and 300 GHz, compared with the bands traditionally used for mobile devices, which are below 6GHz. This technology guarantees huge data capacity as compared with the one that is currently being used. However, mm-waves face one main drawback—i.e., traditionally, a higher range of frequencies is not sufficient for outdoor applications due to blockage and high propagation loss caused by rain and tall buildings [70].
- Pilot contamination and channel estimation/feedback: Channel State Information (CSI) is critical for attaining the benefits of multi-antenna in MIMO systems. CSI has become more demanding in massive MIMO systems because of the massive number of antennas. Furthermore, a massive MIMO system needs a massive number of pilots for both times-division duplexing (TDD) and frequency-division duplexing (FDD) [71].
- The trade-off between computation power and transmission power: Across the 5G network, an additional BS’s power relies on the transmission and computation power of the BSs. When extra power is added to BSs and combined with the transmission and computation power of the additional BS, then the 5G network’s energy efficiency is also calculated by the BSs’ transmission and computation energy [72].
- Mobility: 5G networks require work with speed up to 1000 km/h [73,74]. A substantial investigation is needed to uncover the issues related to the selection of optimum beam and the development of methods/schemes that enhance the requirement for the response for CSI to the transmitter. Thus, massive MIMO performance is delicate with regard to speed because this the computational load can make multiuser solutions unaffordable [41].
- Mixed-Numerology interference: As per the divergent demands of mMTC and URLLC, service configuration contrasts vigorously from the perspectives of the physical layer [75]. Specifically, mMTC is characterized by a sampling rate having a low baseband for supporting huge connectivity and by a small sub-carrier spacing with narrowband transmission, reduced consumption of power, and extensive coverage having low-cost. On the contrary, URLLC mostly requires spacing for many subcarriers to deal with the rigorous requirement for latency and sampling rate for high baseband. These diverse configuration discrepancies in RF and baseband predictably lead to considerable interference [76,77] in crucial mMTC.
- 5G UE’s testing challenges: The problems that appeared in testing 5G UE are like the issues that happened in traditional systems having power control, extreme power output, and sensitivity behavior of receiver as measurement matrices. Therefore, the demand for using SC-FDMA for the uplink and an OFDMA scheme in the downlink in LTE-A/LTE-B based 5G systems, along with assistance for instantaneous links having harvesting capabilities for energy provisioning, need novel ideas of measurement for supporting required trials. For suitable RF measurements, trial equipment must automatically consider operating signaling protocols that utilize parameters defined by the user (such as a channel number). The UE operational testing should integrate the signaling protocol, handover testing, and end-to-end throughput. The main challenge faced in 5G UE testing is to guarantee that the response of state-change requirements is met [78].
- Dynamic heterogeneous resource optimization: It is difficult to endorse data transmission efficiency for the services getting URLLC as a top priority in mMTC. Due to the lack of radio resources, it is mandatory to consider their co-presence by combining their conflicting requirements and specifications concerning latency, reliability, density, and bandwidth. Therefore, the efficient arrangement of the resources in the wireless environment using dynamic and intelligent ways across various stages of service requirements is a demanding job [79].
- Efficient and realistic measurement: As data measurement is critical for the required modification/extension of current transmission models, the approach to measurement should cover various ranges of frequencies, spherical waves, 3D (elevation), and spatial consistency, along with new paradigms of communication, such as small cell and M2M/D2D communications. Furthermore, measurements must be captured for mm-wave (i.e., 60 GHz and over) for outdoor and indoor criteria, and they must feasibly apply to real-life scenarios (such as vehicle-to-vehicle/roadside communication, crowded areas, etc.) [80].
- Isolation among Network Slices: In a 5G network, many services have unique requirements. Consequently, the resources of a dedicated virtual network are required to certify the quality of service at every slice. A network needs high-performance slices isolated from each other. Through control plane and data plane isolation this isolation of network slices can be achieved. Generally, the slice control function can be distributed between various slices, whereas in some of the services, such as mission-critical communications, the resource sharing provides various benefits for infrastructure benefactors while it brings some challenging issues such as slice isolation. Network slices require control functionality. Moreover, the effective isolation of each network slice confirms that a security attack or any other failure does not alter another slice’s operation. Therefore, the mechanism of slice isolation is a predominant challenge while employing network slicing [81].
- Privacy protection: Obscurity services of 5G demands much more attention when compared with the previous cellular networks. The exclusive data rate of 5G carries a huge amount of data flow that contains private and sensitive information such as identity, private content, and position. In certain situations, the breach of privacy may lead to extreme consequences. For example, unintentional release of personal health data may expose the private information of a person, while the release of routing data for a vehicle may reveal its position to unauthorized others [82]. Due to the an application’s privacy requirements, the protection of privacy is a challenging issue faced by 5G wireless networks.
- Coordinated multiple points (CoMP): CoMP having 5G massive MIMO will play a critical part in enhancing the quality of communication, coverage, EE, and throughput of the network [83]. Moreover, mobile users can use relatively higher quality and better performance when located in another cell zone. Therefore, the CoMP system having 5G massive MIMO still has some open challenges—such as backhauling, processing, and cooperative framework—that demand more attention and study, in turn, to achieve maximum benefits of the network for the operator while keeping the cost in control.
5.2. Q2. What Is the Importance of Resource Allocation in 5G?
5.3. Q3. Which Current Policies, Strategies, and Algorithms Are Being Used for Resource Allocation in 5G?
5.4. Q4. Which Metrics and Parameters Are Considered during Resource Allocation in 5G?
5.5. Q5. Which Open Issues and Research Trends Were Unaddressed in Resource Allocation in 5G?
6. Open Research Issues and Trends in 5G
6.1. Joint Resource Allocation Techniques
6.2. Fronthaul/Backhaul/C-RAN Issues
6.3. Minimization of Latency
6.4. Energy Efficiency
6.5. Network Scalability
6.6. Mobility Management
6.7. Management of Services
6.8. Network Virtualization
6.9. Appropriateness in Practical Situations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Ejaz, W.; Sharma, S.K.; Saadat, S.; Naeem, M.; Anpalagan, A.; Chughtai, N.A. A comprehensive survey on resource allocation for CRAN in 5G and beyond networks. J. Netw. Comput. Appl. 2020, 160, 102638. [Google Scholar] [CrossRef]
- Wei, X.; Kan, Z.; Sherman, X. 5G Mobile Communications; Springer: Cham, Switzerland, 2017; ISBN 9783319342061. [Google Scholar]
- Yu, H.; Lee, H.; Jeon, H. What is 5G? Emerging 5G mobile services and network requirements. Sustainability 2017, 9, 1848. [Google Scholar] [CrossRef] [Green Version]
- Fernández-Caramés, T.M.; Fraga-Lamas, P.; Suárez-Albela, M.; Vilar-Montesinos, M. A fog computing and cloudlet based augmented reality system for the industry 4.0 shipyard. Sensors 2018, 18, 1798. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chin, W.H.; Fan, Z.; Haines, R. Emerging technologies and research challenges for 5G wireless networks. IEEE Wirel. Commun. 2014, 21, 106–112. [Google Scholar] [CrossRef] [Green Version]
- Abad-Segura, E.; González-Zamar, M.D.; Infante-Moro, J.C.; García, G.R. Sustainable management of digital transformation in higher education: Global research trends. Sustainability 2020, 12, 2107. [Google Scholar] [CrossRef] [Green Version]
- Institute of Business Management; Institute of Electrical and Electronics Engineers Karachi Section; Institute of Electrical and Electronics Engineers. MACS-13. In Proceedings of the 13th International Conference Mathematics, Actuarial Science, Computer Science & Statistics, Karachi, Pakistan, 14–15 December 2019. [Google Scholar]
- Nalepa, G.J.; Kutt, K.; Zycka, B.G.; Jemioło, P.; Bobek, S. Analysis and use of the emotional context with wearable devices for games and intelligent assistants. Sensors 2019, 19, 2509. [Google Scholar] [CrossRef] [Green Version]
- Liu, G.; Jiang, D. 5G: Vision and Requirements for Mobile Communication System towards Year 2020. Chin. J. Eng. 2016, 2016, 8. [Google Scholar] [CrossRef] [Green Version]
- Rappaport, T.S.; Gutierrez, F.; Ben-Dor, E.; Murdock, J.N.; Qiao, Y.; Tamir, J.I. Broadband millimeter-wave propagation measurements and models using adaptive-beam antennas for outdoor Urban cellular communications. IEEE Trans. Antennas Propag. 2013, 61, 1850–1859. [Google Scholar] [CrossRef]
- Boccardi, F.; Heath, R.; Lozano, A.; Marzetta, T.L.; Popovski, P. Five disruptive technology directions for 5G. IEEE Commun. Mag. 2014, 52, 74–80. [Google Scholar] [CrossRef] [Green Version]
- Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutorials 2015, 17, 2347–2376. [Google Scholar] [CrossRef]
- Marques, G.; Pitarma, R.; Garcia, N.M.; Pombo, N. Internet of things architectures, technologies, applications, challenges, and future directions for enhanced living environments and healthcare systems: A review. Electronics 2019, 8, 1081. [Google Scholar] [CrossRef] [Green Version]
- Belgaum, M.R.; Musa, S.; Alam, M.M.; Su’Ud, M.M. A Systematic Review of Load Balancing Techniques in Software-Defined Networking. IEEE Access 2020, 8, 98612–98636. [Google Scholar] [CrossRef]
- Asadi, A.; Wang, Q.; Mancuso, V. A survey on device-to-device communication in cellular networks. IEEE Commun. Surv. Tutorials 2014, 16, 1801–1819. [Google Scholar] [CrossRef] [Green Version]
- Karagiannis, G.; Altintas, O.; Ekici, E.; Heijenk, G.; Jarupan, B.; Lin, K.; Weil, T. Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Commun. Surv. Tutorials 2011, 13, 584–616. [Google Scholar] [CrossRef]
- Amodu, O.A.; Othman, M. Machine-to-Machine Communication: An Overview of Opportunities. Comput. Networks 2018, 145, 255–276. [Google Scholar] [CrossRef]
- Zeng, Y.; Zhang, R.; Lim, T.J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Commun. Mag. 2016, 54, 36–42. [Google Scholar] [CrossRef] [Green Version]
- Checko, A.; Christiansen, H.L.; Yan, Y.; Scolari, L.; Kardaras, G.; Berger, M.S.; Dittmann, L. Cloud RAN for Mobile Networks—A Technology Overview. IEEE Commun. Surv. Tutorials 2015, 17, 405–426. [Google Scholar] [CrossRef] [Green Version]
- Abbas, N.; Zhang, Y.; Taherkordi, A.; Skeie, T. Mobile Edge Computing: A Survey. IEEE Internet Things J. 2018, 5, 450–465. [Google Scholar] [CrossRef] [Green Version]
- Kamal, M.A.; Raza, H.W.; Alam, M.M.; Mohd, M. Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider. Int. J. Recent Technol. Eng. 2020, 8, 4124–4232. [Google Scholar] [CrossRef]
- Abdelwahab, S.; Hamdaoui, B.; Guizani, M.; Rayes, A. Enabling smart cloud services through remote sensing: An internet of everything enabler. IEEE Internet Things J. 2014, 1, 276–288. [Google Scholar] [CrossRef]
- Pi, Z.; Khan, F. An introduction to millimeter-wave mobile broadband systems. IEEE Commun. Mag. 2011, 49, 101–107. [Google Scholar] [CrossRef]
- Siddiqi, Y.; Joung, J.; Siddiqi, M.A.; Yu, H.; Joung, J. 2019 5G Ultra-Reliable Low-Latency Communication.pdf. Electronics 2019, 8, 981. [Google Scholar] [CrossRef] [Green Version]
- Lien, S.Y.; Chen, K.C.; Liang, Y.C.; Lin, Y. Cognitive radio resource management for future cellular networks. IEEE Wirel. Commun. 2014, 21, 70–79. [Google Scholar] [CrossRef]
- Nam, W.; Bai, D.; Lee, J.; Kang, I. Advanced Interference Management for 5G Cellular Networks. IEEE Commun. Mag. 2014, 52, 52–60. [Google Scholar] [CrossRef]
- Latif, S.; Qadir, J.; Farooq, S.; Imran, M.A. How 5G wireless (and Concomitant Technologies) will revolutionize healthcare? Futur. Internet 2017, 9, 93. [Google Scholar] [CrossRef] [Green Version]
- Borgia, E. The internet of things vision: Key features, applications and open issues. Comput. Commun. 2014, 54, 1–31. [Google Scholar] [CrossRef]
- Al-Turjman, F.; Zahmatkesh, H.; Shahroze, R. An overview of security and privacy in smart cities’ IoT communications. Trans. Emerg. Telecommun. Technol. 2019, e3677. [Google Scholar] [CrossRef]
- Newcomb, J.L. Iota: A Calculus for Internet of Things Automation. In Proceedings of the 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Vancouver, BC, Canada, 25–27 October 2017; pp. 119–133. [Google Scholar]
- Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef] [Green Version]
- Al-Falahy, N.; Alani, O.Y. TelecommunicaTions neTworking 5G: Evolution or Revolution? IEEE Computer Society, University of Salford: Salford, UK, 2017. [Google Scholar]
- Yu, G.; Zhang, Z.; Qu, F.; Li, G.Y. Ultra-Dense Heterogeneous Networks with Full-Duplex Small Cell Base Stations. IEEE Netw. 2017, 31, 108–114. [Google Scholar] [CrossRef]
- Navarro-Ortiz, J.; Romero-Diaz, P.; Sendra, S.; Ameigeiras, P.; Ramos-Munoz, J.J.; Lopez-Soler, J.M. A Survey on 5G Usage Scenarios and Traffic Models. IEEE Commun. Surv. Tutorials 2020, 22, 905–929. [Google Scholar] [CrossRef]
- Agiwal, M.; Roy, A.; Saxena, N. Next generation 5G wireless networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 2016, 18, 1617–1655. [Google Scholar] [CrossRef]
- Gupta, A.; Jha, R.K. A Survey of 5G Network: Architecture and Emerging Technologies. IEEE Access 2015, 3, 1206–1232. [Google Scholar] [CrossRef]
- Vision, I.M.T. Framework and overall objectives of the future development of IMT for 2020 and beyond. In Recommendation ITU; ITU: Geneva, Switzerland, 2015; pp. 1–19. [Google Scholar]
- Zhang, S.; Xu, X.; Wu, Y.; Lu, L. 5G: Towards energy-efficient, low-latency and high-reliable communications networks. In Proceedings of the 2014 IEEE International Conference on Communication Systems, Macau, China, 19–21 November 2014; pp. 197–201. [Google Scholar] [CrossRef]
- Kumbhar, A.; Koohifar, F.; Güvenç, I.; Mueller, B. A Survey on Legacy and Emerging Technologies for Public Safety Communications. IEEE Commun. Surv. Tutor. 2017, 19, 97–124. [Google Scholar] [CrossRef]
- Pedersen, K.I.; Frederiksen, F.; Berardinelli, G.; Mogensen, P.E. The coverage-latency-capacity dilemma for TDD wide area operation and related 5G solutions. In Proceedings of the 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, China, 15–18 May 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Monserrat, J.F.; Mange, G.; Braun, V.; Tullberg, H.; Zimmermann, G.; Bulakci, Ö. METIS research advances towards the 5G mobile and wireless system definition. Eurasip J. Wirel. Commun. Netw. 2015, 2015, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Cengiz, K.; Aydemir, M. Next-Generation infrastructure and technology issues in 5G systems. J. Commun. Softw. Syst. 2018, 14, 33–39. [Google Scholar] [CrossRef] [Green Version]
- Vaezi, M.; Ding, Z.; Vincent Poor, H. Multiple Access Techniques for 5G Wireless Networks and Beyond; Springer: Berlin/Heidelberg, Germany, 2018; ISBN 9783319920900. [Google Scholar]
- Thandekkattu, S.G. A Survey of 5G Network: IoT connectivity Architecture. In Proceedings of the International Conference on Internet Computing (ICOMP), Athens, Greece, 7–10 October 2018; pp. 28–37. [Google Scholar]
- Bhandari, N.; Devra, S.; Singh, K. Evolution of Cellular Network: From 1G to 5G. Int. J. Eng. Tech. Sept-Oct 2017 2017, 3, 98–105. [Google Scholar]
- Chataut, R.; Akl, R. Massive MIMO systems for 5G and beyond networks—overview, recent trends, challenges, and future research direction. Sensors 2020, 20, 2753. [Google Scholar] [CrossRef]
- Santhi, K.R.; Srivastava, V.K.; SenthilKumaran, G.; Butare, A. Goals of true broad band’s wireless next wave (4G-5G). IEEE Veh. Technol. Conf. 2003, 58, 2317–2321. [Google Scholar] [CrossRef]
- Dehon, A. Fundamental underpinnings of reconfigurable computing architectures. Proc. IEEE 2015, 103, 355–378. [Google Scholar] [CrossRef]
- Furht, B.; Ahson, S.A. Long Term Evolution: 3GPP LTE Radio and Cellular Technology; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Stefania, S.; Issam, T.; Matthew, B. LTE, the UMTS Long Term Evolution: From Theory to Practice; A John Wiley Sons, Ltd.: Hoboken, NJ, USA, 2009; Volume 6, pp. 136–144. [Google Scholar]
- Halonen, T.; Romero, J.; Melero, J. GSM, GPRS and EDGE Performance: Evolution towards 3G/UMTS.; John Wiley & Sons: Hoboken, NJ, USA, 2004. [Google Scholar]
- Andrews, J.G.; Ghosh, A.; Muhamed, R. Fundamentals of WiMAX; Pearson Education: London, UK, 2007. [Google Scholar]
- Huang, T.; Yang, W.; Wu, J.; Ma, J.; Zhang, X.; Zhang, D. A Survey on Green 6G Network: Architecture and Technologies. IEEE Access 2019, 7, 175758–175768. [Google Scholar] [CrossRef]
- Imoize, A.L.; Adedeji, O.; Tandiya, N.; Shetty, S. 6G Enabled Smart Infrastructure for Sustainable Society. Sensors 2021, 21, 1709. [Google Scholar] [CrossRef] [PubMed]
- Fletcher, S.; Telecom, N.E.C. Cellular Architecture for 5G. IEEE Commun. Mag. 2014, 52, 122–130. [Google Scholar]
- Baldemair, R.; Dahlman, E.; Fodor, G.; Mildh, G.; Parkvall, S.; Selen, Y.; Tullberg, H.; Balachandran, K. Evolving wireless communications: Addressing the challenges and expectations of the future. IEEE Veh. Technol. Mag. 2013, 8, 24–30. [Google Scholar] [CrossRef]
- Chowdhury, M.Z.; Shahjalal, M.; Hasan, M.K.; Jang, Y.M. The role of optical wireless communication technologies in 5G/6G and IoT solutions: Prospects, directions, and challenges. Appl. Sci. 2019, 9, 4367. [Google Scholar] [CrossRef] [Green Version]
- Zikria, Y.B.; Kim, S.W.; Afzal, M.K.; Wang, H.; Rehmani, M.H. 5G mobile services and scenarios: Challenges and solutions. Sustainability 2018, 10, 3626. [Google Scholar] [CrossRef] [Green Version]
- Schwab, K. The Fourth Industrial Revolution; Currency Books: New York, NY, USA, 2017; ISBN 978-1-5247-5886-8. [Google Scholar]
- Ali, S.; Qaisar, S.B.; Saeed, H.; Khan, M.F.; Naeem, M.; Anpalagan, A. Network challenges for cyber physical systems with tiny wireless devices: A case study on reliable pipeline condition monitoring. Sensors 2015, 15, 7172–7205. [Google Scholar] [CrossRef] [Green Version]
- Manap, S.; Dimyati, K.; Hindia, M.N.; Abu Talip, M.S.; Tafazolli, R. Survey of Radio Resource Management in 5G Heterogeneous Networks. IEEE Access 2020, 8, 131202–131223. [Google Scholar] [CrossRef]
- Xu, Y.; Gui, G.; Gacanin, H.; Adachi, F. A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges. IEEE Commun. Surv. Tutor. 2021, 23, 668–695. [Google Scholar] [CrossRef]
- Olwal, T.O.; Djouani, K.; Kurien, A.M. A Survey of Resource Management Toward 5G Radio Access Networks. IEEE Commun. Surv. Tutor. 2016, 18, 1656–1686. [Google Scholar] [CrossRef]
- Su, R.; Zhang, D.; Venkatesan, R.; Gong, Z.; Li, C.; Ding, F.; Jiang, F.; Zhu, Z. Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models. IEEE Netw. 2019, 33, 172–179. [Google Scholar] [CrossRef]
- Noor-A-Rahim, M.; Liu, Z.; Lee, H.; Ali GM, N.; Pesch, D.; Xiao, P. A survey on resource allocation in vehicular networks. IEEE Trans. Intell. Transp. Syst. 2020. [Google Scholar] [CrossRef]
- Chien, W.C.; Huang, S.Y.; Lai, C.F.; Chao, H.C. Resource management in 5g mobile networks: Survey and challenges. J. Inf. Process. Syst. 2020, 16, 896–914. [Google Scholar] [CrossRef]
- Boell, S.K.; Cecez-Kecmanovic, D. On being “systematic” in literature reviews in IS. J. Inf. Technol. 2015, 30, 161–173. [Google Scholar] [CrossRef]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [Green Version]
- Peters, M.D.J.; Godfrey, C.M.; Khalil, H.; McInerney, P.; Parker, D.; Soares, C.B. Guidance for conducting systematic scoping reviews. Int. J. Evid. Based. Healthc. 2015, 13, 141–146. [Google Scholar] [CrossRef] [Green Version]
- Guevara, L.; Auat Cheein, F. The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems. Sustainability 2020, 12, 6469. [Google Scholar] [CrossRef]
- Foschini, G.J.; Gans, M.J. On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas. Wirel. Pers. Commun. 1998, 6, 311–335. [Google Scholar] [CrossRef]
- Ge, X.; Yang, J.; Gharavi, H.; Sun, Y. Energy Efficiency Challenges of 5G Small Cell Networks. IEEE Commun. Mag. 2017, 55, 184–191. [Google Scholar] [CrossRef] [Green Version]
- Cassiau, N.; Maret, L.; Dore, J.B.; Savin, V.; Ktenas, D. Assessment of 5G NR physical layer for future satellite networks. In Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 26–29 November 2019; pp. 1020–1024. [Google Scholar] [CrossRef]
- Norp, T. 5G Requirements and Key Performance Indicators. J. ICT Stand. 2018, 6, 15–30. [Google Scholar] [CrossRef] [Green Version]
- Kihero, A.B.; Solaija, M.S.J.; Arslan, H. Multi-Numerology Multiplexing and Inter-Numerology Interference Analysis for 5G. arXiv 2019, arXiv:1905.12748. [Google Scholar]
- Yazar, A.; Arslan, H. Flexible multi-numerology systems for 5G new radio. J. Mob. Multimed. 2018, 14, 367–394. [Google Scholar] [CrossRef] [Green Version]
- Zhang, L.; Ijaz, A.; Xiao, P.; Tafazolli, R. Channel Equalization and Interference Analysis for Uplink Narrowband Internet of Things (NB-IoT). IEEE Commun. Lett. 2017, 21, 2206–2209. [Google Scholar] [CrossRef] [Green Version]
- Hossain, E.; Hasan, M. IEEE Instrumentation & Measurement Magazine 5G Cellular: Key Enabling Technologies and Research Challenges. IEEE Instrum. Meas. Mag. 2015, 15, 11–21. [Google Scholar]
- Pokhrel, S.R.; DIng, J.; Park, J.; Park, O.S.; Choi, J. Towards Enabling Critical mMTC: A Review of URLLC within mMTC. IEEE Access 2020, 8, 131796–131813. [Google Scholar] [CrossRef]
- Medbo, J.; Borner, K.; Haneda, K.; Hovinen, V.; Imai, T.; Jarvelainen, J.; Jamsa, T.; Karttunen, A.; Kusume, K.; Kyrolainen, J.; et al. Channel modelling for the fifth generation mobile communications. In Proceedings of the 8th European Conference on Antennas & Propagation, Hague, The Netherlands, 6–11 April 2014; pp. 219–223. [Google Scholar] [CrossRef]
- Li, X.; Samaka, M.; Chan, H.A.; Bhamare, D.; Gupta, L.; Guo, C.; Jain, R. Network Slicing for 5G: Challenges and Opportunities. IEEE Internet Comput. 2017, 21, 20–27. [Google Scholar] [CrossRef]
- Zhang, A.; Wang, L.; Ye, X.; Lin, X. Light-Weight and Robust Security-Aware D2D-Assist Data Transmission Protocol for Mobile-Health Systems. IEEE Trans. Inf. Forensics Secur. 2017, 12, 662–675. [Google Scholar] [CrossRef]
- Zirwas, W. Opportunistic CoMP for 5G massive MIMO multilayer networks. In Proceedings of the WSA 2015: 19th International ITG Workshop on Smart Antennas, Ilmenau, Germany, 3–5 March 2015; pp. 1–7. [Google Scholar]
- O’Connell, E.; Moore, D.; Newe, T. Challenges Associated with Implementing 5G in Manufacturing. Telecom 2020, 1, 5. [Google Scholar] [CrossRef]
- Oughton, E.J.; Frias, Z.; van der Gaast, S.; van der Berg, R. Assessing the capacity, coverage and cost of 5G infrastructure strategies: Analysis of the Netherlands. Telemat. Inform. 2019, 37, 50–69. [Google Scholar] [CrossRef]
- Nguyen, L.D. Resource allocation for energy efficiency in 5G wireless networks. EAI Endorsed Trans. Ind. Netw. Intell. Syst. 2018, 5, 1–7. [Google Scholar] [CrossRef]
- Haryadi, S.; Aryanti, D.R. The fairness of resource allocation and its impact on the 5G ultra-dense cellular network performance. In Proceedings of the 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA), Lombok, Indonesia, 26–27 October 2017; pp. 1–4. [Google Scholar] [CrossRef]
- Tayyaba, S.K.; Shah, M.A. Resource allocation in SDN based 5G cellular networks. Peer-to-Peer Netw. Appl. 2019, 12, 514–538. [Google Scholar] [CrossRef]
- Alqerm, I.; Shihada, B. A cooperative online learning scheme for resource allocation in 5G systems. In Proceedings of the 2016 IEEE International Conference on Communications Workshops, Kuala Lumpur, Malaysia, 23–27 May 2016; pp. 1–7. [Google Scholar] [CrossRef] [Green Version]
- Adeogun, R.O. A Novel Game Theoretic Method for Efficient Downlink Resource Allocation in Dual Band 5G Heterogeneous Network. Wirel. Pers. Commun. 2018, 101, 119–141. [Google Scholar] [CrossRef]
- Zhao, Y.; Chen, Y.; Jian, R.; Yang, L. A Resource Allocation Scheme for SDN-Based 5G Ultra-Dense Heterogeneous Networks. In Proceedings of the 2017 IEEE Globecom Workshops, Singapore, 4–8 December 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Del Fiorentino, P.; Vitiello, C.; Lottici, V.; Giannetti, F.; Luise, M. A robust resource allocation algorithm for packet BIC-UFMC 5G wireless communications. In Proceedings of the 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, 29 August–2 September 2016; pp. 843–847. [Google Scholar] [CrossRef] [Green Version]
- Zhao, P.; Feng, L.; Yu, P.; Li, W.; Qiu, X. A Social-Aware Resource Allocation for 5G Device-to-Device Multicast Communication. IEEE Access 2017, 5, 15717–15730. [Google Scholar] [CrossRef]
- Tang, L.; Tan, Q.; Shi, Y.; Wang, C.; Chen, Q. Adaptive Virtual Resource Allocation in 5G Network Slicing Using Constrained Markov Decision Process. IEEE Access 2018, 6, 61184–61195. [Google Scholar] [CrossRef]
- Saraereh, O.A.; Alsaraira, A.; Khan, I.; Uthansakul, P. An Efficient Resource Allocation Algorithm for OFDM-Based NOMA in 5G Systems. Electronics 2019, 8, 1399. [Google Scholar] [CrossRef] [Green Version]
- Feng, L.; Li, W.; Yu, P.; Qiu, X. An Enhanced OFDM Resource Allocation Algorithm in C-RAN Based 5G Public Safety Network. Mob. Inf. Syst. 2016, 2016. [Google Scholar] [CrossRef]
- Bashir, A.K.; Arul, R.; Basheer, S.; Raja, G.; Jayaraman, R.; Qureshi, N.M.F. An optimal multitier resource allocation of cloud RAN in 5G using machine learning. Trans. Emerg. Telecommun. Technol. 2019, 30, 1–20. [Google Scholar] [CrossRef]
- Jia, Y.; Tian, H.; Fan, S.; Zhao, P.; Zhao, K. Bankruptcy game based resource allocation algorithm for 5G Cloud-RAN slicing. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15–18 April 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Wu, D.; Zhang, Z.; Wu, S.; Yang, J.; Wang, R. Biologically Inspired Resource Allocation for Network Slices in 5G-Enabled Internet of Things. IEEE Internet Things J. 2019, 6, 9266–9279. [Google Scholar] [CrossRef]
- Song, S.; Lee, C.; Cho, H.; Lim, G.; Chung, J.M. Clustered virtualized network functions resource allocation based on context-aware grouping in 5g edge networks. IEEE Trans. Mob. Comput. 2020, 19, 1072–1083. [Google Scholar] [CrossRef]
- Chen, Z.; Li, T.; Fan, P.; Quek, T.Q.S.; Letaief, K. Ben Cooperation in 5G Heterogeneous Networking: Relay Scheme Combination and Resource Allocation. IEEE Trans. Commun. 2016, 64, 3430–3443. [Google Scholar] [CrossRef]
- Bonjorn, N.; Foukalas, F.; Cañellas, F.; Pop, P. Cooperative Resource Allocation and Scheduling for 5G eV2X Services. IEEE Access 2019, 7, 58212–58220. [Google Scholar] [CrossRef]
- Sinaie, M.; Lin, P.H.; Zappone, A.; Azmi, P.; Jorswieck, E.A. Delay-aware resource allocation for 5G wireless networks with wireless power transfer. IEEE Trans. Veh. Technol. 2018, 67, 5841–5855. [Google Scholar] [CrossRef]
- Mishra, P.K.; Pandey, S.; Udgata, S.K.; Biswash, S.K. Device-centric resource allocation scheme for 5G networks. Phys. Commun. 2018, 26, 175–184. [Google Scholar] [CrossRef]
- Mathur, R.P.; Pratap, A.; Misra, R. Distributed algorithm for resource allocation in uplink 5G networks. In Proceedings of the 7th ACM International Workshop on Mobility, Interference, and MiddleWare Management in HetNets, Chennai, India, 10–14 July 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Femenias, G.; Riera-Palou, F.; Mestre, X.; Olmos, J.J. Downlink scheduling and resource allocation for 5G MIMO-multicarrier: OFDM vs FBMC/OQAM. IEEE Access 2017, 5, 13770–13786. [Google Scholar] [CrossRef] [Green Version]
- BenMimoune, A.; Khasawneh, F.A.; Rong, B.; Kadoch, M. Dynamic joint resource allocation and relay selection for 5G multi-hop relay systems. Telecommun. Syst. 2017, 66, 283–294. [Google Scholar] [CrossRef]
- Araniti, G.; Condoluci, M.; Orsino, A.; Iera, A.; Molinaro, A. Effective resource allocation in 5G-satellite networks. In Proceedings of the 2015 IEEE International Conference on Communications, London, UK, 8–12 June 2015; pp. 844–849. [Google Scholar] [CrossRef]
- Chien, H.T.; Lin, Y.D.; Lai, C.L.; Wang, C.T. End-to-end slicing as a service with computing and communication resource allocation for multi-tenant 5G systems. IEEE Wirel. Commun. 2019, 26, 104–112. [Google Scholar] [CrossRef]
- Ali, A.; Shah, G.A.; Arshad, J. Energy efficient resource allocation for M2M devices in 5G. Sensors 2019, 19, 1830. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, S.; Ni, Q.; Sun, Y.; Min, G.; Al-Rubaye, S. Energy-Efficient Resource Allocation for Industrial Cyber-Physical IoT Systems in 5G Era. IEEE Trans. Ind. Inform. 2018, 14, 2618–2628. [Google Scholar] [CrossRef] [Green Version]
- Al Qerm, I.; Shihada, B. Enhanced machine learning scheme for energy efficient resource allocation in 5g heterogeneous cloud radio access networks. In Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, 8–13 October 2017; pp. 1–7. [Google Scholar] [CrossRef]
- Swetha, G.D.; Murthy, G.R. Fair resource allocation for D2D communication in mmwave 5G networks. In Proceedings of the 2017 16th Annual Mediterranean Ad Hoc Networking Workshop, Budva, Montenegro, 28–30 June 2017. [Google Scholar] [CrossRef]
- Hao, P.; Yan, X.; Li, J.; Li, Y.N.R.; Wu, H. Flexible resource allocation in 5G ultra dense network with self-backhaul. In Proceedings of the 2015 IEEE Globecom Workshops, San Diego, CA, USA, 6–10 December 2015. [Google Scholar] [CrossRef]
- Liu, C.F.; Samarakoon, S.; Bennis, M.; Poor, H.V. Fronthaul-Aware Software-Defined Wireless Networks: Resource Allocation and User Scheduling. IEEE Trans. Wirel. Commun. 2018, 17, 533–547. [Google Scholar] [CrossRef]
- Zhang, X.; Wang, J. Heterogeneous QoS-driven resource allocation over MIMO-OFDMA based 5G cognitive radio networks. In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference, San Francisco, CA, USA, 19–22 March 2017. [Google Scholar] [CrossRef]
- Xu, S.; Li, R.; Yang, Q. Improved genetic algorithm based intelligent resource allocation in 5G Ultra Dense networks. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference, Barcelona, Spain, 15–18 April 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Moltafet, M.; Joda, R.; Mokari, N.; Sabagh, M.R.; Zorzi, M. Joint access and fronthaul radio resource allocation in PD-NOMA-based 5G networks enabling dual connectivity and CoMP. IEEE Trans. Commun. 2018, 66, 6463–6477. [Google Scholar] [CrossRef] [Green Version]
- Ferdouse, L.; Anpalagan, A.; Erkucuk, S. Joint Communication and Computing Resource Allocation in 5G Cloud Radio Access Networks. IEEE Trans. Veh. Technol. 2019, 68, 9122–9135. [Google Scholar] [CrossRef]
- Wang, G.; Zomaya, A.; Perez, G.M.; Li, K. Algorithms and Architectures for Parallel Processing: 15th International Conference, ICA3PP 2015, Zhangjiajie, China, 18–20 November 2015, Proceedings, Part IV; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2015; Volume 9531, pp. 244–258. [Google Scholar] [CrossRef]
- Li, W.; Zi, Y.; Feng, L.; Zhou, F.; Yu, P.; Qiu, X. Latency-optimal virtual network functions resource allocation for 5g backhaul transport network slicing. Appl. Sci. 2019, 9, 701. [Google Scholar] [CrossRef] [Green Version]
- Imtiaz, S.; Ghauch, H.; Ur Rahman, M.M.; Koudouridis, G.; Gross, J. Learning-based resource allocation scheme for TDD-based 5G CRAN system. In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Malta, Malta, 13–17 November 2016; pp. 176–185. [Google Scholar] [CrossRef]
- Mishra, P.K.; Kumar, A.; Pandey, S. Minimum interference based resource allocation method in two-hop D2D communication for 5G cellular networks. In Proceedings of the 2017 International Conference on Intelligent Sustainable Systems, Palladam, India, 7–8 December 2017; pp. 1191–1196. [Google Scholar] [CrossRef]
- Ejaz, W.; Ibnkahla, M. Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks. IEEE Internet Things J. 2018, 5, 150–163. [Google Scholar] [CrossRef]
- Scott-Hayward, S.; Garcia-Palacios, E. Multimedia resource allocation in mmwave 5G networks. IEEE Commun. Mag. 2015, 53, 240–247. [Google Scholar] [CrossRef] [Green Version]
- Baghani, M.; Parsaeefard, S.; Le-Ngoc, T. Multi-objective resource allocation in density-aware design of C-RAN in 5G. IEEE Access 2018, 6, 45177–45190. [Google Scholar] [CrossRef]
- Escudero-Garzas, J.J.; Bousono-Calzon, C.; Garcia, A. On the Feasibility of 5G Slice Resource Allocation with Spectral Efficiency: A Probabilistic Characterization. IEEE Access 2019, 7, 151948–151961. [Google Scholar] [CrossRef]
- Han, Y.; Elayoubi, S.E.; Galindo-Serrano, A.; Varma, V.S.; Messai, M. Periodic Radio Resource Allocation to Meet Latency and Reliability Requirements in 5G Networks. In Proceedings of the 2018 IEEE 87th Vehicular Technology Conference, Porto, Portugal, 3–6 June 2018; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
- Yun, J.; Piran, M.J.; Suh, D.Y. QoE-Driven Resource Allocation for Live Video Streaming over D2D-Underlaid 5G Cellular Networks. IEEE Access 2018, 6, 72563–72580. [Google Scholar] [CrossRef]
- Imtiaz, S.; Ghauch, H.; Koudouridis, G.P.; Gross, J. Random forests resource allocation for 5G systems: Performance and robustness study. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference Workshops, Barcelona, Spain, 15–18 April 2018; pp. 326–331. [Google Scholar] [CrossRef] [Green Version]
- Feng, L.; Zhao, P.; Zhou, F.; Yin, M.; Yu, P.; Li, W.; Qiu, X. Resource Allocation for 5G D2D Multicast Content Sharing in Social-Aware Cellular Networks. IEEE Commun. Mag. 2018, 56, 112–118. [Google Scholar] [CrossRef]
- Zhang, B.; Mao, X.; Yu, J.L.; Han, Z. Resource allocation for 5G heterogeneous cloud radio access networks with D2D communication: A matching and coalition approach. IEEE Trans. Veh. Technol. 2018, 67, 5883–5894. [Google Scholar] [CrossRef]
- Ren, H.; Pan, C.; Deng, Y.; Elkashlan, M.; Nallanathan, A. Resource Allocation for URLLC in 5G Mission-Critical IoT Networks. In Proceedings of the 2019 IEEE International Conference on Communications, Shanghai, China, 20–24 May 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Xu, B.; Chen, Y.; Carrión, J.R.; Zhang, T. Resource allocation in energy-cooperation enabled two-tier NOMA HetNets toward green 5G. IEEE J. Sel. Areas Commun. 2017, 35, 2758–2770. [Google Scholar] [CrossRef]
- Moltafet, M.; Parsaeefard, S.; Javan, M.R.; Mokari, N. Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks. IEEE Trans. Veh. Technol. 2019, 68, 5758–5768. [Google Scholar] [CrossRef] [Green Version]
- AlQerm, I.; Shihada, B. Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks. IEEE Trans. Mob. Comput. 2018, 17, 2423–2437. [Google Scholar] [CrossRef] [Green Version]
- Le, N.T.; Jayalath, D.; Coetzee, J. Spectral-efficient resource allocation for mixed services in OFDMA-based 5G heterogeneous networks. Trans. Emerg. Telecommun. Technol. 2018, 29, 1–13. [Google Scholar] [CrossRef]
- Song, Z.; Ni, Q.; Sun, X. Spectrum and energy efficient resource allocation with QoS requirements for hybrid MC-NOMA 5G Systems. IEEE Access 2018, 6, 37055–37069. [Google Scholar] [CrossRef]
- Huo, L.; Jiang, D. Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommun. Syst. 2019, 72, 377–388. [Google Scholar] [CrossRef]
- Condoluci, M.; Araniti, G.; Dohler, M.; Iera, A.; Molinaro, A. Virtual code resource allocation for energy-aware MTC access over 5G systems. Ad Hoc Networks 2016, 43, 3–15. [Google Scholar] [CrossRef] [Green Version]
- Yu, P.; Zhou, F.; Zhang, X.; Qiu, X.; Kadoch, M.; Cheriet, M. Deep Learning-Based Resource Allocation for 5G Broadband TV Service. IEEE Trans. Broadcast. 2020, 66, 800–813. [Google Scholar] [CrossRef]
- Tang, F.; Zhou, Y.; Kato, N. Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet. IEEE J. Sel. Areas Commun. 2020, 8716, 1–10. [Google Scholar] [CrossRef]
- Dai, Y.; Zhang, K.; Maharjan, S.; Zhang, Y. Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G beyond. IEEE Trans. Veh. Technol. 2020, 69, 12175–12186. [Google Scholar] [CrossRef]
- Purushothaman, K.E.; Nagarajan, V. Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO. Int. J. Electron. 2020, 108, 1214–1233. [Google Scholar] [CrossRef]
- Rehman, W.U.; Salam, T.; Almogren, A.; Haseeb, K.; Ud Din, I.; Bouk, S.H. Improved Resource Allocation in 5G MTC Networks. IEEE Access 2020, 8, 49187–49197. [Google Scholar] [CrossRef]
- Song, X.; Wang, K.; Lei, L.; Zhao, L.; Li, Y.; Wang, J. Interference Minimization Resource Allocation for V2X Communication Underlaying 5G Cellular Networks. Wirel. Commun. Mob. Comput. 2020, 2020, 2985367. [Google Scholar] [CrossRef]
- Oladejo, S.O.; Falowo, O.E. Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario. IEEE Access 2020, 8, 74834–74852. [Google Scholar] [CrossRef]
- Karimi, A.; Pedersen, K.I.; Mogensen, P. Low-Complexity Centralized Multi-Cell Radio Resource Allocation for 5G URLLC. In Proceedings of the 2020 IEEE Wireless Communications and Networking Conference, Seoul, Korea, 25–28 May 2020. [Google Scholar] [CrossRef]
- Beshley, M.; Kryvinska, N.; Seliuchenko, M.; Beshley, H.; Shakshuki, E.M.; Yasar, A.U.H. End-to-End QoS “Smart Queue” Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Networks. Sensors 2020, 20, 2324. [Google Scholar] [CrossRef] [Green Version]
- Li, P.; Han, L.; Xu, S.; Wu, D.O.; Gong, P. Resource Allocation for 5G-Enabled Vehicular Networks in Unlicensed Frequency Bands. IEEE Trans. Veh. Technol. 2020, 69, 13546–13555. [Google Scholar] [CrossRef]
- Khan, J.; Jacob, L. Resource Allocation for CoMP Enabled URLLC in 5G C-RAN Architecture. IEEE Syst. J. 2020, 1–12. [Google Scholar] [CrossRef]
- Liu, Z.; Hou, G.; Yuan, Y.; Chan, K.Y.; Ma, K.; Guan, X. Robust resource allocation in two-tier NOMA heterogeneous networks toward 5G. Comput. Netw. 2020, 176, 107299. [Google Scholar] [CrossRef]
- Jang, H.; Kim, J.; Yoo, W.; Chung, J.M. URLLC Mode Optimal Resource Allocation to Support HARQ in 5G Wireless Networks. IEEE Access 2020, 8, 126797–126804. [Google Scholar] [CrossRef]
- Khan, H.Z.; Ali, M.; Naeem, M.; Rashid, I.; Siddiqui, A.M.; Imran, M.; Mumtaz, S. Joint admission control, cell association, power allocation and throughput maximization in decoupled 5G heterogeneous networks. Telecommun. Syst. 2021, 76, 115–128. [Google Scholar] [CrossRef]
- Ma, T.; Zhang, Y.; Wang, F.; Wang, D.; Guo, D. Slicing Resource Allocation for eMBB and URLLC in 5G RAN. Wirel. Commun. Mob. Comput. 2020, 2020, 6290375. [Google Scholar] [CrossRef] [Green Version]
- Ghosh, S.; De, D. Weighted Majority Cooperative Game Based Dynamic Small Cell Clustering and Resource Allocation for 5G Green Mobile Network. Wirel. Pers. Commun. 2020, 111, 1391–1411. [Google Scholar] [CrossRef]
- Ari, A.A.A.; Gueroui, A.; Titouna, C.; Thiare, O.; Aliouat, Z. Resource allocation scheme for 5G C-RAN: A Swarm Intelligence based approach. Comput. Networks 2019, 165, 106957. [Google Scholar] [CrossRef]
- Mudassir, A.; Hassan, S.A.; Pervaiz, H.; Akhtar, S.; Kamel, H.; Tafazolli, R. Game theoretic efficient radio resource allocation in 5G resilient networks: A data driven approach. Trans. Emerg. Telecommun. Technol. 2019, 30, e3582. [Google Scholar] [CrossRef] [Green Version]
String | B1 | B2 |
---|---|---|
String 1 (S1) | Fifth Generation | Resource Allocation |
String 2 (S2) | Fifth Generation | Resource Distribution |
String 3 (S3) | Fifth Generation Network | Resource Reservation |
String 4 (S4) | 5G | |
String 5 (S5) | 5G Network |
Publisher | URL |
---|---|
MDPI | https://www.mdpi.com (accessed on: 1 July 2021) |
Science Direct | https://www.sciencedirect.com (accessed on 15 June 2021) |
Wiley Online Library | https://onlinelibrary.wiley.com (accessed on 20 June 2021) |
Springer | https://link.springer.com (accessed on 25 May 2021) |
Sage | https://journals.sagepub.com (accessed on 12 May 2021) |
Google Scholar | https://scholar.google.com (accessed on 25 July 2021) |
ACM | https://www.acm.org (accessed on 20 May 2021) |
IEEE | https://ieeexplore.ieee.org (accessed on 28 June 2021) |
Criteria | |
---|---|
Inclusion |
|
Exclusion |
|
Ref | Algorithm/Scheme/Strategy | Problem Addressed | Improvements/Achievements | Limitations/Weakness |
---|---|---|---|---|
[89] | Cooperative Online Learning Scheme | Extreme interference between the multi-tier users. |
|
|
[90] | Game-theoretic approach | Cross-tier interference. |
|
|
|
| |||
| ||||
[91] | Genetic Algorithm Particle Swarm Optimization-Power Allocation (GAPSO-PA) | The allocation of power in heterogeneous ultra-dense networks. |
|
|
[92] | Estimation of Goodput based Resource Allocation (EGP-BASED-RA) | Enhance Goodput (GP): (a specific metric of performance). |
|
|
[93] | The social-aware resource allocation scheme | D2D multicast grouping; |
|
|
Ineffective D2D links. |
| |||
| ||||
[94] | PGU-ADP algorithm | Dynamic virtual RA problem. |
|
|
Expansion of the total user rate. |
|
| ||
| ||||
[95] | Efficient Resource Allocation Algorithm | Enhance system capacity and maximum computational complexity. |
|
|
|
| |||
[96] | GBD Based Resource Allocation Algorithm | Enhances allocating algorithm’s efficiency. |
|
|
| ||||
| ||||
[97] | Multitier H-CRAN Architecture | Lacking intelligence perspective using existing C-RAN methods. |
|
|
| ||||
| ||||
| ||||
[98] | Bankruptcy game-based algorithm | Resource allocation and inaccessibility of wireless slices. |
|
|
|
| |||
[99] | BVRA-SCP Scheme | Enhancing service demands like low latency, enormous connection, and maximum data rate. |
|
|
| ||||
| ||||
| ||||
[100] | VNF-RACAG Scheme | Settlement of virtualized network functions (VNF). |
|
|
[101] | Hybrid DF-AF scheme | Promising to incorporate various wireless networks to deliver higher data rates. |
|
|
| ||||
[102] | Cooperative resource allocation and scheduling approach | Scheduling and resource allocation problems. |
|
|
|
| |||
| ||||
[103] | SWIPT framework | Low energy efficiency and high latency. |
|
|
|
| |||
[104] | The device-centric resource allocation scheme | Declining of network throughput and raises delay in resource allocation. |
|
|
|
| |||
[105] | Distributed Resource Allocation Algorithm | Resource allocation and interference management in 5G networks. |
|
|
| ||||
[106] | Unified cross-layer framework | Physical layer modulation format and waveform, resource allocation, and downlink scheduling. |
|
|
[107] | Dynamic joint resource allocation and relay selection scheme | Relay selection and downlink resource allocation. |
|
|
| ||||
[108] | Low-Complexity Subgrouping scheme | Radio resource management of multicast transmissions. |
|
|
|
| |||
| ||||
[109] | Joint Edge and Central Resource Slicer (JECRS) framework | Requires distinct resources from the lower tier and upper tier. |
|
|
| ||||
[110] | TCA algorithm | MTC devices are battery restricted and cannot afford much power consumption needed for spectrum usage. |
|
|
| ||||
[111] | IHM-VD algorithm | Power allocation and channel allocation issue. |
|
|
| ||||
[112] | Centralized approximated online learning resource allocation scheme | The inter-tier interference among macro-BS and RRHS; and energy efficiency. |
|
|
|
| |||
| ||||
[113] | Spectrum resource and power allocation scheme | Emphasize on a fair distribution of resources in one cell. |
|
|
| ||||
| ||||
[114] | Tri-stage fairness scheme | Resource allocation problem in UDN having caching and self-backhaul. |
|
|
| ||||
| ||||
[115] | Fronthaul-aware software-defined resource allocation mechanism | Overhead generated using a capacity-limited shared fronthaul. |
|
|
|
| |||
[116] | Heterogeneous statistical | Heterogeneity issues. |
|
|
The QoS-driven resource allocation scheme |
| |||
| ||||
[117] | Nondominated sorting genetic algorithm II (NSGA-II) | Unable to get optimal results concurrently. |
|
|
|
| |||
[118] | Joint access and fronthaul radio resource allocation | Downlink energy efficiency (EE) and millimeter-wave (MMW) links in access and fronthaul. |
|
|
|
| |||
| ||||
[119] | Double-sided auction-based distributed resource allocation (DSADRA) method | Intercell and inter tier interference. |
|
|
|
| |||
[120] | Joint power and reduced spectral leakage-based resource allocation | Interference from D2D pairs. |
|
|
|
| |||
| ||||
[121] | Branch-and-bound scheme | Latency-optimal virtual resource allocation. |
|
|
|
| |||
| ||||
[122] | The learning-based resource allocation scheme | To achieve high system capacity better performance in terms of effective system throughput. |
|
|
[123] | Resource allocation method with minimum interference for two-hop D2D communications | Interference which reduces network throughput. |
|
|
| ||||
[124] | Multiband cooperative spectrum sensing and resource allocation framework | Energy consumption for spectrum sensing. |
|
|
| ||||
[125] | Channel-time allocation PSO Scheme | To acquire gigabit-per-second throughput and low delay for achieving and maintaining the QoS. |
|
|
|
| |||
[126] | Heterogeneous (high density)/hierarchical (low density) virtualized software-defined cloud RAN (HVSD-CRAN). | Density of users. |
|
|
| ||||
[127] | Mini slot-based slicing allocation problem (MISA-P) model | The probability of forming 5G slices. |
|
|
| ||||
| ||||
[128] | A joint resource allocation and modulation and coding schemes | Requirement of extremely low latency and ultra-reliable communication. |
|
|
|
| |||
[129] | QoS/QoE-aware relay allocation algorithm | Neglects temporal requirements for optimum performances. |
|
|
|
| |||
| ||||
[130] | The learning-based resource allocation scheme | Interference coordination complexity and significant channel state information (CSI) acquisition overhead. |
|
|
| ||||
[131] | Device-to-device multicast (D2MD) scheme | Improving spectrum and energy efficiency and enabling traffic offloading from BSs to device. |
|
|
| ||||
[132] | Constrained deferred acceptance (DA) algorithm and a coalition formation algorithm | The interference management among D2D and current users. |
|
|
|
| |||
[86] | Novel resource allocation schemes (hybrid resource management) | Energy efficiency and consumption. |
|
|
[133] | Orthogonal multiple access (OMA) and relay-assisted transmission schemes. | Jointly optimize the block length and power allocation for reducing error probability. |
|
|
| ||||
[134] | Joint user association and Power Control algorithm | Optimizing power control and user association schemes. |
|
|
[135] | Successive convex approximation (SCA) based alternate search method (ASM) | Raise the total sum rate of users. |
|
|
|
| |||
[136] | An online learning algorithm for resource allocation | Inter-tier interference among RRHS and macro-BSs, and energy efficiency. |
|
|
|
| |||
[137] | Joint resource block (RB) and power allocation scheme | Enhance fairness in data rate among end-users. |
|
|
|
| |||
[138] | Hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) | Achieve the SE-EE tradeoff having minimum rate requirement of each user. |
|
|
|
| |||
[139] | Stackelberg game model | High inter-cell interference (ICI) and less energy efficiency. |
|
|
| ||||
[140] | Virtual code resource allocation (VCRA) approach | Reducing the collision probability. |
|
|
|
| |||
[141] | Deep reinforcement learning -unicast-multicast resource allocation framework (DRL-UMRAF) | High-quality services and achieving green energy savings of base stations. |
|
|
|
| |||
[142] | Deep reinforcement learning-based intelligent Up/Downlink resource allocation | The high dynamic network traffic and unpredicted link-state change. |
|
|
|
| |||
[143] | Joint computation offloading and resource allocation scheme | Complete network information and wireless channel state. |
|
|
| ||||
[144] | Deep neural network-Multi objective Sine Cosine algorithm (DNN-MOSCA) | Achieving better accuracy and reliability. |
|
|
| ||||
[145] | The improved resource allocation algorithm | Improving QoS requirements in MTC. |
|
|
| ||||
[146] | Resource Allocation Algorithm | The interference to 5G cellular users (CUs) related to QoS. |
|
|
|
| |||
[147] | Genetic algorithm- intelligent Latency-Aware Dynamic Resource Allocation Scheme (GI-LARE) | Efficient radio resource management. |
|
|
| ||||
| ||||
[148] | A Low-complexity centralized packet scheduling algorithm | Downlink centralized multi-cell scheduling. |
|
|
|
| |||
[149] | Smart queue management method | QoS of end-to-end real-time traffic. |
|
|
|
| |||
[150] | Proposed Optimal Resource Allocation Algorithm | The optimization problem in mixed-integer nonlinear programming (MINLP). |
|
|
|
| |||
| ||||
[151] | A novel packet delivery mechanism | Issues related to using CoMP for URLLC in C-RAN architecture. |
|
|
|
| |||
[152] | Distributed joint optimization algorithm for user association and power control | Improve total energy efficiency and reduce the inter-cell and intra-cell interference. |
|
|
| ||||
[153] | Pollaczek–Khinchine formula based quadratic optimization (PFQO) | Inaccurate transmission recovery delay of URLLC multi-user services. |
|
|
|
| |||
[154] | An outer approximation algorithm (OAA) | Multiple interferences, imbalanced user traffic load. |
|
|
|
| |||
| ||||
[155] | Joint Power and Subcarrier Allocation | URLLC reliability and network spectral efficiency. |
|
|
|
| |||
| ||||
[156] | Weighted Majority Cooperative Game Theory Based Clustering | Increase interference, improper utilization of resources. |
|
|
|
| |||
[157] | Bee-Ant-CRAN scheme | Design a logical joint mapping among RRHS and User Equipment (UE) and RRHS and BBUS too. |
|
|
| ||||
[158] | Noncooperative game theory-based user-centric resource optimization scheme | Enhance the coverage probability and sum rate. |
|
|
|
|
Metrics | References |
---|---|
Response Time | [119] |
End-To-End Delay | [100,149] |
Delay | [94,103,104,106,115,116,124,125,126,148,153] |
Throughput | [89,93,96,97,98,104,106,107,108,110,115,120,121,122,123,124,125,126,131,137,142,144,150,158] |
Packet Loss | [142,147] |
Latency | [102,103,109,115,121,128,133,147,148,151] |
Overhead | [95,114,122,130] |
Jitter | [96] |
Availability | [93,121] |
Spectral Efficiency | [89,90,92,95,97,105,112,113,117,127,136,137,138,155,156,158] |
Fairness | [89,93,96,98,106,107,113,114,132,137,138,144] |
Outage Ratio | [89,94,145] |
Sum Rate | [90,94,101,103,113,118,135,146,154,158] |
Energy Efficiency | [86,90,97,103,110,111,112,114,118,124,134,136,138,139,140,141,142,143,144,152] |
System Performance | [91,95,99,100,101,105,108,113,117,122,132] |
Low Complexity | [91,95,96,98,99,107,108,110,114,117,130,132] |
Power Allocation | [86,91,92,93,94,95,97,110,111,113,118,119,120,132,134,139,152] |
Reliability | [102,124,128,133,155] |
Time Required for RA | [104] |
Scalability | [108] |
Interference | [100,112,113,119,120,132,146,152,154,156] |
Power Consumption | [126,156] |
Feasibility | [128] |
Energy Consumption | [129,143] |
Domain | References |
---|---|
Fronthaul | [86,89,90,93,94,95,99,101,103,104,105,106,107,109,110,111,113,114,115,117,118,120,123,124,129,133,134,137,138,139,140,142,144,145,146,147,149,150,152,153,154,155,156,158] |
C-RAN | [96,97,109,111,112,118,119,122,126,130,135,143,148,151,157] |
H-CRAN | [97,132,136,143] |
Backhaul | [99,110,111,114,133,147] |
Uplink | [96,97,99,104,105,109,110,111,113,119,120,123,124,129,140,142,143,145,146,149,150,153,154,156,157,158] |
Downlink | [86,89,90,94,95,96,97,99,101,103,104,106,107,109,111,112,114,115,117,118,120,122,126,129,130,132,133,134,135,136,137,138,139,142,143,144,147,148,149,151,152,153,154,155,157] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kamal, M.A.; Raza, H.W.; Alam, M.M.; Su’ud, M.M.; Sajak, A.b.A.B. Resource Allocation Schemes for 5G Network: A Systematic Review. Sensors 2021, 21, 6588. https://doi.org/10.3390/s21196588
Kamal MA, Raza HW, Alam MM, Su’ud MM, Sajak AbAB. Resource Allocation Schemes for 5G Network: A Systematic Review. Sensors. 2021; 21(19):6588. https://doi.org/10.3390/s21196588
Chicago/Turabian StyleKamal, Muhammad Ayoub, Hafiz Wahab Raza, Muhammad Mansoor Alam, Mazliham Mohd Su’ud, and Aznida binti Abu Bakar Sajak. 2021. "Resource Allocation Schemes for 5G Network: A Systematic Review" Sensors 21, no. 19: 6588. https://doi.org/10.3390/s21196588