NOMA-Based Resource Allocation and Mobility Enhancement Framework for IoT in Next Generation Cellular Networks
Open accessAbstract
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the Internet, forming what is called the Internet of Things (IoT). The IoT devices with heterogeneous characteristics and the quality of experience (QoE) requirements may engage in the dynamic spectrum market due to the scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of this paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions enhance the radio resource utilization efficiency. The results show a substantial reduction in the number of sub-carriers required when compared with conventional OMA and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has fewer users or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve the required quality of service with a guaranteed bit rate (GBR) and non-GBR.
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An online adaptive ML approach for detection in NOMA in the context of 5G networks has been proposed.
“Online adaptive ML approach for detection in NOMA in the context of 5G networks has been proposed in [230].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [230]
The paper’s discussion of NOMA in 5G describes dynamic spectrum allocation, and its resource-allocation formulation uses successive interference cancellation. No passage found in our copy describes a proposed online adaptive machine-learning approach for NOMA detection (searched for that phrase and for machine-learning detection). Page numbers are unavailable in the indexed copy. (LLM, conf=0.72)
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Cites
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., & Zhang, J. C. (2014) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2014.2328098 · Cited by 8,106 · see citing works
Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
Islam, S. M. R., Avazov, N., Dobre, O. A., & Kwak, K.-S. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2621116 · Cited by 2,458 · see citing works
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends
Ding, Z., Lei, X., Karagiannidis, G. K., Schober, R., Yuan, J., & Bhargava, V. K. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2725519 · Cited by 2,392 · see citing works
On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users
Ding, Z., Yang, Z., Fan, P., & Poor, H. V. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2343971 · Cited by 1,831 · see citing works
Internet of Things in the 5G Era: Enablers, Architecture, and Business Models
Palattella, M. R., Dohler, M., Grieco, A., Rizzo, G., Torsner, J., Engel, T., & Ladid, L. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2525418 · Cited by 1,459 · see citing works
1 On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed UsersPreprint
Ding, Z., Yang, Z., Fan, P., Member, S., & Poor, H. V. (2014) · Cited by 1,368 · see citing works
Cooperative Non-orthogonal Multiple Access With Simultaneous Wireless Information and Power Transfer
Liu, Y., Ding, Z., Elkashlan, M., & Poor, H. V. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2549378 · Cited by 918 · see citing works
Ali, S., Tabassum, H., & Hossain, E. (2016) · IEEE Access · doi:10.1109/access.2016.2604821 · Cited by 864 · see citing works
Schulz, P., Matthé, M., Klessig, H., Simsek, M., Fettweis, G. P., Ansari, J., Ashraf, S. A., Almeroth, B., Voigt, J., Riedel, I., et al. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600435cm · Cited by 816 · see citing works
A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead
Buzzi, S., Chih-Lin, I., Klein, T. E., Poor, H. V., Yang, C., & Zappone, A. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2550338 · Cited by 797 · see citing works
Fairness for Non-Orthogonal Multiple Access in 5G Systems
Timotheou, S., & Krikidis, I. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2417119 · Cited by 757 · see citing works
Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations
Shirvanimoghaddam, M., Döhler, M., & Johnson, S. J. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600618 · Cited by 395 · see citing works
Non-Orthogonal Multiple Access in Downlink Coordinated Two-Point Systems
Choi, J. (2014) · IEEE Communications Letters · doi:10.1109/lcomm.2013.123113.132450 · Cited by 330 · see citing works
Edge Computing Aware NOMA for 5G Networks
Kiani, A., & Ansari, N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2796542 · Cited by 298 · see citing works
Enabling Massive IoT in 5G and Beyond Systems: PHY Radio Frame Design Considerations
Ijaz, A., Zhang, L., Grau, M., et al. (2016) · IEEE Access · doi:10.1109/access.2016.2584178 · Cited by 239 · see citing works
Nagisa, O., Kishiyama, Y., & Higuchi, K. (2012) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs.2012.6328413 · Cited by 230 · see citing works
Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey
Luong, N. C., Wang, P., Niyato, D. T., Wen, Y., & Han, Z. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2647981 · Cited by 227 · see citing works
Power Allocation for Max-Sum Rate and Max-Min Rate Proportional Fairness in NOMA
Choi, J. (2016) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2596760 · Cited by 214 · see citing works
Tabassum, H., Hossain, E., & Hossain, M. J. (2017) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2699180 · Cited by 211 · see citing works
Montalban, J., Scopelliti, P., Fadda, M., Iradier, E., Desogus, C., Angueira, P., Murroni, M., & Araniti, G. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700660 · Cited by 97 · see citing works
QoS-Guaranteed Bandwidth Shifting and Redistribution in Mobile Cloud Environment
Misra, S., Das, S., Khatua, M., & Obaidat, M. S. (2014) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2013.19 · Cited by 90 · see citing works
Connectivity maximization for narrowband IoT systems with NOMA
Mostafa, A. E., Zhou, Y., & Wong, V. W. S. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996362 · Cited by 78 · see citing works
Non-Orthogonal Multiple Access for Large-Scale 5G Networks: Interference Aware Design
Ali, K. S., ElSawy, H., Chaaban, A., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2753380 · Cited by 75 · see citing works
Exploiting Social Internet of Things Features in Cognitive Radio
Nitti, M., Murroni, M., Fadda, M., & Atzori, L. (2016) · IEEE Access · doi:10.1109/access.2016.2645979 · Cited by 70 · see citing works
Joint Spectrum and Power Allocation for Inter-Cell Spectrum Sharing in Cognitive Radio Networks
Lee, W.-Y., & Akyildiz, I. F. (2008) · IEEE International Symposium on Dynamic Spectrum Access Networks · doi:10.1109/dyspan.2008.36 · Cited by 42 · see citing works
Non‐Orthogonal Multiple Access ( NOMA ): Concept and Design
Benjebbour, A., Saito, K., Li, A., et al. (2016) · doi:10.1002/9781119116493.ch7 · Cited by 41 · see citing works
Combining NOMA and mmWave Technology for Cellular Communication
Naqvi, S. A. R., & Hassan, S. A. (2016) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2016.7880935 · Cited by 36 · see citing works
Internet of Things Session Management Over LTE—Balancing Signal Load, Power, and Delay
Wang, X., Sheng, M.-J., Lou, Y., et al. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2497230 · Cited by 34 · see citing works
Universal and Dynamic Clustering Scheme for Energy Constrained Cooperative Wireless Sensor Networks
Naeem, M. K., Patwary, M., & Abdel‐Maguid, M. (2017) · IEEE Access · doi:10.1109/access.2017.2655345 · Cited by 27 · see citing works
Self-citationField measurements for practical unlicensed communication in the UHF band
Angueira, P., Fadda, M., Morgade, J., et al. (2015) · Telecommunication Systems · doi:10.1007/s11235-014-9963-7 · Cited by 24 · see citing works
An Auction-Based Spectrum Leasing Mechanism for Mobile Macro-Femtocell Networks of IoT
Chen, X., Xing, L., Qiu, T., et al. (2017) · Sensors · doi:10.3390/s17020380 · Cited by 20 · see citing works
Asaduzzaman, M., Abozariba, R., & Patwary, M. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2782248 · Cited by 13 · see citing works
Self-citationOptimized Narrow-Band M2M Systems for Massive Cellular IoT Communications
Chiti, F., Giacomo, D. D., Fantacci, R., et al. (2016) · Globecom · doi:10.1109/glocom.2016.7842106 · Cited by 7 · see citing works
Federations of connected things for delay-sensitive IoT services in 5G environments
Farris, I., Orsino, A., Militano, L., et al. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996644 · Cited by 7 · see citing works
Minimizing upload latency for critical tasks in cellular-based IoT networks using multiple relays
Hsu, S.-H., Lin, C.-H., Wang, C.-Y., & Chen, W.-T. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997421 · Cited by 5 · see citing works
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works
Self-citation
- Authors
- Abozariba, R., Naeem, M. K., Patwary, M., Seyedebrahimi, M., Bull, P., & Aneiba, A.
- Affiliations
- Lancaster University (GB)
- Southampton Solent University (GB)
- Birmingham City University (GB)
- University of Warwick (GB)
- ORCID
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 29158-29172
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2906551017
- Cited by
- 43
- References
- 35
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 9 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 5
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- Southampton Solent University Digital Library (acceptedVersion)
- BCU Open Access Repository (Birmingham City University) (acceptedVersion)
- NECTAR - Northampton Electronic Collection of Thesis and Research (University of Northampton) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Innovate UK
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- non-orthogonal multiple access, resource allocation, Internet of Things, cellular networks, quality of experience, spectrum sharing, intelligent clustering, subcarrier allocation, radio resource utilization, guaranteed bit rate
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