Stochastic geometry analysis of multiple access, mobility, and learning in cellular networks
| dc.contributor.author | Salehi, Mohammad | |
| dc.contributor.examiningcommittee | Bellili, Faouzi (Electrical and Computer Engineering) Irani, Pourang (Computer Science) Ding, Zhiguo (University of Manchester) | en_US |
| dc.contributor.supervisor | Hossain, Ekram (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2021-07-12T16:17:05Z | |
| dc.date.available | 2021-07-12T16:17:05Z | |
| dc.date.copyright | 2021-07-05 | |
| dc.date.issued | 2021 | en_US |
| dc.date.submitted | 2021-07-05T15:08:36Z | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Use cases of future wireless networks (e.g. fifth-generation [5G] networks and beyond [B5G]) will have service-quality requirements including higher data rates than today's networks for enhanced mobile broadband (eMBB), minimal latency and high network availability for ultra-reliability low-latency connection (URLLC), and massive access support for machine-type communications (mMTC). Also, 5G and B5G are expected to support communications for highly mobile scenarios with applications in new vertical sectors such as unmanned aerial vehicle (UAV) and autonomous car. Therefore, 5G and B5G cellular systems require a set of new technology enablers and solutions. In this thesis, we address some of the challenges of future wireless networks. In particular, we develop novel analytical models as well as methods, which will enable us to obtain insights into the performance of large-scale cellular networks and optimize network parameters. Non-orthogonal multiple access (NOMA) is a promising multiple access technique that enables massive connectivity and reduces the delay. We develop an analytical framework to derive the distribution of transmission success probabilities, meta distribution, for uplink and downlink NOMA. We also investigate the accuracy of distance-based ranking, instead of instantaneous signal power-based ranking, in the successive interference cancellation (SIC) at the NOMA receiver. Sojourn time, the time duration that a mobile user stays within a cell, is a mobility-aware parameter that can significantly impact the performance of mobile users and it can also be exploited to improve resource allocation and mobility management methods in the network. We derive the distribution and mean of the sojourn time in multi-tier cellular networks. Future wireless networks will exploit data-driven machine learning techniques for improving network management as well as service provisioning. Due to privacy and communication issues, learning at a centralized location (for example, at a base station) by collecting data from the mobile devices may not be always feasible. Federated learning is a machine learning setting where the centralized location trains a learning model using remote devices. Federated learning algorithms cannot be employed in real-world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. We propose a federated learning algorithm that is suitable for wireless networks. | en_US |
| dc.description.note | October 2021 | en_US |
| dc.identifier.citation | M. Salehi, H. Tabassum and E. Hossain, "Meta Distribution of SIR in Large-Scale Uplink and Downlink NOMA Networks," in IEEE Transactions on Communications, vol. 67, no. 4, pp. 3009-3025, April 2019, doi: 10.1109/TCOMM.2018.2889484. | en_US |
| dc.identifier.citation | M. Salehi, H. Tabassum and E. Hossain, "Accuracy of Distance-Based Ranking of Users in the Analysis of NOMA Systems," in IEEE Transactions on Communications, vol. 67, no. 7, pp. 5069-5083, July 2019, doi: 10.1109/TCOMM.2019.2904987. | en_US |
| dc.identifier.citation | M. Salehi and E. Hossain, "On Coverage Probability in Uplink NOMA With Instantaneous Signal Power-Based User Ranking," in IEEE Wireless Communications Letters, vol. 8, no. 6, pp. 1683-1687, Dec. 2019, doi: 10.1109/LWC.2019.2936854. | en_US |
| dc.identifier.citation | H. Tabassum, M. Salehi and E. Hossain, "Fundamentals of Mobility-Aware Performance Characterization of Cellular Networks: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2288-2308, thirdquarter 2019, doi: 10.1109/COMST.2019.2907195. | en_US |
| dc.identifier.citation | M. Salehi and E. Hossain, "Stochastic Geometry Analysis of Sojourn Time in Multi-Tier Cellular Networks," in IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1816-1830, March 2021, doi: 10.1109/TWC.2020.3036894. | en_US |
| dc.identifier.citation | M. Salehi and E. Hossain, "Handover Rate and Sojourn Time Analysis in Mobile Drone-Assisted Cellular Networks," in IEEE Wireless Communications Letters, vol. 10, no. 2, pp. 392-395, Feb. 2021, doi: 10.1109/LWC.2020.3032596. | en_US |
| dc.identifier.citation | M. Salehi and E. Hossain, "Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks," in IEEE Transactions on Communications, doi: 10.1109/TCOMM.2021.3081746. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35738 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | 5G | en_US |
| dc.subject | Cellular networks | en_US |
| dc.subject | Stochastic geometry | en_US |
| dc.subject | Multiple access | en_US |
| dc.subject | NOMA | en_US |
| dc.subject | Mobility | en_US |
| dc.subject | Sojourn time | en_US |
| dc.subject | Federated learning | en_US |
| dc.title | Stochastic geometry analysis of multiple access, mobility, and learning in cellular networks | en_US |
| dc.type | doctoral thesis | en_US |