Browsing by Author "Abbasi, Qammer"

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Handover Management in Dense Networks with Coverage Prediction from Sparse Networks
(IEEE, 2019) Mollel, Michael; Ozturk, Metin; Kisangiri, Michael; Kaijage, Shubi; Onireti, Oluwakayode; Imran, Muhammad; Abbasi, Qammer
Millimeter Wave (mm-Wave) provides high bandwidth and is expected to increase the capacity of the network thousand-fold in the future generations of mobile communications. However, since mm-Wave is sensitive to blockage and incurs in a high penetration loss, it has increased complexity and bottleneck in the realization of substantial gain. Network densification, as a solution for sensitivity and blockage, increases handover (HO) rate, unnecessary and ping-pong HO’s, which in turn reduces the throughput of the network. On the other hand, to minimize the effect of increased HO rate, Time to Trigger (TTT) and Hysteresis factor (H) have been used in Long Term Evolution (LTE). In this paper, we primarily present two different networks based on Evolved NodeB (eNB) density: sparse and dense. As their name also suggests, the eNB density in the dense network is higher than the sparse network. Hence, we proposed an optimal eNB selection mechanism for 5G intra-mobility HO based on spatial information of the sparse eNB network. In this approach, User Equipment (UE) in the dense network is connected only to a few selected eNBs, which are delivered from the sparse network, in the first place. HO event occurs only when the serving eNB can no longer satisfy the minimum Signal-to-Noise Ratio (SNR) threshold. For the eNBs, which are deployed in the dense network, follow the conventional HO procedure. Results reveal that the HO rate is decreased significantly with the proposed approach for the TTT values between 0 ms to 256 ms while keeping the radio link failure (RLF) at an acceptable level; less than 2% for the TTT values between 0 ms to 160 ms. This study paves a way for HO management in the future 5G network.
Intelligent handover decision scheme using double deep reinforcement learning
(Elsevier B.V., 2020-10) Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri,; Zoha, Ahmed; Imran, Muhammad Ali; Abbasi, Qammer
Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional -learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models.
A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
(IEEE, 2021-03-19) Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri; Hussain, Sajjad; Imran, Muhammad Ali; Abbasi, Qammer
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed.
A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
(IEEE, 2021-03-14) Mollel, Michael; Abubakar, Attai; Ozturk, Metin; Kaijage, Shubi; Kisangiri, Michael; Imran, Muhammad; Abbasi, Qammer
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of- the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed