New vertical handover prediction schemes for LTE-WLAN heterogeneous networks

Hosny, Khalid M.; Khashaba, Marwa M.; Khedr, Walid I.; Amer, Fathy A.

doi:10.1371/journal.pone.0215334

Cited by 18 publications

(11 citation statements)

References 51 publications

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“…However, details of results are missing from the proposed work. The proposed work in [17], propose new vertical handover prediction strategies with aim to provide efficient prediction method without putting stresses on the mobile station and entire network. The prediction strategies use two independent thresholds.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2021

IJWMN

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Recently, many generations of mobile networks have changed from one transition to another transition. The mode of transition from the first generation (1G) to the fifth generation (5G) is characterized by a lot of performance challenges such as delay, speed of the users, mobility, and variety of services. Currently, different from prior generations, 5G is not only concerned with the mobile networks but also, with various applications and different services such as health sector, transportation, energy consumption, safety well as Smart City. All these services are incorporated by 5G. In this paper, we proposed a call admission control (CAC) algorithm with an efficient handoff for both 4G and 5G networks. Simulation is used to evaluate the effectiveness of the proposed algorithm, and the obtained results indicate it considerably performs better than do other algorithms based on valuable metrics such as data throughput, call blocking probability (CBP), and call dropping probability (CDP).

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Section: Related Workmentioning

confidence: 99%

Untitled

2021

IJWMN

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“…This procedure is known as location update. The handover delay is one of the main problems in wireless networks [48]. Conventionally, a handover is carried out based on a predefined threshold of the Reference Signal Receiver Power (RSRP), the Reference Signal Receiver Quality (RSRQ), among other signal strength parameters [49].…”

Section: Handover Predictionmentioning

confidence: 99%

When 5G Meets Deep Learning: A Systematic Review

Santos¹,

Endo²,

Sadok³

et al. 2020

Preprint

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This last decade, the amount of data exchanged in the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. But for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include: high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements. Among these we list ultra-densification, millimeter Wave usage, antennas with massive multiple-input multiple-output (MIMO), antenna beamforming to increase spacial diversity, edge/fog computing, among others. Each of these technologies brings its own design issues and challenges. For instance, ultra-densification and MIMO will increase the complexity to estimate channel condition and traditional channel state information (CSI) estimation techniques are no longer suitable due to the complexity of the new scenarios. As a result, new approaches to evaluate network condition such as by continuously collecting and monitoring key performance indicators become necessary. Timely decisions are needed to ensure the correct operation of such network. In this context, deep learning (DL) models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contributions, this paper presents a systematic review about how DL is being applied to solve some 5G issues. We examine data from the last decade and the works that addressed diverse 5G problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using DL models in 5G scenarios and identify several issues that deserve further consideration.

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“…This action reduces the power consumption and selects the most optimal network. Similarly, [17] proposed two handover prediction schemes for LTE-WLAN heterogeneous networks. The first scheme depends on the quality of all signals among mobile stations, and the second scheme is a multi-criteria prediction based on the SNR value and the stations' bandwidth.…”

Section: Mobility Predictionmentioning

confidence: 99%

Enhanced handover mechanism using mobility prediction in wireless networks

2020

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The rapid increase in the usage of the mobile internet has led to a great expansion of cellular data networks in order to provide better quality of service. However, the cost to expand the cellular network is high. One of the solutions to provide affordable wireless connectivity is the deployment of a WiFi access point to offload users' data usage. Nevertheless, the frequent and inefficient handover process between the WiFi AP and cellular network, especially when the mobile device is on the go, may degrade the network performance. Mobile devices do not have the intelligence to select the optimal network to enhance the quality of service (QoS). This paper presents an enhanced handover mechanism using mobility prediction (eHMP) to assist mobile devices in the handover process so that users can experience seamless connectivity. eHMP is tested in two wireless architectures, homogeneous and heterogeneous networks. The network performance significantly improved when eHMP is used in a homogeneous network, where the network throughput increases by 106% and the rate of retransmission decreases by 85%. When eHMP is used in a heterogeneous network, the network throughput increases by 55% and the retransmission rate decreases by 75%. The findings presented in this paper reveal that mobility prediction coupled with the multipath protocol can improve the QoS for mobile devices. These results will contribute to a better understanding of how the network service provider can offload traffic to the WiFi network without experiencing performance degradation. OPEN ACCESSCitation: Yap K-L, Chong Y-W, Liu W (2020) Enhanced handover mechanism using mobility prediction in wireless networks. PLoS ONE 15(1): e0227982. https://doi.org/10.

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New vertical handover prediction schemes for LTE-WLAN heterogeneous networks

Cited by 18 publications

References 51 publications

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When 5G Meets Deep Learning: A Systematic Review

Enhanced handover mechanism using mobility prediction in wireless networks

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