Optimized Handoff with Mobility Prediction Scheme Using HMM for femtocell networks

Cheikh, Ahlam Ben; Ayari, Mouna; Langar, Rami; Pujolle, Guy; Saı̈dane, Leı̈la Azouz

doi:10.1109/icc.2015.7248858

Cited by 24 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ahlam et al [75] proposed HO decision algorithm (OHMP) using HMM predictor to accurately estimate the next femtocell using a) the current and historical movement information, and b) the strength of the received signals of the nearby BSs. The performance of OHMP is validated by comparison with the nearest-neighbor and random BS selection strategies.…”

Section: D) Hidden-markov Model (Hmm)mentioning

confidence: 99%

“…Results show that the number of ping-pong HOs reduce by 7 times when considering dense deployment of femto cells. Results in [75] are demonstrated for a single user scenario only and does not portray futuristic cellular networks with large number of users. To address this concern, same set of authors extended their idea in [76] by incorporating multiple UEs.…”

Section: D) Hidden-markov Model (Hmm)mentioning

confidence: 99%

See 1 more Smart Citation

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

et al. 2020

View full text Add to dashboard Cite

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultradense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

show abstract

Section: D) Hidden-markov Model (Hmm)mentioning

confidence: 99%

Section: D) Hidden-markov Model (Hmm)mentioning

confidence: 99%

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Considering the benefit of mobility prediction, [14] proposed an efficient handover mechanism that reduces the ping-pong effect and maximizes the residence time in a target femtocell. The hidden Markov model (HMM) is used as the prediction scheme, which is based on current and historical movement information of user equipment (UE) as well as signal quality of the femtocell AP to predict the next location of the next assigned femtocell AP.…”

Section: Mobility Predictionmentioning

confidence: 99%

Enhanced handover mechanism using mobility prediction in wireless networks

2020

View full text Add to dashboard Cite

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.

show abstract

“…The authors of [16] use 2nd-order Markov chains to predict both the user's destination and their most likely path to that destination. Some service focused papers, like [4] which uses Hidden Markov Models to assign users to Femtocell Access Points (FAP), appear to implicitly be making use of PDF-predictions to influence their choices. However because they are focused on measuring the effect of predictions on the performance of a specific service, they do not provide a direct analysis of the accuracy of their PDF-predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Opportunistic caching [3] for handovers in a mobile system that utilises a passive optical network backhaul relies upon predictions to efficiently use the restricted memory space available at base stations for caching to improve handovers. Future mobile technologies are shifting toward smaller cell sizes, such as Femtocells, to improve spectrum re-use and mobility predictions are necessary to decrease the amount of unnecessary handovers in these dense small cell topologies [4]. Many location-based services [5], such as shared ride recommendations or targeted ads, are also heavily dependant upon predictions to provide a good quality of service.…”

Section: Introductionmentioning

confidence: 99%