Skipping-based handover algorithm for video distribution over ultra-dense VANET

Next-generation networks will pave the way for video distribution over vehicular Networks (VANETs), which will be composed of ultra-dense heterogeneous radio networks by considering existing communication infrastructures to achieve higher spectral efficiency and spectrum reuse rates. However, the increased number of cells makes mobility management schemes a challenging task for 5G VANET, since vehicles frequently switch among different networks, leading to unnecessary handovers, higher overhead, and ping-pong effect. In this sense, an inefficient handover algorithm delivers videos with poor Quality of Experience (QoE), caused by frequent and ping-pong handover that leads to high packets/video frames losses. In this article, we introduce a multi-criteria skipping-based handover algorithm for video distribution over ultra-dense 5G VANET, called Skip-HoVe. It considers a skipping mechanism coupled with mobility prediction, Quality of Service (QoS)- and QoE-aware decision, meaning the handovers are made more reliable and less frequently. Simulation results show the efficiency of Skip-HoVe to deliver videos with Mean Opinion Score (MOS) 30% better compared to state-of-the-art algorithms while maintaining a ping-pong rate around 2%.publishe

Analytical Review and Study on Various Vertical Handover Management Technologies in 5G Heterogeneous Network

In recent mobile networks, due to the huge number of subscribers, the traffic may occur rapidly; therefore, it is complex to guarantee the accurate operation of the network. On the other hand, the Fifth generation (5G) network plays a vital role in the handover mechanism. Handover management is a prominent issue in 5G heterogeneous networks. Therefore, the Handover approach relocates the connection between the user equipment and the consequent terminal from one network to another. Furthermore, the handover approaches manage each active connection for the user equipment. This survey offers an extensive analysis of 50 research papers based on existing handover approaches in the 5G heterogeneous network. Finally, existing methods considering conventional vertical handover management strategies are elaborated to improve devising effective vertical handover management strategies. Moreover, the possible future research directions in attaining efficient vertical handover management in a 5G heterogeneous network are elaborated

Handover Management in Dense Networks with Coverage Prediction from Sparse Networks

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

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

Research on Radio Environment Maps for Mobility Management in 5G Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.One main feature of the fifth generation (5G) of cellular mobile communications is the deployment of an ultra-dense cellular network architecture with much more cell towers. This will construct a multi-tier 5G network, and make ubiquitous access difficult if maintaining the same approaches to mobility management as in previous generations. In recent years, we have witnessed remarkable advancements in cognitive radio, which provides radio-environmental awareness. This awareness can be exploited to improve system performance in various aspects. This thesis studies how to incorporate radio environment maps (REM) into 5G networks with a particular emphasis on mobility management. Our work begins halfway between Long-Term Evolution (LTE) and 5G. We propose a REM-based handover algorithm that reduces the number of unnecessary handovers in multi-tier networks. The designed handover procedure is fully backward compatible with LTE and exploits the incomplete channel states stored in a REM. We evaluate our method under two different scenarios in which we can deliver the same downlink traffic as current approaches in the literature as well as decrease the overall number of handovers by at least 33% without overloading the backhaul. We also present a geometric model to derive the handover and handover failure regions taking into consideration imperfect location, by finding the optimal prediction time through numerical optimisation. The effect of multiple mobility-management parameters is investigated as well. The proposed scheme achieves a substantial reduction of up to 30% in the number of unnecessary handovers in multi-tier networks. We then propose to use REMs for network optimisation in a dense cellular network and obtain the coverage probability for REM cell association using stochastic geometry. The optimal prediction distance maximises the average ergodic rate, including the penalty incurred by the handovers. Our strategy increases the average ergodic rate extensively by 65% across high-mobility users when compared to state-of-the-art strategies found in the literature. In summary, radio-environmental awareness in mobile cellular networks has not been wholly addressed yet. This thesis introduces REMs as an enabling technology that contributes to the mitigation of the number of unnecessary handovers and capacity growth for mobility management in 5G networks and beyond

Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds