Conditional handover (CHO) has been introduced in 5G to improve mobility robustness, namely, to reduce the number of handover failures by preparing target Base Stations (BSs) in advance and allowing the user to decide when to make a handover. This algorithm constantly prepares and releases BSs, thereby adapting to the fast changing radio condition. A user might make a handover to a distant BS that has a favorable channel only for a short time due to signal fluctuations. This increases the handover rate and might result in a Radio Link Failure (RLF) afterwards. Moreover, the constant preparation and release of BSs leads to an increased exchange of control messages between the user, the serving BS and all target BSs. Hence, there is a need to carefully select the target BSs. Therefore, we propose the Enhanced CHO (ECHO) scheme that uses trajectory prediction to prepare the BSs along the user's path. To achieve this, we also propose a Sequence to Sequence (Seq2Seq) mobility prediction model. ECHO with only one prepared BS (ECHO-1) outperforms CHO with three prepared BSs. ECHO-1 reduces the handover rate by 23 percent and the RLF rate by 77 percent, while also reducing the number of control messages in the network by 69 percent.
A LiFi-RF heterogeneous network can provide additional capacity to standalone wireless technologies due to their non-interfering nature. However, due to the properties of the short-range LiFi channel, the network is prone to transient channel variations that result in frequent, unnecessary handovers. This handover process creates an overhead and can result in the loss of connection. To ensure a stable connection for all users, a low complexity resource allocation algorithm, that considers the loss due to handovers, is proposed to minimize the number of handovers. This algorithmic approach is evaluated with simulations. For scenarios with unavoidable handovers, a system approach to manage vertical handovers is proposed to minimize the vertical handoff overhead and to offer a seamless interface switch, thereby resulting in a stable network. This protocol is implemented in hardware and the results show a negligible overhead.
Line of Sight (LoS) blockages are a common occurrence in densely deployed cellular networks, as is the case with 5G. This leads to a significant deterioration in the signal quality on the user side. Modeling LoS blockages is crucial for simulations to obtain reliable results, but also challenging since LoS might appear and disappear occasionally because how often an LoS happens depends on the environment and the user speed. To capture LoS blockages in a realistic manner for a particular scenario in a given environment, we propose to model blockages geometrically by considering all static and mobile objects in the environment such as buildings, cars, busses and humans, including self-blockages from the user. This enables a better evaluation of the metrics of interest, such as handover rate. In dense network deployments, users make frequent handovers, which deteriorates their experience and reduces the network capacity. Also, operators should strive to provide fairness in resource allocation to all users as well as to guarantee a minimum Quality of Service (QoS). Thus, handover decisions should be considered jointly with resource allocation. To that end, in this paper, we formulate an optimization problem that provides proportional fair resource allocation, while simultaneously reducing the handover rate, and providing a minimum data rate for all users at all times. It is an integer non-linear program, which is NP-hard. We relax it to a linear problem, which allows us to find a near-optimal user-to-BS assignment and resource proportion for every user quickly. We compare the result from our optimal and relaxed approaches with other two benchmarks showing that it outperforms them considerably in terms of fairness, handover rate reduction and users' rate satisfaction. Moreover, our relaxed approach performs within above 90% of the optimum and reduces the handover rate up to 40%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.