1. What are the challenges in VLC-RF heterogeneous networks?
The two primary challenges impacting communication quality in VLC-RF heterogeneous networks are the vulnerability of VLC line-of-sight links to blockage and the co-channel interference (CCI) encountered by mobile terminals (MTs). In the event of a blocked or interrupted VLC link, MTs must perform a vertical handover (VHO) to an alternative communication network. The hybrid application-aware VHO scheme (HA-VHO) introduced by Okine et al. considers the main factors of VLC link blockage and performance degradation, as well as the activity of the video-based real-time application (RTA), to determine the dwelling time for handover. However, using only a video-based RTA as a benchmark may not meet the actual needs of user terminals. Therefore, the proposed Ac-VHO scheme considers different types of RTA (video, VoIP, and web) and incorporates different psychological tolerance times to calculate the best tradeoff for dwell time during handover. Simulation results show that the Ac-VHO scheme significantly reduces the signaling cost of frequent handover and maintains a consistently high integrated Quality of Experience (QoE) in an indoor VLC heterogeneous network environment compared to traditional time-dwelling VHO schemes.
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2. How does Ac-VHO scheme optimize VHO decisions?
The Ac-VHO scheme optimizes VHO decisions by considering various factors such as VLC link quality degradation and the activity of different types of RTA. It combines elements from the D-VHO, dynamic D-VHO, and HA-VHO schemes, resulting in a reduced number of unnecessary handovers. For an MT moving randomly within an indoor space, the scheme optimizes the handover dwell time by taking into account the MT's RTA usage requirements and the impact of movement speed on the QoE. This optimization ensures a stable and highly integrated QoE even when the MT's speed changes. The classification of different types of RTA in the Ac-VHO scheme allows for more efficient VHO decisions, reducing the occurrence of unnecessary handovers and minimizing the ping-pong effect caused by back-and-forth handovers. By considering the link state information and the psychological tolerance time intervals associated with different types of real-time application delay, the Ac-VHO scheme optimizes the timing of VHO execution. This targeted approach ensures that handovers occur at the most suitable moments, taking into account the specific requirements and sensitivities of each application. The proposed scheme incorporates specific delay tolerance intervals and VHO dwell times for each of the three main RTA categories (video, VoIP, and web). This enables the scheme to make optimal switching decisions when the MT encounters different types of active RTA. Ac-VHO dynamically sets the dwell time based on the average interval at which the RTA becomes active, accounting for the impact of RTA activity on QoE. This ensures that the scheme adapts to changes in the RTA's active status and optimizes the QoE accordingly. The proposed scheme classifies RTAs into distinct types and sets RTA delay psychological tolerance intervals based on user groups with varying psychological tolerance levels. This personalized approach allows for handover decisions that align with the actual needs of each user, thereby enhancing the QoE when facing different types of RTA activity.
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3. What are the benefits of incorporating artificial intelligence schemes in VHO mechanisms?
Incorporating artificial intelligence schemes in VHO mechanisms offers several benefits. Firstly, it enables rapid determination of the optimal network and seamless access to it. For instance, the authors in [6, 7] utilize a Markov decision process framework that employs a dynamic approach to balance switching costs and delay requirements, facilitating effective decision making during handovers. Secondly, it enhances the efficiency and performance of VHO operations. In [8], Zeshan A. and his team implement a smart-aware VHO mechanism in a converged VLC and WiFi networking environment by integrating a location-aware coordinator. Lastly, it mitigates the ping-pong effect during network handover, ensuring a smoother and more stable handover process. The authors in [10] develop a scheme based on radial basis function fuzzy neural networks, which effectively addresses the ping-pong effect. Overall, artificial intelligence schemes in VHO mechanisms optimize the handover process and enhance the overall network performance.
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4. How does the VLC indoor localization technique work?
The VLC indoor localization technique determines the dynamics of users' random motions within real indoor VLC heterogeneous network environments. It achieves high positioning accuracy by using LEDs as transmitters. By recording the user's position and velocity at each moment, the technique identifies if the MT is in a CCI area. The technique enhances the random waypoint (RWP) model to simulate the random movement of a single MT in a 6 m x 6 m indoor environment. This model is commonly used in network simulations to describe human mobility in a two-dimensional region. The MT engages in stochastic movement characterized by random displacements within a specified time interval T, governed by maximum velocities Vmax. The direction of movement spans a range from 0 to 2p radians. Following a duration of t for active motion, a pause of tw seconds is introduced before resuming the subsequent sequence of motion patterns. This technique helps in addressing scenarios where MTs move randomly in VLC heterogeneous networking environments and experience higher bit error rates (BER) in CCI regions due to significant drops in the signal-to-interference-plus-noise ratio (SINR).
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