Sub-Decimeter VLC 3D Indoor Localization With Handover Probability Analysis

Abstract

This paper addresses the problem of Visible Light Communication (VLC)-based indoor localization and handover, where mobile users communicate with hybrid VLC/mmWave Access Points (APs). The VLC system consists of multiple Light-Emitting Diodes (LEDs) treated as VLC transmitters, multiple Photodiodes (PDs) on the user's smart device, and multiple mmWave Radio Frequency (RF) transmitters used as complementary APs for the VLC system in the case of blockage. We propose a Convolutional Neural Network (CNN)-based algorithm consisting of offline and online modes. In the offline mode, we gather a data set by dividing the environment into fixed-sized elements where the received VLC signal along with the data attained from the smart device at each element represent a sample to train a CNN model for indoor localization. In the online mode, users employ the received VLC signals to estimate their locations. We then propose a virtual soft handover process according to the Coordinated Multiple Point (CoMP) transmission, where the HandOver Margin (HOM) and Waiting Time (WT) are dynamically set based on the change in Signal-to-Noise-Ratio (SNR) values in consecutive time slots. We derive a closed-form expression for the average effective throughput during the handover process, which shows the algorithm's superior performance compared to conventional soft and hard handovers. Simulation results show an average positioning error of 4.31 centimeters for the proposed localization algorithm in a 5 × 4 × 3 m 3 smart environment.

Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

“…This 6G multi-dimensional expansion surfaces serious challenges related to threats in cyberspace [15,16]. Hence, a 6G-based unconventional communication environment will generate several fundamental security threats related to Confidentiality, Integrity and Availability (CIA), known as the CIA triad [17]. Furthermore, IP-based network architecture will also entail specific conventional vulnerabilities.…”

Reinforcement Learning-Based Visible Light Positioning and Handover Scheme With Stereo-Camera

“…Wang et al proposed an adaptive optical wireless communication handover mechanism for 6G networks with hybrid architectures by employing RL to optimize the waiting time before the handover process and reduce the interruption time incurred by the handovers [19]. Seyed et al [20] employed multiple millimeter-wave radio frequency (RF) transmitters as complementary APs for an optical wireless communication system in the case of blockage. The study proposes a convolutional neural network (CNN)-based algorithm consisting of offline and online modes to dynamically tune the waiting time (WT) and handover margin (HOM) based on alternating the SNR values related to the serving of cooperating VLC transmitters in consecutive time slots.…”