Shuman Huang

This paper investigates handover in hybrid visible light communication (VLC)/radio frequency (RF) networks. In such a network, mobile users are prone to experience frequent handovers (FHOs). To this end, we propose a collaborative online learning-based handover scheme (COLH) in hybrid VLC/RF 5G systems. By selecting the next access point (AP) to which a user should handover, our goal is to make the user–AP connection as long as possible after the handover, defined as a reward that is learned online through a multi-armed bandit (MAB) framework. Unlike previous schemes based on independent and collective learning, first, our scheme dynamically clusters users with similar feedback on a given AP. Second, the users in the same cluster collaborate in estimating the expected reward for that AP, and the one with the maximum expected reward is selected as the next AP. This scheme can be implemented without extensive offline training and location information; thus, its practicality is greatly enhanced. The simulation results show that the proposal outperforms existing benchmarks on reducing handovers.

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms.

Relay cooperation with energy harvesting provides a promising solution to alleviate coverage limitations and energy constraints in hybrid visible light communication (VLC)/radio frequency (RF) Internet of Things (IoT) system with simultaneous lightwave information and power transfer (SLIPT). In the hybrid system, VLC service provider (VLCSP) seeks the cooperation of relay nodes (RNs) for information delivery to a certain end node (EN). However, considering the autonomous behaviors of the RNs, there are two challenging issues to address in facilitating relay cooperation: selfishness and information asymmetry. Hence, this paper proposes a novel incentive scheme for relay cooperation based on agency selling format. Unlike previous incentive scheme designs, first, the VLCSP charges the cooperating RN for energy harvesting per unit energy price. Second, once the information is successfully transmitted to the EN, the VLCSP pays a portion of future revenue as an agency payment to the RN, in a format seen as agency selling. By giving the VLCSP pricing power, it needs to design a mutually agreeable contract that includes a menu of unit energy prices and agency payments. Here, aiming to maximize the VLCSP’s expected utility, we apply a joint adverse selection and moral hazard model to formulate and optimize the contract design problem in the presence of information asymmetry. Then, the optimal contract solution is derived by using Lagrangian dual analysis. Besides, we present two extreme scenarios where only adverse selection or moral hazard model is applied. Numerical results illustrate that the proposed incentive scheme outperforms the considered benchmarks in terms of the expected utility of VLCSP and RNs, as well as the expected social welfare. We also demonstrate the incentive efficiency of our scheme.