User-Centric Blind Interference Alignment Design for Visible Light Communications

Visible light communications (VLC) are considered as a key technology for future wireless communications. In order to mitigate the interference, several transmit precoding (TPC) schemes have been proposed for VLC. However, beyond the need for channel state information and backhaul links, the TPC schemes are subject to additional constraints given by the features of the optical channel such as ensuring a real and non-negative transmitted signal or a low correlation among users. Besides, the traditional network centric (NC) design, i.e., considering only the position of the transmitters, leads to rigid transmission schemes for VLC networks due to the small and confined coverage of the optical transmitters.In this paper, we consider blind interference alignment (BIA) schemes for VLC, which solve the aforementioned issues, based on the concept of reconfigurable photodetector. In this context, we propose a user-centric (UC) clustering strategy based on the K-means algorithm where the users are treated as an active element of the network instead of a mere endpoint. For the proposed UC design, we derive two BIA schemes based on the connectivity of the clusters; a straightforward scheme considering each cluster as a broadcast channel referred to as KM-sBIA and a scheme that is flexible to the connectivity of each user within the cluster referred to as KM-topBIA. The simulation results show that the proposed schemes outperform the use of classical TPC or other BIA-based schemes considering both NC and UC approach.This work was supported by the Spanish National Project TERESA-ADA (MINECO/AEI/FEDER, UE) under Grant TEC2017-90093-C3-2-

AI-Driven Resource Allocation in Optical Wireless Communication Systems

Visible light communication (VLC) is a promising solution to satisfy the extreme demands of emerging applications. VLC offers bandwidth that is orders of magnitude higher than what is offered by the radio spectrum, hence making best use of the resources is not a trivial matter. There is a growing interest to make next generation communication networks intelligent using AI based tools to automate the resource management and adapt to variations in the network automatically as opposed to conventional handcrafted schemes based on mathematical models assuming prior knowledge of the network. In this article, a reinforcement learning (RL) scheme is developed to intelligently allocate resources of an optical wireless communication (OWC) system in a HetNet environment. The main goal is to maximise the total reward of the system which is the sum rate of all users. The results of the RL scheme are compared with that of an optimization scheme that is based on Mixed Integer Linear Programming (MILP) model.Comment: 6 pages, 2 Figures, Conferenc

Resource Allocation in IRS-aided Optical Wireless Communication Systems

One of the main challenges facing optical wireless communication (OWC) systems is service disconnection in high blockage probability scenarios where users might lose the line of sight (LoS) connection with their corresponding access points (APs). In this work, we study the deployment of passive reflecting surfaces referred to as Intelligent Reflecting Surfaces (IRSs) in indoor visible light communication (VLC) to boost users signal to noise ratio (SNR) and ensure service continuity. We formulate an optimization problem to allocate APs and the mirrors of IRSs to users such that the sum rate is increased. The results show a 35% increase in the sum rate of the IRS-aided OWC system compared to the sum rate achieved by only considering the LoS channel components. The results also shows that the deployment of IRSs improves the sum rate under LoS blockage