Optical Wireless Communications Using Intelligent Walls

This chapter is devoted to discussing the integration of intelligent reflecting surfaces (IRSs), or intelligent walls, in optical wireless communication (OWC) systems. IRS technology is a revolutionary concept that enables communication systems to harness the surrounding environment to control the propagation of light signals. Based on this, specific key performance indicators could be achieved by altering the electromagnetic response of the IRSs. In the following, we discuss the background theory and applications of IRSs and present a case study for an IRS-assisted indoor light-fidelity (LiFi) system. We then highlight some of the challenges related to this emerging concept and elaborate on future research directions.

Flexible LED Index Modulation for MIMO Optical Wireless Communications

The limited bandwidth of optical wireless communication (OWC) front-end devices motivates the use of multiple-input-multiple-output (MIMO) techniques to enhance data rates. It is known that very high multiplexing gains could be achieved by spatial multiplexing (SMX) in exchange for exhaustive detection complexity. Alternatively, in spatial modulation (SM), a single light emitting diode (LED) is activated per time instance where information is carried by both the signal and the LED index. Since only an LED is active, both transmitter (TX) and receiver (RX) complexity reduces significantly while retaining the information transmission in the spatial domain. However, significant spectral efficiency losses occur in SM compared to SMX. In this paper, we propose a technique which adopts the advantages of both systems. Accordingly, the proposed flexible LED index modulation (FLIM) technique harnesses the inactive state of the LEDs as a transmit symbol. Therefore, the number of active LEDs changes in each transmission, unlike conventional techniques. Moreover, the system complexity is reduced by employing a linear minimum mean squared error (MMSE) equalizer and an angle perturbed receiver at the RX. Numerical results show that FLIM outperforms the reference systems by at least 6 dB in the low and medium/high spectral efficiency regions.

Channel Modelling and Error Performance Investigation for Reading Lights Based In-flight LiFi

The new generation of communication technologies are constantly being pushed to meet a diverse range of user requirements such as high data rate, low power consumption, very low latency, very high reliability and broad availability. To address all these demands, 5G radio access technologies have been extended into a wide range of new services. However, there are still only a limited number of applications for RF based wireless communications inside aircraft cabins that comply with the 5G vision. Potential interference and safety issues in on-board wireless communications pose significant deployment challenges. By transforming each reading light into an optical wireless AP, LiFi, could provide seamless on-board connectivity in dense cabin environments without RF interference. Furthermore, the utilization of available reading lights allows for a relatively simple, cost-effective deployment with the high energy and spectral efficiency. To successfully implement the aeronautical cabin LiFi applications, comprehensive optical channel characterization is required. In this paper, we propose a novel MCRT channel modelling technique to capture the details of in-flight LiFi links. Accordingly, a realistic channel simulator, which takes the cabin models, interior elements and measurement based optical source, receiver, surface material characteristics into account is developed. The effect of the operation wavelength, cabin model accuracy and user terminal mobility on the optical channel conditions is also investigated. As a final step, the on-board DCO-OFDM performance is evaluated by using obtained in-flight LiFi channels. Numerical results show that the location of a mobile terminal and accurate aircraft cabin modelling yield as much as 12 and 2 dB performance difference, respectively.

Channel Estimation for Visible Light Communications Using Neural Networks

Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions.