This study presents a real-time hardware implementation of a novel physical layer security algorithm developed for visible-light communications (VLCs) based on precoded spatial modulation (SM). The demonstration was carried out on a low-cost 70 cm × 40 cm × 40 cm miniature room model with four light-emitting diodes (LEDs) as a test-bed for conducting experiments in the field of VLC. The test-bed is a 10:1 shrunk replica of a conventional room and can be built with simple office supplies totaling <$10, excluding drive and collection optoelectronic components. While being cost-friendly, the test-bed also allows for (i) integrating optical components and (ii) carving desired window and door patterns with different cardboard color tones. Hence, the effects of reflections from different colored walls and the effect of external light sources can be observed on the performance of the secure VLC system. We successfully demonstrate the operation of the zero-forcing precoder and the SM on the built set up to provide robust and secure communication among the transmitting LEDs and the receivers, representing the legitimate user and the eavesdropper, for the first time in the literature. The secrecy capacity improvement is also noted, validating the proposed approach in realistic environments.
In this paper, we investigate channel modeling for visible light communications (VLC) using non-sequential ray tracing simulation tools. We create three dimensional realistic simulation environments to depict indoor scenarios specifying the geometry of the environment, the objects inside, the reflection characteristics of the surface materials as well as the characteristics of the transmitter and receivers, i.e., LED sources and photodioes. Through ray tracing simulations, we compute the received optical power and the delay of direct/indirect rays which are then used to obtain the channel impulse response (CIR). Following this methodology, we present CIRs for a number of indoor environments including empty/furnished rectangular rooms with different sizes and wall/object materials (e.g., plaster, gloss paint, wood, aluminum metal, glass) assuming deployment of both single and multiple LED transmitters. We further quantify multipath channel parameters such as delay spread and channel DC gain for each configuration and provide insights into the effects of indoor environment parameters (e.g., size, wall/object materials, etc.), transmitter/receiver specifications (e.g., single vs. multiple transmitters, location, rotation etc.) on the channel.
Recently a new approach to Bayesian image segmentation has been proposed by Bouman and Shapiro, based on a multiscale random field (MSRF) model along with a sequential MAP (SMAP) estimator as an efficient and computationally feasible alternative to MAP segmentation. But their method is restricted to image models with observed pixels that are conditionally independent given their class labels. In this paper, we follow the approach of and extend the SMAP method for a more general class of random field models. The proposed scheme is recursive, yields the exact MAP estimate, and is readily applicable to a broad range of image models. We present simulations on synthetic images and conclude that the generalized algorithm performs better and requires much less computation than maximum likelihood segmentation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.