Characterizing multiplexing and diversity in visual MIMO

Abstract

Abstract-Mobile optical wireless has so far been limited to very short ranges for high data rate systems. It may be feasible to overcome the data rate limitations over large transmission range in optical wireless through camera receivers and light emitting transmitter arrays through a concept what we call "visual MIMO". In this concept multiple transmit elements of a light emitting array (LEA) are used as transmitters to communicate to the individual pixel elements of the camera which act as multiple receive elements to create the visual MIMO channel. Multiplexing information over parallel data channels albeit be very similar to RF MIMO in concept, the visual MIMO approach dramatically differs in its characterization. In visual MIMO since the received signal is essentially the image of the transmitting element, the perspective distortions in the visual channel dominate over some of the important properties of a RF wireless channel such as distance based attenuation and multipath fading. Some of the prominent perspective distortions include the reduction in the size of the image with distance and skew/rotation in the image due to angular view. Further lens blur (typically due to focus imperfection or jerks while capturing the image) can also significantly depreciate the image quality. In this paper we will detail how MIMO techniques such as multiplexing and diversity are characterized based on the effect of perspective distortions. Based our visual MIMO channel model we will derive the analytical channel capacity of the visual MIMO channel and using the same we illustrate the significance of parameters such as distance, viewing angle and blur in characterizing multiplexing and diversity in visual MIMO.

Optimal radiometric calibration for camera-display communication

“…e = (e r , e g , e b ). Therefore the emitted light I as a function of wavelength λ for a given pixel (x, y) on the electronic display is given by I(x, y, λ) = ρ r e r (λ, θ) + ρ g e g (λ, θ) + ρ b e b (λ, θ), (1) or I(x, y, λ) = ρ · e(λ, θ).…”

Mathematical modeling for calibrating illuminated image in image sensor communication

“…For secure communication, it is very conducive as the detection of transmitted data is very tough in steganography process. Likewise, it is considered as interference-free communication due to the highly directional property of the camera [5]. Moreover, electromagnetic radiation is absent in this communication which is very detrimental to human health.…”

M-ary Double-layer Optical Camera Communication For Intelligent Transport System

“…Recently, many fundamental studies have focused on analyzing system channel capacity [6], studying communication factors [7], constructing an appropriate system model and establishing some experimental prototypes under different scenarios [8]. Double-layer (overlay coding) OCC system was proposed by Japanese scholars, applied in intelligent transport system (ITS).…”