  This paper presents an approach for visible light communication-based indoor
positioning using compressed sensing. We consider a large number of light
emitting diodes (LEDs) simultaneously transmitting their positional information
and a user device equipped with a photo-diode. By casting the LED signal
separation problem into an equivalent compressed sensing framework, the user
device is able to detect the set of nearby LEDs using sparse signal recovery
algorithms. From this set, and using proximity method, position estimation is
proposed based on the concept that if signal separation is possible, then
overlapping light beam regions lead to decrease in positioning error due to
increase in the number of reference points. The proposed method is evaluated in
a LED-illuminated large-scale indoor open-plan office space scenario. The
positioning accuracy is compared against the positioning error lower bound of
the proximity method, for various system parameters.
