Learning Indoor Environment for Effective LiFi Communications: Signal Detection and Resource Allocation

Abstract

Seamless light fidelity (LiFi) communications in a realistic indoor environment faces a number of important challenges including the loss of line-of-sight (LOS) link due to the random orientation of mobile devices or blockage by users or furniture in the room. These effects make the frequency response of the LiFi channel highly environment-dependent. The dynamic nature of the channel is not only dependent on the geometrical configuration of the room but also on the user behavior. In this paper, we investigate the hypothesis that, deep learning (DL) based LiFi communication techniques can effectively learn the distinct features of the indoor environment and user behavior to provide superior performance compared to conventional channel estimation techniques particularly when access to real-time channel state information (CSI) is restricted. In particular, we implement DL methods in two different problems, namely, signal detection and resource allocation for orthogonal frequency division multiplexing (OFDM) LiFi systems. To effectively test this hypothesis, a realistic LiFi channel impaired by random device orientation and blockage is simulated considering different geometrical configurations and user behavior in an indoor environment, e.g. presence or lack of furniture and varying user distribution defined by a conditional hotspot model. The simulation results confirm that DL-based LiFi systems with partial CSI are able to offer close performance to the optimal signal detection and resource allocation with perfect CSI. Moreover, the DL-based techniques demonstrate substantial gain against conventional benchmark schemes that employ channel estimation algorithms such as least squares (LS) or minimum mean square error (MMSE) and this gain increases in a more realistic or complex indoor environment, e.g. room with furniture or a hotspot scenario.

Keywords

• Visible light communication
• deep learning
• feed forward neural network
• long short-term memory (LSTM)
• DCO-OFDM
• signal detection