A VLC-based beacon location system for mobile applications

This paper proposes a low cost and complexity indoor location and navigation system using visible light communications and a mobile device. LED lamps work as beacons transmitting an identifier code so a mobile device can know its location. Experimental designs for transmitter and receiver interfaces are presented and potential applications are discussed

VLC-based light-weight portable user interface for in-house applications

Advances in solid-state lighting have overcome common limitations on optical wireless such as power needs due to light dispersion. It's been recently proposed the modification of lamp's drivers to take advantages of its switching behaviour to include data links maintaining the illumination control they provide. In this paper, a remote access application using visible light communications is presented that provides wireless access to a remote computer using a touchscreen as user interfac

Optimising signal detection techniques in wireless ultraviolet communication systems

Wireless ultraviolet (UV) communication is regarded as a promising supplement to conventional wireless communications. The challenges lie in the inter-symbol-interference (ISI) and the time-varying channel impulse response (CIR), which deteriorate the detection/estimation of transmitted symbols from the received signals. The existing coherent detection schemes that leverage CIR estimation for ISI compensation, fall into two camps. They present either an overhead burden of pilot sequences and computational complexity or poor CIR acquisition that further hinders the detection performance. The aim of this thesis is to design non-coherent detection schemes that can transform the ISI contaminated sequential detection process into discrete binary or multiple detection framework. This is achieved by extracting the UV communication signal-related geometrical features that are inherently resistant to ISI. Then, one-dimensional and high-dimensional non-coherent detection schemes are proposed, by designing optimal linear and high-dimensional combinations of these features that minimize the theoretical bit error rate (BER). Both theoretical and simulation verification are performed to validate the proposed scheme, showing the comparable detection accuracy of the state-of-the-art coherent schemes but at the expense of lower computational complexity. To further expand the scope of the ISI-resistant features, machine learning is employed to discover nonlinear features that can express hidden relations from received signals to transmitted symbols. This is done by (i) a supervised neural network (NN) based detector, and (ii) a more explainable Parzen window technique based NN to approximate the detection likelihoods. For future work, deep reinforcement learning will be utilized to explore better ISI-resistant features for detection purposes. As such, by casting the complex sequential detection into the concise discrete detection framework, and combining the manual and machine learning-based ISI-resistant feature construction, this work provides a novel idea, not only for UV communications but can also be applied to other communication and signal detection scenarios suffering from ISI