Two peer-reviewed research papers have emerged from the 6G-LEADER project, advancing the state of the art in visible light communication (VLC) and multiple-input multiple-output (MIMO) systems by introducing reconfigurable liquid lens-based imaging receivers for next-generation indoor wireless systems. The authors of both papers are Kapila W. S. Palitharathna, Christodoulos Skouroumounis, and Ioannis Krikidis from the University of Cyprus (UCY).
The first paper, titled “Optimization of Liquid Lens-based Imaging Receiver for MIMO VLC Systems” was presented at the IEEE Global Communications Conference (GLOBECOM 2025), held from 8-12 December 2025 in Taipei, Taiwan. This conference paper proposes a tunable liquid convex lens-assisted imaging receiver for MIMO VLC systems operating under realistic conditions, including user mobility and random device orientation. Unlike conventional static optical receivers, the proposed design dynamically adjusts the focal length and orientation of the liquid lens, significantly reducing spatial channel correlation and improving link reliability. A comprehensive three-dimensional optical and geometric channel model is developed, which is beneficial for the future theoretical modeling of liquid lens-enabled VLC systems. Building on this foundation, this paper further focuses on low-complexity optimization strategies for liquid lens-based MIMO VLC receivers. By formulating a bit error rate (BER) minimization problem and introducing practical lens adjustment schemes, the study demonstrates that liquid lenses can robustly enhance system performance even under severe orientation uncertainty. The results confirm that adaptive optical reconfiguration is a key enabler for reliable, high-capacity indoor communications, outperforming static lens solutions across a wide range of operating conditions.
The second paper, titled “Liquid Lens-Based Imaging Receiver for MIMO VLC Systems” was published in the prestigious journal IEEE Transaction on Communications. This paper provides a comprehensive analysis on the channel model for liquid lens-assisted MIMO VLC systems, extending the mathematical framework presented in the first paper. Given the complexity of the channel model, the paper proposes a machine learning (ML)-based prediction and optimization framework to adapt the lens parameters in real time under user mobility and random receiver orientation conditions. In particular, the ML framework predict the receiver position and orientation form the average received power at the MIMO receiver and then optimizes the lens parameters for a future time instance. Numerical results demonstrate substantial performance gains, with BER improvements of more than one order of magnitude compared to conventional receivers across a wide range of user mobility and random receiver orientation scenarios.
Together, these publications validate the 6G-LEADER vision of combining reconfigurable hardware, intelligent optimization, and optical wireless technologies to support future 6G networks. They demonstrate how adaptive VLC receivers can deliver high data rates, robustness, and flexibility, positioning VLC and liquid lens technology as strong complementary solutions for beyond-5G and 6G indoor connectivity.
