Optimization of Liquid Lens-based Imaging Receiver for MIMO VLC Systems

In this paper, a liquid lens-based imaging receiver is proposed for multiple-input multiple-output (MIMO) visible light communication (VLC) systems. By dynamically adjusting the focal length and orientation angles of the liquid lens, the spatial correlation between MIMO channel gains is reduced, leading to enhanced bit-error rate (BER) performance. Unlike static lenses, liquid lenses offer adaptability in dynamic conditions, including user mobility and random receiver orientation. An accurate mathematical framework is developed to model the channel gains of the proposed system, and an optimization problem is formulated to minimize its BER. Due to the complexity of the resulting channel model, two lens adjustment schemes, namely, ($i$) the CLS scheme, and ($ii$) the VULO scheme are introduced. Numerical results demonstrate that the proposed liquid lens-based system offers substantial BER improvements over conventional static lens-based receivers across a wide range of random receiver orientation conditions. Specifically, at a random receiver orientation variance of $10^{\circ}$, the BER is improved from $4\times 10^{-2}$ to $5\times 10^{-4}$ by employing the proposed liquid lens.

Outage Probability Analysis of Tunable Liquid Lens-assisted VLC Systems

This paper presents a tunable liquid lens (TLL)-assisted indoor mobile visible light communication system. To mitigate performance degradation caused by user mobility and random receiver orientation, an electrowetting cuboid TLL is used at the receiver. By dynamically controlling the orientation angle of the liquid surface through voltage adjustments, signal reception and overall system performance are enhanced. An accurate mathematical framework is developed to model channel gains, and two lens optimization strategies, namely ($i$) the best signal reception (BSR), and ($ii$) the vertically upward lens orientation (VULO) are introduced for improved performance. Closed form expressions for the outage probability are derived for each scheme for practical mobility and receiver orientation conditions. Numerical results demonstrate that the proposed TLL and lens adjustment strategies significantly reduce the outage probability compared to fixed lens and no lens receivers across various mobility and orientation conditions. Specifically, the outage probability is improved from $1\times 10^{-1}$ to $3\times 10^{-3}$ at a transmit power of $12$ dBW under a $8^{\circ}$ polar angle variation in random receiver orientation using the BSR scheme.

Liquid Lens-Based Imaging Receiver for MIMO VLC Systems

In this paper, we consider a tunable liquid convex lens-assisted imaging receiver for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems. In contrast to existing MIMO VLC receivers that rely on fixed optical lenses, the proposed receiver leverages the additional degrees of freedom offered by liquid lenses via adjusting both focal length and orientation angles of the lens. This capability facilitates the mitigation of spatial correlation between the channel gains, thereby enhancing the overall signal quality and leading to improved bit-error rate (BER) performance. We present an accurate channel model for the liquid lens-assisted VLC system by using three-dimensional geometry and geometric optics. To achieve optimal performance under practical conditions such as random receiver orientation and user mobility, optimization of both focal length and orientation angles of the lens are required. To this end, driven by the fact that channel models are mathematically complex, we present two optimization schemes including a blockwise machine learning (ML) architecture that includes convolution layers to extract spatial features from the received signal, long-short term memory layers to predict the user position and orientation, and fully connected layers to estimate the optimal lens parameters. Numerical results are presented to compare the performance of each scheme with conventional receivers. Results show that a significant BER improvement is achieved when liquid lenses and presented ML-based optimization approaches are used. Specifically, the BER can be improved from $6\times 10^{-2}$ to $1.4\times 10^{-3}$ at an average signal-to-noise ratio of $30$ dB.