Dual-Diode Unified SWIPT for High Data Rates with Adaptive Detection

Due to their low-complexity and energy-efficiency, unified simultaneous wireless information and power transfer (U-SWIPT) receivers are especially suitable for low-power Internet of Things (IoT) applications. Towards accurately modeling practical operating conditions, in this study, we provide a unified transient framework for a dual-diode U-SWIPT that jointly accounts for diode nonlinearity and capacitor-induced memory effects. The proposed model accurately describes the inherent time dependence of the rectifier, highlighting its fundamental impact on both energy harvesting (EH) and information decoding (ID) processes. Based on the provided memory-aware model, we design a low-complexity adaptive detector that learns the nonlinear state transition dynamics and performs decision-directed detection with linear complexity. The proposed detection scheme approaches maximum likelihood sequence detection (MLSD) performance in memory-dominated regimes, while avoiding the exponential search required by classical sequence detection. Overall, these results demonstrate that properly exploiting rectifier memory provides a better tradeoff between data rate and reliability for U-SWIPT receivers.

Sub-6 GHz Beam-Reconfigurable Microfluidic Antenna Using Graphene Liquid for 5G Network

As wireless communication systems continue to grow rapidly, high-performance antennas become increasingly crucial for expanding coverage, improving capacity, and enhancing transmission quality. In light of this, research has focused considerable attention on liquid antennas due to their unique characteristics, which include small size, flexibility, reconfigurability and transparency. Recently, graphene liquid has been explored for numerous applications due to its low cost, high conductivity, flexibility, and ease of processing. Specifically for antenna applications, graphene liquid performs better than conventional liquid metal. This paper presents a graphene-liquid antenna with beam reconfiguration ability for sub-6 GHz communication system. The graphene-liquid movement within the microfluidic channel is taken into consideration by the reconfiguration mechanism. The antenna achieves beam reconfiguration in 360° directions with 6 dBi of gain at 5.5 GHz, featuring a wideband impedance bandwidth of 24%. The antenna main beam is specifically reconfigured into six directions (0°, 45°, 135°, 180°, 225° and 315°) at 5.5 GHz. Additionally, in all six reconfigurable scenarios at 5.5 GHz, the antenna provides a stable reflection coefficient. Therefore, for the next generation of wireless communication systems, this novel design of graphene-liquid-based reconfigurable sub-6 GHz antennas holds promise.

Asymmetric Modulation Design for Fluid-Antenna SWIPT Systems

In this work, we propose the design of modulation schemes that improve the rate-energy region of fluid antenna-assisted simultaneous wireless information and power transfer (SWIPT) systems. By considering the nonlinear characteristics of practical energy harvesting circuits, we formulate a dual-objective rate-energy (RE) region optimization problem to jointly maximize the discrete-input mutual information (DIMI) and harvested current. The problem is solved using the epsilon-constraint method and optimized constellations are designed for various energy harvesting thresholds. We then evaluate the performance of the optimized constellations under three different fluid antenna (FA) port selection strategies: (i) Best Port, (ii) Fixed Port, and (iii) Random Port. Our simulation results demonstrate significant performance gains of optimized constellations over conventional constellations in both information rate and energy harvesting.

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.

Joint Power Allocation and Reflecting-Element Activation for Energy Efficiency Maximization in IRS-Aided Communications Under CSI Uncertainty

We study the joint power allocation and reflecting element (RE) activation to maximize the energy efficiency (EE) in communication systems assisted by an intelligent reflecting surface (IRS), taking into account imperfections in channel state information (CSI). The robust optimization problem is mixed integer, i.e., the optimization variables are continuous (transmit power) and discrete (binary states of REs). In order to solve this challenging problem we develop two algorithms. The first one is an alternating optimization (AO) method that attains a suboptimal solution with low complexity, based on the Lambert W function and a dynamic programming (DP) algorithm. The second one is a branch-and-bound (B&B) method that uses AO as its subroutine and is formally guaranteed to achieve a globally optimal solution. Both algorithms do not require any external optimization solver for their implementation. Furthermore, numerical results show that the proposed algorithms outperform the baseline schemes, AO achieves near-optimal performance in most cases, and B&B has low computational complexity on average.

Soft Robotics-Inspired Flexible Antenna Arrays

In this work, a novel soft continuum robot-inspired antenna array is proposed, featuring tentacle-like structures with multiple antenna elements. The proposed array achieves reconfigurability through continuous deformation of its geometry, in contrast to reconfigurable antennas which incur a per-element control. More specifically, the deformation is modeled by amplitude and spatial frequency parameters. We consider a multi-user multiple-input single-output downlink system, whereby the optimal deformation parameters are found to maximize the sum rate in the network. A successive convex approximation method is adopted to solve the problem. Numerical results show that the proposed deformable array significantly outperforms fixed geometry and per-element reconfigurable arrays in sum rate, demonstrating the benefits of structure-level flexibility for next-generation antenna arrays.

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.

A Hybrid Noise Approach to Modelling of Free-Space Satellite Quantum Communication Channel for Continuous-Variable QKD

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative satellite quantum channel model and derive the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, our model incorporates a hybrid noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This paper delves into the variation of SKR with the Signal-to-Noise Ratio (SNR) under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, transmission coefficient, transmission efficiency, the quantum Poissonian noise parameter, and the satellite altitude. These parameters are pivotal in determining the SKR in FSO satellite quantum channels, highlighting the challenges of satellitebased quantum communication. Our work provides a comprehensive framework for understanding and optimizing SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.

Hybrid Semantic-Shannon Communications

Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable "similarity" between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and bit communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-bit transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-bit communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.