Constellation Design for Nonlinear Unified SWIPT Receiver Channels with Memory

Unified receivers (URs) have emerged as a promising architecture for simultaneous wireless information and power transfer (SWIPT), since a common rectifying front-end enables information decoding (ID) and energy harvesting (EH) from the same rectified output. However, rectification is nonlinear due to the diode, while the capacitor introduces memory across symbols, making constellation design over the channel challenging. In this paper, we study constellation design for nonlinear UR-SWIPT channels in both memoryless and memory regimes. First, we propose a tractable unified rectification model that captures both (i) the nonlinear steady-state mapping and (ii) the asymmetric capacitor charging/discharging dynamics under transient operation. To isolate the impact of rectification with memory on ID, we study the information-based design. In this setting, we develop a state-adaptive policy with an algorithmic constellation design that accounts for the rectifier state and shapes the constellation in the observation domain. By approximating the rectifier state distribution, we derive a closed-form average symbol error rate (SER) expression and characterize the rate-reliability (R-R) tradeoff. We then seek constellations that minimize the SER under average transmit power and EH constraints. We address the resulting energy-constrained setting in the memoryless regime using an autoencoder-based framework that embeds the nonlinear rectification model as a differentiable channel block. Numerical results validate the proposed models, demonstrate the impact of memory on the R-R tradeoff, and show how learned constellations adapt to EH requirements in the rate-energy tradeoff.

Large-scale Tunable Liquid Lens-assisted VLC Systems under Random Receiver Orientation

This paper investigates the performance of tunable liquid lens (TLL)-assisted receivers in large-scale visible light communication (VLC) systems under random receiver orientation. A simple electrowetting-based TLL architecture is proposed, capable of dynamically steering the incident optical signal toward the photodiode receiver by adjusting the orientation of the liquid interface. The proposed architecture enhances the desired signal reception while mitigating interference from neighboring access points (APs). The spatial distribution of APs is modeled using a Matérn hard-core point process, whereas receiver orientation is characterized by uniformly distributed azimuth angles and Gaussian-distributed polar angles. Furthermore, a tractable mathematical optical channel model is developed to capture the combined effects of AP/receiver locations, receiver orientation, and lens adjustment angles on the VLC channel gain. Based on this framework, three lens orientation strategies, namely best signal reception (BSR), closest LED selection, and vertical upward lens orientation, are proposed to improve system performance under dynamic receiver conditions. Using stochastic geometry tools, exact and approximate analytical expressions for the outage probability are derived for each scheme. Numerical results verify the accuracy of the developed analysis and demonstrate that the proposed TLL-assisted receiver architecture significantly improves the robustness of VLC systems under severe receiver orientation fluctuations and dense AP deployments. In particular, the BSR scheme reduces the outage probability by $57.1\%$ compared with conventional fixed-lens receivers at an AP height of $3.5$ m and AP density of $0.2~\text{m}^{-2}$. The presented analytical framework and numerical results provide useful design insights for the deployment of future TLL-assisted VLC networks.

A Family of Hybrid Beyond-Diagonal RIS Architectures: Design and Performance Analysis

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) extend conventional diagonal RISs by allowing inter-element coupling, thereby enlarging the set of attainable scattering matrices and improving the achievable signal-to-noise ratio (SNR). On the other hand, hybrid active/passive RISs use reflect-type power amplifiers in a fraction of the elements to alleviate the multiplicative path loss. In this paper, we bring these two ideas together and introduce a \emph{family of hybrid BD-RIS architectures}, in which the surface is partitioned into two reflecting subsurfaces (RSs), each adopting either a passive or an active group-connected BD-RIS design. We derive a closed-form SNR-maximizing solution that combines, for every BD-RIS group, Takagi's factorization of a certain complex symmetric matrix with an optimal per-group amplification factor that satisfies the reflect-power budget. Three architectures within the proposed family (active/passive, fully-connected-active/sub-connected-active, and sub-connected-active/sub-connected-active hybrid BD-RIS) are studied. Numerical results in a single-input single-output (SISO) link with blocked direct path show that the proposed hybrid BD-RIS architectures attain the same or higher receive SNR than their diagonal counterparts while using significantly fewer reflect-type amplifiers.

Secure and Robust Beamforming Design for STAR-RIS-aided MU-MIMO ISAC Systems

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) offer a transformative approach for integrated sensing and communication (ISAC) systems, particularly for enhancing physical layer security (PLS). This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user (MU) multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers. To optimize system security, we formulate a worst-case robust beamforming problem to maximize the secrecy rate. This formulation jointly designs the active transmit beamforming at the BS and the passive reflection, transmission coefficients at the STAR-RIS, adheres to transmit power budgets, user quality-of-service (QoS) thresholds, sensing signal-to-interference-plus-noise ratio (SINR) requirements, maximum tolerable eavesdropping leakage, and practical phase shifts constraints. To efficiently tackle the formulated problem, we develop an alternating optimization (AO) algorithm. Specifically, the S-procedure is employed to solve semi-infinite channel uncertainty constraints, while semidefinite relaxation (SDR) and penalty convex-concave programming (CCP) are applied to obtain tractable suboptimal solutions. Extensive simulation results validate the efficacy of the proposed framework and demonstrate significant improvement in spectral efficiency compared to conventional reflecting-only RIS (R-RIS) systems under stringent sensing conditions.

Improving Reliability of Hybrid Bit-Semantic Communications for Cellular Networks

Semantic communications (SemComs) have been considered as a promising solution to reduce the amount of transmitted information, thus paving the way for more energy-and spectrum-efficient wireless networks. Nevertheless, SemComs rely heavily on the utilization of deep neural networks (DNNs) at the transceivers, which limit the accuracy between the original and reconstructed data and are challenging to implement in practice due to increased architecture complexity. Thus, hybrid cellular networks that utilize both conventional bit communications (BitComs) and SemComs have been introduced to bridge the gap between required and existing infrastructure. To facilitate such networks, in this work, we investigate reliability by deriving closed-form expressions for the outage probability of the network. Additionally, we propose a generalized outage probability through which the cell radius that achieves a desired outage threshold for a specific range of users is calculated in closed form. Additionally, to consider the practical limitations caused by the specialized dedicated hardware and the increased memory and computational resources that are required to support SemCom, a semantic utilization metric is proposed. Based on this metric, we express the probability that a specific number of users select SemCom transmission and calculate the optimal cell radius for that number in closed form. Simulation results validate the derived analytical expressions and the characterized design properties of the cell radius found through the proposed metrics, providing useful insights.

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.