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.

Fluid Antenna-aided Full Duplex Communications: A Macroscopic Point-Of-View

The synergy of fluid-based reconfigurable antenna (FA) technology and full-duplex (FD) communications can be jointly beneficial, as FD can enhance the spectral efficiency of a point-to-point link, while the new degree of freedom offered by the FA technology can be exploited to handle the overall interference. Hence, in this paper, an analytical framework based on stochastic geometry is developed, aiming to assess both the outage and average sum-rate performance of large-scale FA-aided FD cellular networks. In contrast to existing studies, where perfect channel state information is assumed, the developed framework accurately captures the impact of channel estimation (CE) on the performance of the considered network deployments, as well as the existence of residual loop-interference (LI) at the FD transceivers. Particularly, we focus on a limited coherence interval scenario, where a novel sequential linear minimum-mean-squared-error-based CE method is performed for all FA ports and LI links, followed by data reception from the port with the strongest estimated channel. By using stochastic geometry tools, analytical expressions for the outage and the average sum-rate performance are derived. Our results reveal that FA-aided FD communications experience an improved average sum-rate performance of around 45\% compared to conventional FD communications.

Fluid Antenna with Linear MMSE Channel Estimation for Large-Scale Cellular Networks

The concept of reconfigurable fluid antennas (FA) is a potential and promising solution to enhance the spectral efficiency of wireless communication networks. Despite their many advantages, FA-enabled communications have limitations as they require an enormous amount of spectral resources in order to select the most desirable position of the radiating element from a large number of prescribed locations. In this paper, we present an analytical framework for the outage performance of large-scale FA-enabled communications, where all user equipments (UEs) employ circular multi-FA array. In contrast to existing studies, which assume perfect channel state information, the developed framework accurately captures the channel estimation errors on the performance of the considered network deployments. In particular, we focus on the limited coherence interval scenario, where a novel sequential linear minimum mean-squared error (LMMSE)-based channel estimation method is performed for only a very small number of FA ports. Next, for the communication of each BS with its associated UE, a low-complexity port-selection technique is employed, where the port that provides the highest signal-to-interference-plus-noise-ratio is selected among the ports that are estimated to provide the strongest channel from each FA. By using stochastic geometry tools, we derive both analytical and closed-form expressions for the outage probability, highlighting the impact of channel estimation on the performance of FA-based UEs. Our results reveal the trade-off imposed between improving the network's performance and reducing the channel estimation quality, indicating new insights for the design of FA-enabled communications.

An Evolutionary Game for Mobile User Access Mode Selection in sub-$6$ GHz/mmWave Cellular Networks

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-$6$ GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility{.} According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-$6$ GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.

FD-JCAS Techniques for mmWave HetNets: Ginibre Point Process Modeling and Analysis

In this paper, we study the co-design of full-duplex (FD) radio with joint communication and radar sensing (JCAS) techniques in millimeter-wave (mmWave) heterogeneous networks (HetNets). Spectral co-existence of radar and communication systems causes mutual interference between the two systems, compromising both the data exchange and sensing capabilities. Focusing on the detection performance, we propose a cooperative detection technique, which exploits the sensing information from multiple base stations (BSs), aiming at enhancing the probability of successfully detecting an object. Three combining rules are considered, namely the \textit{OR}, the \textit{Majority} and the \textit{AND} rule. In real-world network scenarios, the locations of the BSs are spatially correlated, exhibiting a repulsive behavior. Therefore, we model the spatial distribution of the BSs as a $\beta$-Ginibre point process ($\beta$-GPP), which can characterize the repulsion among the BSs. By using stochastic geometry tools, analytical expressions for the detection performance of $\beta$-GPP-based FD-JCAS systems are expressed for each of the considered combining rule. Furthermore, by considering temporal interference correlation, we evaluate the probability of successfully detecting an object over two different time slots. Our results demonstrate that our proposed technique can significantly improve the detection performance when compared to the conventional non-cooperative technique.