Programmable wireless environments (PWEs) have emerged as a key paradigm for next-generation communication networks, aiming to transform wireless propagation from an uncontrollable phenomenon into a reconfigurable process that can adapt to diverse service requirements. In this framework, pinching-antenna systems (PASs) have recently been proposed as a promising enabling technology, as they allow the radiation location and effective propagation distance to be adjusted by selectively exciting radiating points along a dielectric waveguide. However, most existing studies on PASs rely on the idealized assumption that pinching-antenna (PA) positions can be continuously adjusted along the waveguide, while realistically only a finite set of pinching locations is available. Motivated by this, this paper analyzes the performance of two-state PASs, where the PA positions are fixed and only their activation state can be controlled. By explicitly accounting for the spatial discreteness of the available pinching points, closed-form analytical expressions for the outage probability and the ergodic achievable data rate are derived. In addition, we introduce the pinching discretization efficiency to quantify the performance gap between discrete and continuous pinching configurations, enabling a direct assessment of the number of PAs required to approximate the ideal continuous case. Finally, numerical results validate the analytical framework and show that near-continuous performance can be achieved with a limited number of PAs, offering useful insights for the design and deployment of PASs in PWEs.

As 6G wireless networks seek to enable robust and dynamic programmable wireless environments (PWEs), reconfigurable intelligent surfaces (RISs) have emerged as a cornerstone for controlling electromagnetic wave propagation. However, realizing the potential of RISs for demanding PWE applications depends on precise and real-time user localization, especially in scenarios with random receiver orientations and inherent hardware imperfections. To address this challenge, we propose a novel optical localization framework that integrates conventional ceiling-mounted LEDs with light-emitting reconfigurable intelligent surfaces (LeRISs). By leveraging the spatial diversity offered by the LeRIS architecture, the framework introduces robust signal paths that improve localization accuracy and reduce errors under varying orientations. To this end, we derive a system of equations for received signal strength-based localization that accounts for random receiver orientations and imposes spatial constraints on LED placement, ensuring unique and reliable solutions. Finally, our simulation results demonstrate that the proposed framework achieves precise beam control and high spectral efficiency even for RISs with large number of reflecting elements by tightly coupling the localization process with the beamforming configuration, allowing accurate direction estimation and robust PWE operation.

Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RISes) to actively shape electromagnetic (EM) propagation, enabling advanced control over wireless channels. Beyond improved performance in B5G/6G networks, this control also introduces new security capabilities. Exploiting this, we propose RF-Fencing: a service that selectively suppresses EM signals toward eavesdroppers while preserving reliable communication for legitimate users, thereby significantly enhancing network covertness. Building on that, in this paper, we introduce SHIELD, the first RF-Fencing algorithm that partitions the PWE into Signal Suppression Areas (SSAs) and Signal Delivery Areas (SDAs) through on-the-fly merging of RIS configurations. Extensive EM analysis confirms SHIELD’s effectiveness in preventing wardens from intercepting critical information and achieving covert communications with minimal impact on legitimate users. Moreover, SHIELD can serve also as a jamming-mitigation mechanism and is applicable across various frequency bands and RIS designs.

The evolution toward sixth-generation wireless networks introduces the concept of intelligent and reconfigurable environments designed to support advanced services. Achieving this paradigm shift requires addressing the limitations of traditional wireless systems, particularly their inability to effectively counteract path loss or adapt to diverse user scenarios. Pinching antenna systems (PASs) have emerged as a promising solution, enabling dynamic control over path loss by leveraging dielectric waveguides to support low-loss transmission at high frequencies. This work presents an analytical framework for assessing the reliability of PASs through the derivation of closed-form expressions for the outage probability under both free-space and waveguide attenuation. In addition, a rigorous formulation is provided for the optimal positioning of the pinching antennas to maximize signal reception, taking into account the trade-off between waveguide losses and spatial separation. Simulation results validate the impact of waveguide attenuation on performance and show that PASs consistently outperform conventional architectures in terms of outage behavior, confirming their suitability for next-generation wireless networks.

Accurate indoor localization is crucial for enabling 6G applications, such as smart homes, augmented reality, and advanced healthcare systems. Optical wireless systems utilizing Light-Emitting Diodes (LEDs) offer centimeter-level accuracy due to their dominant line-of-sight (LoS) characteristics. However, most existing methods assume fixed and known user orientations, limiting their practical applicability in real-world scenarios with random orientations. In this paper, we propose an LED-based visible light positioning (VLP) scheme that accurately, through closed form equations, localizes users with arbitrary orientations using optical received signal strength (RSS) measurements. The proposed method achieves high localization accuracy, without requiring hardware for orientation measurements. Finally, an analytical expression for the error is derived, while Monte Carlo simulations validate the scheme's performance, highlighting the critical role of the parameters of the system in achieving accurate localization.

In the rapidly advancing field of sixth generation (6G) wireless networks, where the achievement of ultra-high data rates and energy efficiency is crucial, this study introduces the concept of super constellations as a solution to meet these pressing requirements. However, when higher-order modulations are employed, the system becomes increasingly susceptible to Gaussian phase noise (GPN), resulting in significant performance degradation. To address this issue, we propose a novel modulation scheme, called super amplitude phase shift keying (SAPSK), which is specifically designed to improve resilience against GPN. Furthermore, recognizing the critical need for efficient detection methods in high-order constellations, we present a detection framework that is not only capable of mitigating the detrimental effects of GPN, but also provides a foundation for the development of low-complexity detection algorithms. Specifically, we introduce the generalized polar distance detector (GPD-D), a detection metric that approximates maximum likelihood detection (MLD) in GPN-affected channels while introducing structured decision regions. Building on the GPD-D, SAPSK formulates a hexagonal lattice that provides improved energy efficiency and is further supported by a detection algorithm with Ό (1) complexity, ensuring fast and accurate symbol detection. Finally, the superiority of SAPSK is demonstrated through extensive numerical simulations, which show that SAPSK has significant advantages in terms of symbol error probability (SEP) over other conventional modulation schemes.

Programmable Wireless Environments aim to render the communication environment a controllable, software-defined medium. Reconfigurable Intelligent Surfaces (RISes) are the key enabling technology, which can offer the real-time capability to manipulate impinging waves. RISes are expected to be widely deployed in B5G/6G networks to serve a large number of users simultaneously. Despite numerous analyses highlighting the benefits of utilizing previously unexploitable propagation factors through the use of RISes, there is a lack of analysis regarding their relation to the concept of network resource, their allocation to users/stakeholders and their fair pricing. Thus, this paper models RISes as networked resources. Based on this definition, the PRIME algorithm is proposed, the first algorithm for RIS resource allocation and joint pricing. PRIME strives for proportionality between the offered end-user performance level and the corresponding resource pricing, promoting fairness. The algorithm is validated via full-wave electromagnetic simulations and applies to multiple RIS functionalities and frequency bands.

The advent of sixth-generation (6G) wireless communication systems represents a transformative leap in global connectivity, moving from traditional internet of things (IoT) frameworks to an advanced artificial intelligence of things (AIoT) paradigm. This evolution presents significant challenges, primarily due to exponential growth in data volume, complexity, and latency requirements. To address these challenges, over-the-air (OTA) computation has emerged as a breakthrough approach by integrating computational processes directly into the communication framework, overcoming the inefficiencies of traditional separate designs. In this study, we explore the integration of OTA computation with reconfigurable intelligent surfaces (RISs) within multi-cell multiple-input multiple-output (MIMO) networks. RIS technology enhances signal propagation, mitigates interference, and optimizes wireless coverage, thereby complementing the OTA computation paradigm’s ability to facilitate real-time data aggregation and processing. Specifically, we propose a novel joint optimization framework aimed at minimizing the mean squared error (MSE) in multi-cell environments. This framework addresses the complexity of beamforming design through an innovative power-iteration-based majorization-minimization approach and a successive alignment technique. In addition, we perform asymptotic analysis to elucidate the performance benefits of large-scale MIMO and RIS configurations. We also consider the fairness of MSE computation throughout the multi-cell system. Finally, numerical simulations validate the effectiveness of the proposed methods and provide additional insights based on the asymptotic analysis.

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

In sixth-generation (6G) wireless communication systems, the coexistence of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) services requires the investigation of appropriate multiple access schemes. In this direction, this paper delves into the hybrid eMBB-mMTC policy, focusing on the implications of non-orthogonality in contention-based access schemes and combining the strengths of slotted ALOHA and successive interference cancellation to address the challenges of this hybrid policy. Closed-form expressions for the outage probability, which are crucial for deriving the throughput of the sources, are presented and integrated into a comprehensive analysis. Finally, simulation results are used to validate the provided theoretical expressions, highlighting the effects of random source deployment within the hybrid eMBB-mMTC framework and highlighting the potential and challenges of this policy in shaping the future of 6G wireless communication systems.

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode-and-forward (DF) protocol, aiming to maximize the network’s minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported byzero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.

Massive IoT deployments in smart cities pose a significant challenge to the data collection due to the harsh wireless channel conditions of dense urban environments. Aerial reconfigurable intelligent surfaces (RIS) carried by Unmanned Aerial Vehicles (UAVs) can improve the communication thanks to their high mobility that provides line-of-sight propagation. In this paper, we investigate the impact of the aerial RIS in the data collection by deriving the outage probability of the randomly-deployed devices, while taking into account the imperfect channel state and the UAV fluctuations. Furthermore, we study the effects on the network reliability of two hybrid automatic repeat request protocol types, i.e., incremental redundancy and code combining, as well as on the average throughput. Finally, we provide useful insights regarding the RIS characteristics that guarantee the optimal network performance.