Reconfigurable intelligent surface (RIS) has emerged as a key enabler for providing signal coverage, energy efficiency, reliable communication, and physical layer security (PLS) in next-generation wireless communication networks. This paper investigates an artificial noise (AN)-driven RIS-assisted secure communication system. The RIS is partitioned into two segments, where the first segment is configured to direct the communication signal (CS) toward the legitimate user (Bob), and the other one is configured to steer the AN toward the eavesdropper (Eve). To this end, iterative and discrete Fourier transform-based algorithms are developed for practical RIS phase shift optimization. The power allocation between the CS and the AN signals is optimized in such a way that the secrecy capacity (SC) is maximized while limiting Eve's channel capacity. The proposed PLS framework is evaluated through both simulations and software defined radio based testbed experiments. The results demonstrate promising improvements in the SC, highlighting the potential of AN-driven RIS-assisted PLS for practical deployments.

Determining the optimal phase configurations of reconfigurable intelligent surface (RIS) elements typically requires complex channel estimation procedures with high pilot overhead, creating a bottleneck for real-time deployment in time-varying wireless environments. In this paper, we propose a digital twin (DT)-driven framework for RIS phase shift optimization that eliminates extensive signaling overhead associated with estimating high-dimensional RIS channels. Leveraging the NVIDIA Sionna ray-tracing library, we construct a DT of the physical environment based on a three-dimensional map. The proposed system utilizes the location information of the transceivers to compute the optimal RIS phase shift configurations within the DT. These computationally generated configurations are then transferred to a physical RIS prototype. Experimental results demonstrate that the phase configurations obtained from the DT significantly enhance the received signal power in the physical environment, validating the fidelity of the ray-tracing model and the feasibility of the proposed optimization strategy.

Low-altitude wireless networks (LAWN) envision a reconfigurable 3D network capable of supporting mission-critical aerial operations. This paper presents a reconfigurable intelligent surface (RIS)-assisted LAWN to establish a reliable communication with an unmanned aerial vehicle (UAV) across varying wireless channel conditions and signal blockages. A low complexity stripe-based RIS phase shift optimization framework is proposed to simultaneously enhance communication reliability and provide passive sensing capability for UAV tracking under 3D mobility. Unlike high-complexity optimization approaches, the proposed method leverages the inherent structural phase-gradient of the RIS adjacent elements to significantly reduce the search space for calculating and updating the RIS configuration as the UAV moves. The analysis and simulation results demonstrate that the proposed framework outperforms conventional benchmarks in convergence speed and computational efficiency, while maintaining robust, high signal-to-noise-ratio (SNR) connectivity even in the presence of phase estimation errors and low SNR regimes. In addition, the measurement experiments using a real RIS prototype in an outdoor campus environment are performed to demonstrate the practical viability of the proposed approach.

This paper introduces and analyzes Spatial Phase Manifold Communications (SPMC), a paradigm that facilitates joint communication and sensing (JCAS) over Local Oscillator (LO) free receiver. Information is embedded in, and recovered from, the relative spatial phase between antennas. In contrast to conventional coherent receivers that rely on LOs and on channel estimation/equalization, SPMC exploits antenna-domain correlation to form a baseband observable that is a function of inter-antenna phase differences. Since these phase differences are fundamentally tied to Direction-of-Arrival (DoA) and vice-versa, the formulation recasts communication and sensing as inference over the unit-circle manifold and thus naturally supports JCAS decomposition, i.e., data and spatial sensing are encoded and recovered through DoA signatures. We develop a comprehensive framework comprising: (i) a manifold-domain signal model and corresponding phase-alphabet design; (ii) an LO-free quadrature spatial-correlator receiver architecture that resolves the phase-sign ambiguity without requiring an LO; and (iii) an analysis of error probability and sensing precision, including robustness to phase noise. The proposed paradigm is particularly suited to massive Internet-of-Things (IoT) deployments, for which hardware simplicity, LO distribution cost, power consumption, and seamless sensing integration are critical, especially at millimeter-wave and higher carrier frequencies.

This paper investigates the performance of faster-than-Nyquist (FTN) signaling within the context of hyper-reliable low-latency communications (HRLLC), specifically focusing on the challenges imposed by the short-packet regime. While traditional Nyquist-based systems maintain symbol orthogonality to prevent inter-symbol interference (ISI), FTN intentionally introduces ISI to achieve higher transmission rates. While many existing FTN studies assume perfect channel state information, this assumption is often impractical for mission-critical HRLLC. In such scenarios, a portion of the limited packet length must be reserved for pilot symbols to ensure reliable estimation. To characterize the achievable error probability while accounting for imperfect channel estimation in the short-blocklength regime, we derive the random coding union bound with parameter s (RCUs) under mismatched decoding for FTN systems. The numerical results demonstrate that FTN provides up to a 2 SNR gain over Nyquist signaling, provided that power allocation and pilot overhead are optimized. These findings highlight the necessity of non-asymptotic analysis for designing efficient, next-generation HRLLC-FTN systems.

This letter proposes a novel mathematical framework for the statistical characterization of reconfigurable intelligent surface (RIS)-mounted high-altitude platform station (HAPS)-assisted MIMO systems over cascaded Rician fading channels. Due to the inherent coupling introduced by the RIS, the resulting cascaded channel does not satisfy the independence assumptions required for conventional Wishart-based modeling, which motivates a tractable alternative approach. By adopting a line-of-sight (LoS)-aligned precoding strategy, the received signal-to-noise ratio (SNR) is represented as a non-central quadratic form with a structured covariance matrix. Exploiting this structure, a saddle point approximation (SPA)-based framework is developed to characterize the SNR distribution. Closed-form expressions for the probability density function (PDF), cumulative distribution function (CDF), and outage probability are derived. The proposed framework further incorporates practical RIS hardware impairments, including discrete phase shifts and phase-dependent amplitude responses. The accuracy of the proposed analysis is validated through Monte Carlo simulations.

Integrated sensing and communication (ISAC) has attracted significant attention in recent years, driven by the growing demand for spectrum efficiency and the additional opportunities arising from leveraging communication signals for sensing. Among applications of ISAC, localization in cellular networks stands out as particularly important and is projected to become even more critical as cellular deployments become increasingly dense. This paper proposes a cellular localization method based on distributed ISAC that leverages physical random access channel (PRACH) preambles to estimate the positions of multiple targets, such as uncrewed aerial vehicles (UAVs), using direction of arrival (DoA) estimation. The simulation and laboratory measurement experiments demonstrate promising localization results of the distributed ISAC approach. Furthermore, the Cramér-Rao lower bound (CRLB) on localization error is derived as a theoretical benchmark for evaluating the estimation accuracy of the proposed method across different cell geometries.

Motivated by the potential of reconfigurable intelligent surfaces (RIS) for 6G networks, this paper extends our previous work by presenting a real-time implementation of an RIS-assisted downlink multi-user non-orthogonal multiple access (NOMA) system, with a focus on outage probability (OP) and achievable capacity. Two RIS configuration strategies, namely weighting-based and partitioning-based, are comparatively evaluated through both real-time experiments and Monte Carlo simulations. The results show that RIS-assisted transmission significantly enhances the received signal power for both users, particularly for the far user, and that the weighting-based approach consistently outperforms the partitioning-based approach in terms of both OP and achievable capacity.

Driven by the emerging capabilities of reconfigurable intelligent surfaces (RIS), this letter presents the implementation and experimental evaluation of an RIS-assisted downlink multi-user non-orthogonal multiple access (NOMA) network. Two RIS configuration strategies are implemented and compared. The first is a partitioning-based approach, primarily adopted in many existing theoretical RIS-NOMA studies but lacking experimental validation, where each user is served by a distinct subset of RIS elements. The second is a proposed weighting-based approach, which leverages power-domain NOMA and successive interference cancellation (SIC) principles to iteratively maximize the weighted sum of the received signal powers, expressed in the dB domain, across all users according to their power allocation coefficients. The performance of both strategies is experimentally evaluated under real-time conditions in terms of bit error rate and further validated through Monte Carlo simulations. The results demonstrate that the proposed weighting-based approach achieves 8-9 dB power improvement and 1-2 dB performance gain over the partitioning-based approach at high signal-to-noise ratios.

For high-throughput applications such as ultra-high-definition video streaming and immersive extended-reality, perceptual quality rather than bit-level accuracy defines the primary performance criterion and provides a more informative and spectrally efficient objective than strict bitwise reconstruction. This is particularly relevant in millimeter-wave (mmWave) and sub-Terahertz (sub-THz) systems, where path loss, short channel coherence times and phase noise introduce severe fluctuations that degrade link spectral efficiency. We propose an extension to conventional Adaptive Modulation and Coding (AMC) framework that incorporates perceptual quality awareness into link adaptation. In this framework, the decision metric is a Perceptual Quality Indicator (PQI) derived from the Structural Similarity Index Measure (SSIM). The receiver employs a Denoising Convolutional Neural Network (DnCNN) denoiser to enhance post-decoding image quality before feedback estimation. The resulting perceptual metric replaces the standard Channel Quality Indicator (CQI) in the AMC loop, enabling adaptation to maximize spectral efficiency while satisfying a perceptual-fidelity constraint. Experiments on a 5G-compliant mmWave testbed demonstrate up to a twofold gain in spectral efficiency while maintaining perceptual fidelity, underscoring the potential of perception-optimized link adaptation.

The vision of a smart wireless factory (SWF) demands highly flexible, low-latency, and reliable connectivity that goes beyond conventional wireless solutions. Reconfigurable intelligent surface (RIS)-empowered communications, when integrated with the open radio access network (O-RAN) architectures, have emerged as a promising enabler to meet these challenging requirements. This article introduces the methodology for the orchestration of RIS with xApps (ORIX), bringing the RIS technology into the O-RAN ecosystem through xApp-based control for SWF environments. ORIX features three key components: an O-RAN-compliant RIS service model for dynamic configuration, an RIS channel simulator that supports 3GPP indoor factory models with multiple industrial scenarios, and practical RIS optimization strategies with finite-resolution control. Together, these elements provide a realistic end-to-end emulation platform for evaluating RIS placement, control, and performance in SWF environments prior to deployment. The presented case study demonstrates how ORIX enables the evaluation of achievable performance gains, exploration of trade-offs among key RIS design parameters, and identification of deployment strategies that balance system performance with practical implementation constraints. By bridging theoretical advances with industrial feasibility, ORIX lays the groundwork for RIS-assisted O-RAN networks to power next-generation wireless communication in industrial scenarios.

Short channel coherence time and oscillator phase noise are two major impairments in millimeter-wave (mmWave) communication systems. Several studies indicate that a substantial fraction of the available bandwidth may be required as overhead to compensate for these impairments, potentially exceeding one third of the total capacity. In this paper, we study Direction-Shift Keying (DSK), a variant of Spatial Modulation (SM), which encodes information in the Direction-of-Arrival (DoA) rather than in the signal amplitude or phase. DSK is implemented over a Distributed Antenna System (DAS), enabling angular resolvability of the transmitted signals. We first derive the structure of the optimal detector for a mobile device equipped with M antennas. We then introduce and characterize the Direction Coherence Time (DCT), defined as the temporal interval over which the DoA remains approximately invariant. Our analysis shows that DCT scales with d/v (transmitter-receiver distance over velocity), whereas the conventional Channel Coherence Time (CCT) scales with lambda /v , revealing a coherence-time gain proportional to d/lambda , which can exceed several orders of magnitude in mmWave systems. Furthermore, we show that the proposed detector inherently cancels receiver phase noise, eliminating the need for explicit phase-noise tracking. Simulation results validate the analytical findings and demonstrate the robustness of DSK in mobile mmWave environments in the presence of phase noise.

In terms of mobility considerations of next generation wireless networks, localization of moving entities to realize the benefits of Integrated Sensing and Communication (ISAC) based systems becomes a critical issue. To that end, this paper proposes and lays down two new ISAC methods to localize moving targets by utilizing Physical Random Access Channel (PRACH) preambles generated from Zadoff-Chu (ZC) sequences through sensing signals across synchronized base stations (gNBs). The first method utilizes Time Difference of Arrival (TDoA) method, while the second method relies on the back projection on a multistatic setting. The first approach estimates the target position by employing TDoA values derived from ZC sequence-based correlation peaks. On the other hand, the back projection method for a multistatic setup, aligns the phase of channel responses from multiple gNBs on a spatial grid and fuses them to generate a reflectivity map, with the strongest peaks indicating the target location. The simulation results indicate that targets are localized with an error of approximately 40 m and 65 m at the 95% confidence level using the TDoA and the back projection algorithms along the signal to noise ratio (SNR) of -20 dB, respectively. Notably, these results are achieved using the existing 3GPP waveform configuration, including the limited PRACH channel bandwidth and through the cooperation of neighboring gNBs. For a better performance, the 3GPP standard body can make PRACH bandwidth and power flexible parameters, allowing larger bandwidths to be allocated for ISAC applications and/or allowing inter gNB communication.

Fast and low-overhead beam management is a critical requirement for the practical deployment of non-terrestrial networks (NTNs) operating at millimeter-wave and higher frequencies. In this paper, we propose a radar-assisted beam selection framework for NTNs that limits the set of candidate beams by utilizing spatial sensing information such as the angle-of-departure (AoD) and distance estimations. To provide theoretical insight into the expected worst-case overhead, we conduct a probabilistic analysis under idealized conditions, where an approximation of the worst-case beam selection overhead is proposed and its statistics are derived under Gaussian error. Additionally, the proposed framework is applied to a physical-layer security (PLS) scenario by leveraging the radar's capability to detect passive targets that represent unintended users. The simulation results show that the unintended user's power is suppressed below -135 dBm, while an additional beamforming gain of roughly 2 dB is attained for the legitimate users.

Narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is a key enabler for massive Internet of Things (IoT) in 6G, but in low Earth orbit (LEO) scenarios, large and time-varying Doppler shifts generate carrier frequency offset (CFO) beyond the correction range of standard user equipment (UE), making initial downlink synchronization a major bottleneck. This paper analyzes Doppler characteristics in realistic NB-IoT LEO scenarios, reviews Doppler mitigation strategies, and proposes a standard-compliant, low-overhead search-space optimization method for downlink acquisition. Results under realistic LEO conditions with real-time measurements show reduced acquisition overhead while maintaining synchronization reliability, supporting NB-IoT adaptation to 6G NTN deployment.

Hardware acceleration has emerged as a key research topic for supporting computationally intensive signal processing and artificial intelligence applications in 6G research and development studies. This paper presents an RF Network on Chip (RFNoC) based hardware acceleration framework that offloads key physical layer procedures to a field programmable gate array (FPGA). The proposed design accelerates procedures, including low density parity check codes (LDPC) encoding and decoding, rate matching and unmatching, interleaving and deinterleaving, scrambling and descrambling, and log likelihood ratio estimation. The accelerator is integrated directly into the OpenAirInterface radio access network software, enabling simultaneous use of the FPGA as driver of the radio front end and a high throughput accelerator. The proposed system is validated through real time experiments with a commercial smartphone successfully connecting to the network. The implementation results demonstrate that a throughput of about 900 Mbps is achiievable using a moderate FPGA resource utlization.

By intelligently reconfiguring wireless propagation environment, reconfigurable intelligent surfaces (RISs) can enhance signal quality, suppress interference, and improve channel conditions, thereby serving as a powerful complement to multiple-input multiple-output (MIMO) systems. However, the joint optimization of RIS phase shifts and the selection of MIMO precoder vectors for next-generation communication systems remains largely unexplored. This paper proposes a low-complex singular value-based RIS optimization strategy, where the phase shifts are configured to maximize the dominant singular values of the cascaded channel matrix, and the corresponding singular vectors are utilized for precoding. The proposed precoder selection is independent of the number of transmission layers and does not require mutual information computation across subbands, thereby reducing time complexity. The simulation results show that the proposed precoder selection method generally outperforms the conventional approach and sometimes achieves similar performance. In addition, for multilayer transmission and two-dimensional planar antenna arrays, the proposed method provides a significant reduction in time complexity compared to the conventional selection approach.

While reconfigurable intelligent surfaces (RISs) are among the key enablers for next-generation (NextG) wireless networks, efficient feedback reporting for joint base station (BS) precoding and passive RIS configuration remains a major challenge due to the associated signaling overhead. By extending the standard-compliant channel state information reference signal framework, this paper introduces a novel channel information indicator (CII) that jointly represents the active BS precoding matrix and passive RIS configuration within a single feedback metric for multi-user multiple-input single-output systems. Simulation results demonstrate that the proposed unified feedback framework significantly reduces uplink signaling overhead compared with conventional disjoint reporting schemes. Furthermore, despite only a modest increase in the feedback payload, the proposed CII-based scheme outperforms conventional precoding matrix indicator approaches in terms of system performance, offering a practical and standards-compatible solution for RIS integration in NextG wireless networks.

Reconfigurable intelligent surface (RIS) technology is a promising enabler for next-generation (NextG) wireless systems, capable of dynamically shaping the propagation environment. Integrating RIS within the open radio access network (O-RAN) architecture enables flexible and intelligent control of wireless links. However, practical RIS-assisted operation requires efficient acquisition and reporting of channel state information (CSI) to support real-time control from the base station side. This paper proposes a CSI reference signal (CSI-RS)-based reporting scheme for downlink complex channel information (CCI) to facilitate RIS optimization in an O-RAN-compliant environment. The proposed framework establishing CCI extraction and CSI-RS reporting procedures is experimentally validated on a real-world testbed integrating an open-source O-RAN system with an RIS prototype operating in the n78 frequency band. Existing channel estimation-based RIS optimization algorithms, including Hadamard and orthogonal matching pursuit (OMP), are tailored for integration into the O-RAN architecture. Experimental results demonstrate notable improvements in received signal power for both near and far users, highlighting the effectiveness and practical viability of the proposed scheme.

In disaster scenarios, ensuring both reliable communication and situational awareness becomes a critical challenge due to the partial or complete collapse of terrestrial networks. This paper proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture referred to as ISAC-over-NTN that integrates multiple uncrewed aerial vehicles (UAVs) and a high-altitude platform station (HAPS) to maintain resilient and reliable network operations in post-disaster conditions. We aim to achieve two main objectives: i) provide a reliable communication infrastructure, thereby ensuring the continuity of search-and-rescue activities and connecting people to their loved ones, and ii) detect users, such as those trapped under rubble or those who are mobile, using a Doppler-based mobility detection model. We employ an innovative beamforming method that simultaneously transmits data and detects Doppler-based mobility by integrating multi-user multiple-input multiple-output (MU-MIMO) communication and monostatic sensing within the same transmission chain. The results show that the proposed framework maintains reliable connectivity and achieves high detection accuracy of users in critical locations, reaching 90