One of the key requirements for future 5th Generation (5G) Industrial Internet of Things (IIoT) networks will be to deliver low latency to support different production processes. To this end, 5G New Radio (NR) provides Configured Grant (CG) scheduling for periodic traffic, additionally to conventional Grant-Based Scheduling (GBS). However, in view of the complexities introduced by spatio-temporal traffic correlations in IIoT, a fixed scheduler configuration may be suboptimal: GBS introduces excessive signaling overhead, while CG leads to inefficient resource utilization and latency degradation when traffic is not perfectly periodic. To solve these critical issues, we propose Hybrid Centralized Uplink Scheduler (HCUS), a new scheduling framework that dynamically learns the type of traffic generated by User Equipments (UEs), and adapts resource allocation accordingly. HCUS operates per-UE, and dynamically switches between GBS and Configured Grant (CG), optimizing resource allocation while preserving low End-to-End (E2E) latency. We consider both mixed periodic and aperiodic uplink traffic to model different network load conditions and IIoT applications. Extensive simulations show that HCUS achieves up to four times lower latency than GBS and CG while maintaining high reliability, even considering traffic correlations or periodicity changes, making it a robust and scalable solution for next-generation IIoT scenarios.

The coexistence of terrestrial networks (TNs) and non-terrestrial networks (NTNs) within shared spectrum bands poses significant uplink (UL) interference challenges. While conventional studies assume single-antenna TN terminals, this work investigates UE-side precoding for NTN–TN coexistence using multi-antenna user equipment (UE) operating in upper mid-band frequencies, where larger antenna arrays at the UE become feasible. We develop a parametrized, direction-informed linear UL precoder that leverages antenna directivity and satellite ephemeris information, enabling UEs to shape their radiation patterns without requiring real-time satellite channel state information (CSI). By tuning two design parameters, the precoder can be configured for different practical objectives: an NTN-oriented setting that prioritizes interference suppression toward satellites, and a TN-oriented setting that preserves TN signal quality while adaptively prioritizing the most interference-exposed satellites, reducing the upper tail of the interference-to-noise ratio (INR) distribution and achieving an interference profile closer to uniform.

This work investigates 2D distributed satellite swarm configurations for direct-to-cell (D2C) applications. Unlike previous studies, which primarily considered swarms as deployment alternatives to monolithic arrays, this paper focuses on exploiting the increased spatial resolution enabled by large distributed apertures. Simulation results show that, for the considered scenarios, increasing the level of antenna distribution across satellite platforms enlarges the effective aperture and improves the system sum rate under ideal operating conditions. While gains are moderate for uniformly distributed users, they become particularly pronounced in scenarios including hotspot with high user densities. The results further show that user scheduling strategies do not invalidate the superiority of highly distributed configurations, as these architectures provide a more balanced rate distribution across the coverage area, including hotspot regions. In addition, the paper analyzes several key implementation challenges associated with large distributed swarms, including errors in inter-satellite relative positioning, synchronization impairments, and limited beamforming and user-position update rates. Although within the range of swarm configurations and scenarios considered in this work, performance gains continue to increase with aperture size, these practical constraints may restrict swarm sizes in the medium term. Nevertheless, the observed performance gains strongly motivate further research into scalable synchronization, positioning and data distribution techniques for future large-scale satellite swarms.

Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains. However, standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized grant-based scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and granted by the gNB. In turn, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the resources for transmission, may lead to potentially many collisions especially when the traffic increases. In this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available resources to minimize the number of collisions, without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple parallel actions. We demonstrate the superior performance of DISNETS in addressing URLLC in IIoT scenarios compared to other baselines.

This paper investigates a modular beamforming framework for reconfigurable intelligent surface (RIS)-aided multi-user (MU) communications in the near-field regime, built upon a novel antenna architecture integrating an active multi-antenna feeder (AMAF) array with a transmissive RIS (T-RIS), referred to as AT-RIS. This decoupling enables coordinated yet independently configurable designs in the AMAF and T-RIS domains, supporting flexible strategies with diverse complexity-performance trade-offs. Several implementations are analyzed, including diagonal and non-diagonal T-RIS architectures, paired with precoding schemes based on focusing, minimum mean-square error, and eigenmode decomposition. Simulation results demonstrate that while non-diagonal schemes maximize sum-rate in scenarios with a limited number of user equipments (UEs) and high angular separability, they exhibit fairness and scalability limitations as UE density increases. Conversely, diagonal T-RIS configurations, particularly the proposed focusing-based scheme with uniform feeder-side power allocation, offer robust, fair, and scalable performance with minimal computational overhead. The findings emphasize the critical impact of UEs’ angular separability and reveal inherent trade-offs among spectral efficiency, complexity, and fairness, positioning diagonal AT-RIS architectures as practical solutions for scalable near-field MU multiple-input single-output systems.

Improved spectral efficiency and inter-user interference mitigation are important aspects of IMT-2030 and the ongoing 3GPP 6G study. ETSI established the Industry Specification Group on Multiple Access Techniques (ISG MAT) as a research and pre-standardisation activity to build wider consensus on downlink MAT for 3GPP-based 6G systems. Its first report, ETSI GR MAT 001 V1.1.1, provides a standards-oriented comparison of 3GPP-specified techniques, including OMA, MU-MIMO, and MUST, with candidate techniques comprising power-domain NOMA, RSMA, and cache-aided MU-MIMO. The report identifies operating conditions in which candidate MAT can improve spectral efficiency and assesses implications for transceiver processing, network assistance information, and reference-signal requirements. Ongoing work includes realistic 5G NR link-level evaluations and the study of MAT for non-terrestrial networks, providing timely technical evidence for 3GPP 6G standardisation discussions.

The International Mobile Telecommunications (IMT)-2030 framework recently adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions 6G networks to deliver intelligent, seamless connectivity that supports reliable, sustainable, and resilient communications. Recent developments in the 3rd Generation Partnership Project (3GPP) Releases 17-19, particularly within the Radio Access Network (RAN)4 working group addressing satellite and cellular spectrum sharing and RAN2 enhancing New Radio (NR)/IoT for NTN, highlight the critical role NTN is set to play in the evolution of 6G standards. The integration of advanced signal processing, edge and cloud computing, and Deep Reinforcement Learning (DRL) for Low Earth Orbit (LEO) satellites and aerial platforms, such as Uncrewed Aerial Vehicles (UAV) and high-, medium-, and low-altitude platform stations, has revolutionized the convergence of space, aerial, and Terrestrial Networks (TN). Artificial Intelligence (AI)-powered deployments for NTN and NTN-IoT, combined with Next Generation Multiple Access (NGMA) technologies, have dramatically reshaped global connectivity. This tutorial paper provides a comprehensive exploration of emerging NTN-based 6G wireless networks, covering vision, alignment with 5G-Advanced and 6G standards, key principles, trends, challenges, real-world applications, and novel problem solving frameworks. It examines essential enabling technologies like AI for NTN (LEO satellites and aerial platforms), DRL, edge computing for NTN, AI for NTN trajectory optimization, Reconfigurable Intelligent Surfaces (RIS)-enhanced NTN, and robust Multiple-Input-Multiple-Output (MIMO) beamforming. Furthermore, it addresses interference management through NGMA, including Rate-Splitting Multiple Access (RSMA) for NTN, and the use of aerial platforms for access, relay, and fronthaul/backhaul connectivity.

Low-Earth orbit (LEO) satellite networks, being a promising component of non-terrestrial networks (NTN), are meant to provide seamless and ubiquitous global connectivity. However, the high mobility of the LEO satellites results in frequent handovers (HOs), poses significant challenges to maintaining service continuity due to the interruption time experienced during the HO execution phase, which is particularly critical for high reliability communications (HRC). In this aspect, 3GPP proposed the dual active protocol stack (DAPS) HO mechanism for terrestrial networks (TN), a soft HO approach that enables the user equipment (UE) to maintain the simultaneous connection between both the source and target gNBs during the HO execution phase, potentially achieving a zero handover interruption time (HIT). However, DAPS HO is still not supported for NTN in latest 3GPP Release 18, likely due to rapid orbital movement of satellites. Moreover, DAPS HO requires synchronization between source and target nodes during the handover process, a condition that is challenging to identify at all times. Inspired by this challenge, this paper proposes a network-controlled algorithm that enhances the feasibility of DAPS HO in LEO satellites network, thereby minimizing the HIT over a given observation period. The proposed algorithm employs a sequential decision-making process within a predefined window to make the decision on configuring the DAPS HO. Simulation results demonstrate that the proposed approach significantly reduces the HIT, achieving a 80.28% reduction as compared to baseline HO scheme.

The increasing spectrum overlap between non-terrestrial networks (NTNs) and terrestrial networks (TNs) introduces significant uplink (UL) interference challenges. This paper addresses NTN–TN coexistence by exploiting the spatial degrees of freedom available at TN user equipment (UE) with large antenna arrays, which become feasible at upper mid-band frequencies. We propose an uplink precoding method based on regularized zero-forcing (RZF), adapted to suppress interference toward satellites while preserving TN performance. Unlike classical RZF applications at the base station, the proposed scheme is applied at the UE and does not require real-time estimation of satellite channel state information (CSI). Moreover, the gNB centrally computes a user-specific regularization parameter. To address hardware constraints, hybrid beamforming is also incorporated. The proposed approach is benchmarked against Maximum Ratio Transmission (MRT) and the interference nulling scheme. Simulation results show that adaptive RZF significantly reduces interference at satellites while preserving terrestrial signal quality, offering an effective solution for NTN–TN spectrum sharing in the upper mid-band.

Future 6G communications are expected to be complemented by non-terrestrial networks (NTNs), particularly satellites, to ensure ubiquitous connectivity. Furthermore, to address the growing spectrum shortage in terrestrial networks (TNs), the upper mid-band (7-24 GHz), currently known as frequency range 3 (FR3), is emerging as a key frequency band due to its appealing balance between capacity and coverage. However, this band is already utilized by incumbent satellite communications, making the investigation of NTN and TN coexistence in FR3 critical. This paper explores linear multi-antenna receiver techniques at TN user equipment (UE), aimed at enhancing coexistence between NTN and TN, leveraging the increased number of antennas that higher frequency bands enable on UE. Specifically, we apply Maximum Ratio Combining (MRC) and Interference Rejection Combining (IRC) techniques to address the dual challenges of interference suppression and signal enhancement. Simulation results demonstrate the effectiveness of these techniques in improving TN UE performance and reducing satellite interference.

Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as delays or errors can lead to significant deviations of the AGVs from the planned trajectory. In this paper, we present a simulation framework based on Gazebo and Robot Operating System 2 (ROS 2) to simulate and visualize, respectively, the complex interaction between the control of AGVs and the underlying communication network. This framework explicitly incorporates communication metrics, such as delay and packet loss, and control metrics, especially the Mean Squared Error (MSE) between the optimal/desired and actual path of the AGV in response to driving commands. Our results shed light into the correlation between the network performance, particularly Packet Reception Ratio (PRR), and accuracy of control.

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of pre-designed phase-shift configurations for the IRS unit cells. Since the complexity of online optimization and the overhead for channel estimation scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We consider both continuous and discrete phase-shift designs and formulate the codebook construction as optimization problems. To solve the optimization problems, we propose an optimal algorithm for the discrete phase-shift design and a locally optimal solution for the continuous design. Simulation results show that the proposed algorithms facilitate the construction of codebooks of different sizes and with different beamwidths. Moreover, the performance of the discrete phase-shift design with 2-bit quantization is shown to approach that of the continuous phase-shift design. Finally, our simulation results show that the proposed designs enable large transmit power savings compared to the existing linear and quadratic codebook designs.

This paper explores multi-satellite transmission schemes. The focus is on the multi-user downlink scenario, where two low Earth orbit satellites transmit data cooperatively, which are then combined at each of the ground user terminals. A single synchronization circuit is employed at the receiver to reduce the terminal burden. The orthogonal time and frequency space (OTFS) waveform is utilized to handle the residual offsets and schedule users on non-overlapping delay-Doppler resources. The paper leverages on OTFS, beam-centric compensation and the characteristics of the satellite channel to eliminate inter-user interference. Numerical results assess the effectiveness of the proposed scheme and highlight the improvement in performance achieved through satellite cooperation as compared to single satellite transmission.

Future factories will rely on highly reliable wireless communication among the plenty of devices and the network. The 5G networks enable ultra-reliable low-latency communication (URLLC) service to address the reliability and latency requirements in factories, where device-to-device (D2D) connections can provide additional means for improving communication reliability. To this aim, we have proposed a sidelink (SL)-assisted cooperative retransmissions (CoRe) scheme in our previous work, where retransmissions via SL for unsuccessful downlink (DL) transmissions are used to improve communication reliability under strict latency constraints. In this paper, we evaluate the CoRe scheme for a realistic factory scenario using system-level simulations, where we consider interference coordination and an optimal power control (PC) scheme. Inspired by the outcome of small transmit powers needed for the SL-assisted retransmissions, we propose a novel resource management scheme named “flipped-underlay”, which is realized by underlay communication. While in conventional underlay, a D2D communication with small transmit power is assigned to resources already allocated for uplink (UL) communication and thus underlaid, the D2D resources are allocated firstly in our scheme, and hence called flipped-underlay. Results demonstrate that the gains from CoRe scheme, are three-fold: showing a significant reduction in the total number of SL-assisted retransmissions compared to conventional retransmissions via DL while maintaining the desired reliability and latency performance, reduced transmit power consumption by virtue of the optimal power allocation for retransmissions, and finally yet importantly, reuse of resources in our new flipped-underlay resource allocation (FURA) algorithm substantially reduces the total amount of resources occupied by the system.

We propose a communication framework suitable for data rate maximization in the Terahertz (THz) bands using adaptive Orthogonal Frequency Division Multiplexing (OFDM) numerology and carrier aggregation. OFDM is a widely adopted waveform due to the simplicity of its implementation and its effectiveness in combating frequency selectivity when the numerology is carefully chosen. However, it suffers from a multitude of limitations, including phase noise due to local oscillator inaccuracies, high peak-to-average power ratio, and is particularly sensitive to time-frequency synchronization errors, which can considerably impact its performance. This is especially relevant at THz frequencies where larger-than-usual bandwidth is available, and the choice of the numerology should be carefully made given the intrinsic transceiver constraints. Moreover, the abundance of frequency resources in the THz band imposes new design challenges that should be addressed, especially since the bandwidth usability at these frequencies depends on the communication distance. Hence, we propose a dynamic OFDM numerology adaptation mechanism, where the bandwidth of a Component Carrier (CC) covered by a single OFDM waveform is changed. For each CC, the Component Carrier Data Rate (CCDR) is evaluated while considering the effect of both hardware impairments and the wireless channel statistics. We further propose the adoption of a dynamic distance-aware CC allocation such that the available frequency resources are fully utilized, and maximize an Aggregated Data Rate (ADR) through the aggregation of several CCs. Simulation results show that the proposed approach yields the highest ADR out of all possible setups.

This paper investigates the resource allocation algorithm design for wireless systems assisted by large intelligent reflecting surfaces (IRSs) with coexisting enhanced mobile broadband (eMBB) and ultra reliable low-latency communication (URLLC) users. We consider a two-time scale resource allocation scheme, whereby the base station's precoders are optimized in each mini-slot to adapt to newly arriving URLLC traffic, whereas the IRS phase shifts are reconfigured only in each time slot to avoid excessive base station-IRS signaling. To facilitate efficient resource allocation design for large IRSs, we employ a codebook-based optimization framework, where the IRS is divided into several tiles and the phase-shift elements of each tile are selected from a pre-defined codebook. The resource allocation algorithm design is formulated as an optimization problem for the maximization of the average sum data rate of the eMBB users over a time slot while guaranteeing the quality-of-service (QoS) of each URLLC user in each mini-slot. An iterative algorithm based on alternating optimization (AO) is proposed to find a high-quality suboptimal solution. As a case study, the proposed algorithm is applied in an industrial indoor environment modelled via the Quadriga channel simulator. Our simulation results show that the proposed algorithm design enables the coexistence of eMBB and URLLC users and yields large performance gains compared to three baseline schemes. Furthermore, our simulation results reveal that the proposed two-time scale resource allocation design incurs only a small performance loss compared to the case when the IRSs are optimized in each mini-slot.

This paper investigates a deployment of intelligent reflecting surfaces (IRS) in a scenario of industrial IoT communication, characterized by data transmission with high reliability under low latency constraints. In this scenario, channel diversity from independent radio links is of supreme importance, which can be enriched by IRS assisted links. By first dimensioning the size of the IRS to fulfill the far-field conditions for the carrier frequency used, the path-loss of the IRS assisted links is modeled based on the 3GPP path-loss models for indoor factories. An analysis of the achievable effective capacity is carried out for selected carrier frequencies in the cm- and mm-wave bands, where it is shown that the IRS deployment can yield significant capacity gains compared to the baseline without IRS.

This paper investigates a dual satellite transmission scheme with coherent reception. The receiver has a single synchronization circuit and is locked to only one of the satellites. Beam-centric pre-compensation techniques are considered in the paper. The cooperation area in which coherent reception is feasible is characterized analytically. The application of precoding to the orthogonal time and frequency space (OTFS) waveform is considered to counteract the residual offsets, which result from the displacement of the receiver from the selected reference point. Numerical evaluations show that the dual satellite scheme improves the system spectral efficiency as well the link reliability in comparison with the single satellite transmission scheme.