This paper investigates the use of the weighted centroid localization (WCL) method for user localization in cell-free massive MIMO (CF-mMIMO) networks. This low-complexity algorithm operates solely on received power measurements from pilot transmissions, requiring no prior channel information or estimation. It enables coarse localization, which is valuable for various network management tasks while incurring minimal cost and overhead. Using a stochastic geometry-based analytical framework, we derive approximations for the localization mean-square error, providing insights into the performance and limitations of WCL. We also present an exact expression for the localization error cumulative distribution function, along with alternative approximations based on moment matching. The predictive capability of the proposed analytical framework is validated through extensive simulations that incorporate key practical impairments, such as multipath propagation, spatially correlated shadowing, and pilot contamination, that are analytically intractable. These results confirm the practical utility of our analysis in supporting the design of CF-mMIMO networks to meet specific localization performance targets.

The industrial Internet of things (IIoT) is revolutionizing industrial processes by facilitating massive machinetype communications among countless interconnected devices. To efficiently handle the resulting large-scale and sporadic traffic, grant-free random access protocols-especially coded random access (CRA)-have emerged as scalable and reliable solutions. At the same time, advancements in wireless hardware, including extremely large-scale MIMO arrays and high-frequency communication (e.g., mmWave, Terahertz), are pushing network operations into the near-field propagation regime, allowing for dense connectivity and enhanced spatial multiplexing. This paper proposes an innovative approach that combines near-field spatial multiplexing with the interference mitigation capabilities of CRA, utilizing an extremely large aperture array at the access point. This integration improves reliability and reduces access latency, offering a robust framework for IIoT connectivity in next-generation 6 G networks.

Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit a smaller gap to optimum decoding but are computationally prohibitive for practical deployment in URLLC systems. To bridge this gap, we propose an enhanced Chase-II decoding algorithm that leverages a neural network (NN) to predict promising perturbation patterns, drastically reducing the number of required decoding trials. The proposed approach combines the reliability of quasi-ML decoding with the efficiency of NN inference, making it well-suited for time-sensitive and resource-constrained applications.

This paper addresses the joint design of Unmanned Aerial Vehicles (UAVs) trajectory and radio resource management (RRM) in dynamic wireless environments by leveraging a multi-agent deep reinforcement learning (MADRL) framework. In contrast to prior works that either assume constant synchronization between agents and the controller or overlook the communication cost, we explicitly model the interaction between UAVs and the central controller. We propose an adaptive synchronization strategy that selectively transmits model parameters and experience data based on their relevance, enabling a resource-aware RRM algorithm that optimally balances learning performance and communication overhead. The MADRL agents optimize their trajectories based on rewards that incorporate priorities derived from the RRM layer, which jointly manages both uplink and downlink communications. Simulation results demonstrate that our event-driven synchronization strategy outperforms periodic baselines in both convergence speed and communication overhead, towards scalable deployment in realistic urban environments.

We investigate the packet collision probability among uncoordinated devices, in the uplink of Direct-to-Satellite Internet of Things (DtS-IoT). Both the satellite spot shape and its motion along its orbital path are considered in the analysis. We analyze the probability of no uplink collision under two DtS-IoT settings: 1) unconfirmed ALOHA over a single channel, e.g., long-range wide-area network (LoRaWAN) class A with long-range chirp spread spectrum (LoRa-CSS) modulation and 2) unconfirmed ALOHA with frequency-hopping compliant with LoRaWAN class A with long-range frequency-hopping spread spectrum (LR-FHSS). A closed-form solution is derived for the former, while an upper bound is found for the latter. The analytical results are validated by comparison with the outcomes of extensive simulations, showing that the obtained closed-form expressions accurately predict the probability of no collision. Moreover, the upper bound for the frequency-hopping case is proven to be tighter when there are less than 35 hopping channels. Finally, we provide a concise performance comparison between LoRa-CSS and LR-FHSS in DtS-IoT, showing that LR-FHSS significantly increases the number of devices that can simultaneously transmit during a satellite pass, thereby enhancing uplink capacity.

Massive machine-type communications are transforming the Industrial Internet of Things (IIoT) by enabling seamless connectivity among a vast number of devices. To efficiently manage the resulting sporadic and large-scale traffic, grant-free random-access protocols, particularly coded random access (CRA), have emerged as scalable and reliable solutions. Meanwhile, advancements in extremely large-scale MIMO and high-frequency communications (e.g., mmWave, THz) are pushing networks into the near-field regime, enhancing spatial multiplexing and connectivity. In this paper, we propose a novel coded spatial random access (CSRA) scheme that leverages an extremely large aperture array (ELAA) at the access point (AP) to exploit near-field spatial multiplexing for grant-free massive access in IIoT systems. Unlike conventional approaches, CSRA eliminates the need for complex channel estimation by integrating CRA with successive interference cancellation and the spatial multiplexing capabilities of the near-field regime. This enables improved reliability and scalability, making CSRA a promising solution for next-generation wireless networks.

In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. Such joint trajectory design can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes a critical challenge. Traditional methods involve imposing high penalties during training to discourage unsafe conditions, but these can be proven to be ineffective, whereas binary masks can be used to restrict unsafe actions, but naively applying them to all agents can lead to suboptimal solutions and inefficiencies. To address these issues, we propose a rank-based binary masking approach. Higher-ranked UAVs move optimally, while lower-ranked UAVs use this information to define improved binary masks, reducing the number of unsafe actions. This approach allows to obtain a good trade-off between exploration and exploitation, resulting in enhanced training performance, while maintaining safety constraints.

The adoption of dynamic spectrum sharing requires addressing various technical challenges, such as intelligent sensing and radio frequency (RF) spectrum awareness. In this scenario, we present a new framework that utilises variational Bayes factor analysis (VBFA) for cooperative wideband spectrum sensing (WSS) without any prior assumption. The approach is data-driven and capable of detecting unused spectrum bands using a test statistic on an evidence lower bound (ELBO). The framework is then applied to a case study that considers shadowing effects and frequency-selective multipath channels between primary users (PUs) and sensors. The solution performs better than the state-of-the-art methods, demonstrating excellent performance in low signal-to-noise ratio (SNR) environments, e.g., reaching a detection probability of 90% for a nominal SNR of $-10 \mathrm{d B}$.

Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper presents a novel multi-task learning approach aimed at mitigating inter-user interference and enhancing spectral efficiency, particularly in scenarios where the number of users far exceeds the available orthogonal pilots. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment. Numerical results demonstrate that our algorithm outperforms existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption.

Unmanned Aerial Vehicles (UAVs), functioning as Unmanned Aerial Base Stations (UABSs), hold considerable potential for augmenting vehicular network performance through on-demand enhanced radio coverage. A pivotal challenge lies in devising algorithms that efficiently optimize UABS trajectories under strict Radio Resource Management (RRM) and coverage gap discovery. This can be tackled using Deep Reinforcement Learning (DRL) models. However, their effectiveness relies on the relevance of acquired knowledge to the current scenario, posing a challenge when the underlying dynamics or governing rules undergo modifications. To address this issue, we propose a framework integrating a deep meta-learning algorithm to enhance the adaptability of our DRL-based trajectory design to newly encountered scenarios. Scenarios may vary in mobile users’ movement profiles, UABS take-off zones, or new service maps. Our numerical results demonstrate that an agent that leverages information from prior tasks achieves target performance in fewer episodes compared to a conventional DRL agent, while also ensuring superior long-term training proficiency.

This paper considers a direct-to-satellite Internet of Things system where nodes on ground contend to deliver data packets to a low Earth orbit satellite, during its pass. In this framework, the paper focuses on the collision probability between the packets transmitted by the uncoordinated contending devices. An exact and closed-form expression for this probability is derived that considers both the satellite velocity and the different contention time widows the devices may have, depending on their positions on ground. The analytical expression, valid for a wide range of different shapes of the satellite spot, is validated by comparison with numerical simulations. Usage of the developed expression to upper bound the packet loss probability over realistic channels is discussed.

In this paper, grant-free uplink communication from a large number of machine-type devices in CF-mMIMO networks is explored. A novel approach that leverages coded random access, on the device side, with combining of signals received at properly selected AP and cooperative successive interference cancellation, on the network side, is presented. Initially, an analytical framework based on stochastic geometry is developed to investigate performance of AP cooperation through signal combining under diverse AP cluster compositions. The potential gain from AP signal combining is then assessed by evaluating a genie-aided scheme, guiding the network in cluster selection for each active device. Subsequently, two practical AP selection algorithms that operate in grant-free conditions (i.e., do not require prior information regarding the active users) are proposed. Numerical results show how AP cooperation through signal combining and distributed interference cancellation can bring tangible benefits even without prior information about active users, under different SNR regimes, closing in some cases the gap to the genie-aided approach. Additionally, the results prove that AP cooperation can be used to reduce the devices’ energy consumption and the number of AP that have to be deployed by the service providers to achieve specific performance levels.

In this work, we propose a novel framework for reactive jammer detection based on spectrum patrolling, blind source separation (BSS), and causal inference. The methodology is based on a spectrum patrol that performs jamming detection without demodulating the received signals; therefore, it can manage scenarios with legitimate users belonging to different networks and adopting completely different network protocols. Furthermore, because of the wireless medium, over-the-air signals captured by the patrols are mixed; consequently, BSS is used to separate traffic patterns. The proposed causal inference-based approach (namely all-versus-one transfer entropy (AvOTE)) reaches a remarkably high probability of detection (i.e., 0.95) with low false alarm probability (i.e., 0.05) even in high shadowing regimes. Moreover, to fully understand the impact of different parameters on the performance, we study the impact of the signal-to-jammer ratio (SJR) and the jammer attack strategy on the detection performance in different shadowing regimes.

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.

This work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility.