Availability of Aerial Heterogeneous Networks for Reliable Emergency Communications

We investigate network availability (NA) in aerial heterogeneous networks (AHetNets) for effective emergency rescue, where diverse delay-constrained communication services must be provided to user equipments (UEs) with varying mobility. The heterogeneity in delay constraints and UE mobility introduces resource allocation conflicts and imbalances, which undermine communication reliability and challenge NA. Although unified resource allocation (URA) can mitigate these issues, it remains unclear whether NA can be sustained under such diverse conditions. To address this, we derive expressions for the lower bound (LB) on NA in AHetNets under URA. Our analysis reveals that extended heterogeneity significantly degrades the LB due to resource limitations-even when the heterogeneity stems from additional services under less stringent delay constraints (LSDC) or from UEs with lower mobility. To overcome this degradation, we formulate and solve a joint optimization problem for the number of UEs sharing time-frequency resources ($K$) and pilot length ($ξ$), aiming to enhance the LB by improving spatial, frequency, and temporal resource efficiency. Simulation results validate our analysis and demonstrate that jointly optimizing $K$ and $ξ$ enables AHetNets to achieve the target NA under greater heterogeneity, outperforming existing resource allocation policies.

Cell-Free Massive MIMO-Assisted SWIPT Using Stacked Intelligent Metasurfaces

This study explores a next-generation multiple access (NGMA) framework for cell-free massive MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.

Distributed Continuous Aperture Arrays for Multiuser SWIPT

This paper proposes a distributed continuous aperture array (D CAPA) to support simultaneous wireless information and power transfer (SWIPT) to multiple information users (IUs) and energy users (EUs). Each metasurface supports continuous surface currents that radiate electromagnetic (EM) waves for information and energy transmission to the users. These waves propagate through continuous EM channels characterized by the dyadic Green function. We formulate a system power consumption (PC) minimization problem subject to spectral efficiency and energy harvesting quality of service (QoS) requirements, where the QoS requirements are derived under the equal power allocation (EPA) scheme. An efficient two layer optimization algorithm is developed to solve this problem by optimizing the power allocation subject to the QoS violation penalties using augmented Lagrangian transformation. Our numerical results show that well optimized current distributions over each metasurface in the proposed D CAPA achieve up to 65% and 61% reductions in overall system PC compared to the EPA and colocated CAPA (C CAPA) cases, while maintaining the same total aperture size and transmission power.

Secure Task Offloading and Resource Allocation Design for Multi-Layer Non-Terrestrial Networks

Remote and resource-constrained Internet-of-Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

Robust SAC-Enabled UAV-RIS Assisted Secure MISO Systems With Untrusted EH Receivers

This paper investigates secure downlink transmission in a UAV-assisted reconfigurable intelligent surface (RIS)-enabled multiuser multiple-input single-output network, where legitimate information-harvesting receivers coexist with untrusted energy-harvesting receivers (UEHRs) capable of eavesdropping. A UAV-mounted RIS provides blockage mitigation and passive beamforming, while the base station employs zero-forcing precoding for multiuser interference suppression. Due to limited feedback from UEHRs, their channel state information (CSI) is imperfect, leading to a worst-case secrecy energy efficiency (WCSEE) maximization problem. We jointly optimize the UAV horizontal position, RIS phase shifts, and transmit power allocation under both perfect and imperfect CSI, considering discrete RIS phases, UAV mobility, and energy-harvesting constraints. The resulting problem is highly nonconvex due to coupled channel geometry, robustness requirements, and discrete variables. To address this challenge, we propose a soft actor-critic (SAC)-based deep reinforcement learning framework that learns WCSEE-maximizing policies through interaction with the wireless environment. As a structured benchmark, a successive convex approximation (SCA) approach is developed for the perfect CSI case with continuous RIS phases. Simulation results show that the proposed SAC method achieves up to 28% and 16% secrecy energy efficiency gains over SCA and deep deterministic policy gradient baselines, respectively, while demonstrating superior robustness to CSI uncertainty and stable performance across varying transmit power levels and RIS sizes.

Anti-Malicious ISAC: How to Jointly Monitor and Disrupt Your Foes?

Integrated sensing and communication (ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by malicious foes. In this paper, we propose a new wireless proactive monitoring paradigm, where a legitimate monitor intercepts a suspicious communication link while performing cognitive jamming to enhance the monitoring success probability (MSP) and simultaneously safeguard the target. To this end, we derive closed-form expressions of the signal-to-interference-plus-noise-ratio (SINR) at the user (UE), sensing access points (S-APs), and an approximating expression of the SINR at the proactive monitor. Moreover, we propose an optimization technique under which the legitimate monitor minimizes the success detection probability (SDP) of the legitimate target, by optimizing the jamming power allocation over both communication and sensing channels subject to total power constraints and monitoring performance requirement. To enhance the monitor's longevity and reduce the risk of detection by malicious ISAC systems, we further propose an adaptive power allocation scheme aimed at minimizing the total transmit power at the monitor while meeting a pre-selected sensing SINR threshold and ensuring successful monitoring. Our numerical results show that the proposed algorithm significantly compromises the sensing and communication performance of malicious ISAC.

Energy-Efficient Federated Learning with Relay-Assisted Aggregation in IIoT Networks

This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a global model by uploading local updates to an edge server (ES), either directly or via neighboring SNs acting as decode-and-forward relays. To enhance communication efficiency, relays perform partial aggregation before forwarding the models to the ES, significantly reducing overhead and training latency. We analyze the convergence behavior of this relay-assisted FL scheme. To address the inherent energy efficiency (EE) challenges, we decompose the original non-convex optimization problem into sub-problems addressing computation and communication energy separately. An SN grouping algorithm categorizes devices into single-hop and two-hop transmitters based on latency minimization, followed by a relay selection mechanism. To improve FL reliability, we further maximize the number of SNs that meet the roundwise delay constraint, promoting broader participation and improved convergence stability under practical IIoT data distributions. Transmit power levels are then optimized to maximize EE, and a sequential parametric convex approximation (SPCA) method is proposed for joint configuration of system parameters. We further extend the EE formulation to the imperfect channel state information (ICSI). Simulation results demonstrate that the proposed framework significantly enhances convergence speed, reduces outage probability from 10-2 in single-hop to 10-6 and achieves substantial energy savings, with the SPCA approach reducing energy consumption by at least 2x compared to unaggregated cooperation and up to 6x over single-hop transmission.

Pinching-Antenna System-Assisted Localization: A Stochastic Geometry Perspective

This paper proposes a novel localization framework underpinned by a pinching-antenna (PA) system, in which the target location is estimated using received signal strength (RSS) measurements obtained from downlink signals transmitted by the PAs. To develop a comprehensive analytical framework, we employ stochastic geometry to model the spatial distribution of the PAs, enabling tractable and insightful network-level performance analysis. Closed-form expressions for target localizability and the Cramer-Rao lower bound (CRLB) distribution are analytically derived, enabling the evaluation of the fundamental limits of PA-assisted localization systems without extensive simulations. Furthermore, the proposed framework provides practical guidance for selecting the optimal waveguide number to maximize localization performance. Numerical results also highlight the superiority of the PA-assisted approach over conventional fixed-antenna systems in terms of the CRLB.

Cell-Free Massive MIMO-Based Physical-Layer Authentication

In this paper, we exploit the cell-free massive multiple-input multiple-output (CF-mMIMO) architecture to design a physical-layer authentication (PLA) framework that can simultaneously authenticate multiple distributed users across the coverage area. Our proposed scheme remains effective even in the presence of active adversaries attempting impersonation attacks to disrupt the authentication process. Specifically, we introduce a tag-based PLA CFmMIMO system, wherein the access points (APs) first estimate their channels with the legitimate users during an uplink training phase. Subsequently, a unique secret key is generated and securely shared between each user and the APs. We then formulate a hypothesis testing problem and derive a closed-form expression for the probability of detection for each user in the network. Numerical results validate the effectiveness of the proposed approach, demonstrating that it maintains a high detection probability even as the number of users in the system increases.

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems

We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral efficiency (SE) performance of the system wherein the users use the minimum mean-squared errorbased successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. Specifically, we formulate an optimization problem for the user association and power control for maximizing the sum SE. The formulated problem is under per-AP transmit power and fronthaul constraints, and is based on only long-term channel state information (CSI). The challenging formulated problem is transformed into tractable form and a novel algorithm is proposed to solve it using minorization maximization (MM) technique. We analyze the trade-offs provided by the CF-mMIMO system with different number of CB clusters, hence highlighting the importance of the appropriate choice of CB design for different system setups. Numerical results show that for the centralized CB, the proposed power optimization provides nearly 59% improvement in the average sum SE over the heuristic approach, and 312% improvement, when the distributed beamforming is employed.