To mitigate the significant degradation of Ultra-Wideband (UWB) positioning accuracy in non-line-of-sight (NLoS) scenarios, this paper proposes an integrated framework that synergizes Channel Impulse Response (CIR) fingerprinting with Time of Arrival (ToA) ranging. Unlike conventional methods that treat fingerprinting and ranging independently, we introduce a novel uncertainty-aware fusion strategy. Specifically, an attention-enhanced Convolutional Neural Network (CNN) is first employed to extract environmental features from CIR data. Crucially, instead of direct coordinate regression, we utilize a Copula-based conformal prediction(CP) method to quantify the positioning uncertainty and construct a reliable spatial search constraint. This constraint then guides a Particle Swarm Optimization (PSO) algorithm to robustly identify the optimal position within the predicted region when ToA ranging errors are modeled by a Gaussian Mixture Model (GMM). This mechanism effectively filters out large outliers by leveraging the complementary strengths of feature matching and geometric ranging. Experimental results on public datasets demonstrate that the proposed method achieves a mean error of 0.0911 meters, outperforming six state-of-the-art algorithms by at least 8.35% while satisfying high computational efficiency requirements.

As wireless communication systems evolve toward intelligent operation, the growing complexity of dynamic and non-stationary propagation environments inherent in large-scale and heterogeneous Internet of Things (IoT) scenarios imposes stringent demands on link adaptation robustness. To address these challenges, we propose a joint bandwidth and modulation and coding scheme (MCS) optimization framework that leverages autonomous scenario identification (ASI) and dueling double deep Q-network (D3QN), named as ASI-D3QN. Specifically, a lightweight deep convolutional neural network (CNN) is tailored for ASI to achieve high identification accuracy while maintaining low computational complexity. This design is particularly suited for resource-constrained devices. Then, a D3QN-based optimization strategy is developed to seamlessly integrate ASI-derived environmental context with intrinsic channel metrics. The proposed framework explicitly expands the decision space by integrating signal bandwidth as an additional optimization dimension, facilitating adaptive optimization over multi-dimensional parameters. Finally, an intelligent communication prototype is established to comprehensively validate the proposed approach under representative standardized channel models. Experimental results demonstrate that, compared to existing methods, the proposed ASI achieves at least a 0.27% accuracy improvement with fewer model parameters. Moreover, relative to conventional link adaptation schemes, the ASI-D3QN strategy achieves superior throughput gains in complex dynamic environments.

To efficiently implement Terahertz (THz) communications in the 6G era, the ultra-massive multiple-input multipleoutput (UM-MIMO) technique is considered an essential building block. However, effective wideband THz UM-MIMO transmissions can never be achieved without pilot-inexpensive yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem, accounting for the hybrid near- and far-field propagation characteristics, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing-aided counterpart, leveraging the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. We harness the power of model-driven deep learning and propose a deep unfolding (DU)-aided Bayesian learning (DUBL) CE algorithm. We tailor the structure of the deep neural network (DNN)-based unfolded expectation-maximization (EM) iteration, aiming to achieve efficient DUBL training performance. Simulation results demonstrate that the DUBL solution can offer substantial THz UM-MIMO CE gains over the considered representative benchmarks.

While Wi-Fi Received Signal Strength Indicator (RSSI) fingerprinting has emerged as a prominent solution for indoor positioning, its accuracy remains hindered by labor-intensive data collection and environmental variability. To overcome these challenges, we propose a novel LCVAE-CNN methodology that integrates a Location-Conditioned Variational Autoencoder (LCVAE) and a multi-task Convolutional Neural Network (CNN) to enhance data quality and positioning performance. The LCVAE employs a dual-encoder architecture to augment RSSI fingerprints by jointly modeling signal features and spatial dependencies, introducing three key innovations: (1) dual-stream encoding that decouples RSSI and location processing for more effective feature learning, (2) a geospatial loss function that enforces topological consistency in the generated data, and (3) conditional data augmentation that preserves physical constraints of indoor spaces. The multi-task CNN then leverages shared feature extraction to jointly optimize classification and regression tasks, enabling efficient and accurate positioning. Extensive evaluations on the UJIIndoorLoc and Tampere datasets demonstrate the superiority of the LCVAE-CNN that achieves 98.80% floor classification accuracy with a Mean Positioning Error (MPE) of 6.79 meters on UJIIndoorLoc, whereas 97.22% accuracy with a MPE of 5.44 meters on the Tampere dataset. Compared to five state-of-the-art methods, it improves floor accuracy by at least 1.9% and reduces MPE by over 19%, while maintaining comparable computational overhead, thereby achieving superior accuracy-efficiency tradeoffs.

We investigate the problem of maximizing anti-jamming sum throughput in an RIS-assisted Internet of Things (IoT) network. The network’s operation is divided into two stages: 1) IoT terminals first harvest energy from the wireless energy station (WES) and 2) they then transmit their information to the information receiver (IR) using a frequency division multiple access (FDMA) protocol. We consider three different design scenarios: 1) ideal hardware; 2) phase shift error (PSE); and 3) a combination of both PSE and transceiver hardware impairments (THIs). To address the nonconvexities of these designs, we employ novel techniques, such as the Lagrangian dual method, Karush–Kuhn–Tucker (KKT) conditions, quadratic transformation (QT), element-wise block coordinate descent (EBCD), complex circle manifold (CCM), and 1-D search to obtain the optimal solutions. Numerical results are provided to illustrate that the proposed approaches outperform existing benchmarks.

This article focuses on maximizing the anti-jamming sum throughput in a time division multiple access (TDMA)-based reconfigurable intelligent surfaces (RIS)-assisted wireless powered Internet of Things (WP-IoT) network. In this setup, multiple IoT devices harvest energy from wireless energy stations (WES) and then utilize the collected energy to upload their own data to an information receiver (IR). The network also includes a jammer that sends jamming signals to the IR, and a RIS is deployed to mitigate this jamming effect and enhance the sum throughput. This study addresses both an upper bound design and a robust design with fractional nonlinear energy harvesting model. The primary optimization goal is to maximize the anti-jamming sum throughput, with the constraints of RIS phase shifts and time scheduling. For both designs, closed-form expressions for time scheduling are derived using the Lagrangian duality and Karush-Kuhn-Tucker (KKT) conditions. The quadratic transformation (QT) technique is used to handle fractional functions within the optimization. Furthermore, the phase shifts are optimized iteratively using the element-wise block coordinate descent (EBCD) and Riemannian manifold optimization (RMO) algorithms. Simulation results are presented to validate the effectiveness of the proposed approaches.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods.

This paper investigates a secrecy energy efficiency (SEE) optimization problem for a multiple-input multiple-output non-orthogonal multiple access network. In particular, a multi-antenna transmitter intends to send two integrated service messages: a confidential message for the stronger user and a broadcast message for both stronger and weaker users. It is assumed that both users are equipped with multi-antennas. In this secure wireless network, we consider the transmit covariance matrices design of confidential and broadcast message, under broadcast energy efficiency (BEE) constraint. In addition, it is assumed that the weaker user might turn out to be a potential eavesdropper due to the broadcast nature of wireless transmission. We formulate this transmit covariance matrices design as an SEE maximization problem which is non-convex in its original form due the non-linear fractional objective function and constraints. To realize the solution for this problem, we utilize non-linear fractional programming and difference of concave (DC) functions approach which facilitate to reformulate it into a tractable form. Based on the Dinkelbach's algorithm and DC approximation method, we propose iterative algorithms to determine a solution to the original SEE maximization problem. Numerical results are provided to demonstrate the performance of the proposed transmit covariance matrices design to maximize the SEE.

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

In this paper, we study a joint energy harvesting (EH) and task offloading (TO) design for an energy- constrained fog computing network, which consists of a mobile terminal and two fog nodes. The energy- constrained terminal employs time switching (TS) protocol to harvest energy from the signals sent by the circuit-powered fog node, and then exploits the harvested energy to perform local computing and offload computing. Our aim is to minimize the product of energy consumption and time delay with the constraints of TS ratio and EH requirements. To determine the optimal solution of the original non-convex problem, we decouple it into three subproblems based on the time delay assumption and then solve these subproblems through our proposed constraints activation algorithm. Furthermore, we also derive the closed form expressions for the optimal TO and TS ratios. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.