Strong Physical Unclonable Functions (PUFs) are considered the most promising circuits for lightweight IoT authentication. However, current white-box attacks on PUF circuits through reverse engineering compromise the effectiveness of many anti-modeling attack techniques. To address this challenge, we propose a configurable four-state Hybrid PUF (CF PUF), which includes three strong PUFs and one weak PUF. The proposed structure uses the key stream generated in the weak PUF state to control the configuration state transition sequence of the CF PUF. This approach allows the PUF’s challenge-response mapping structure to be dynamically controlled by the unpredictable weak PUF, rather than relying on a fixed PUF structure, thereby providing enhanced resistance to white-box attacks through reverse engineering. To hide the weak PUF characteristics and prevent reverse analysis attackers from using divide-and-conquer methods to break the weak PUF, we employ LUT6_2 resources to embed the weak PUF structure into strong PUFs with minimal hardware overhead. The proposed CF PUF was implemented and evaluated on an Xilinx Artix-7 FPGA hardware platform. Experimental results demonstrate that the proposed solution achieves good prediction resistance, with the best modeling accuracy limited to 54.6% under 1M CRPs across Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) attacks. Moreover, it maintains good reliability, averaging 98.02% under temperature variations from -20 °C to 80 °C.

Multiple-input multiple-output wireless sensing systems achieve high-resolution detection through spatial diversity. However, they suffer from reliability degradation under low signal-to-noise ratio (SNR) conditions. Conventional matrix-based methods may discard critical target information embedded in inter-dimensional signal correlations due to dimension-reduction flattening operations. To overcome these limitations, this paper proposes a tensorial target detection (TTD) framework combining noise reduction and enhanced detection specifically designed for high-dimensional processing. Firstly, we propose a two-stage tensorial noise reduction (TNR) method based on the minimum mean square error criterion and the alternating least square iteration strategy to remove noise in high-order signal space. We further identify the sub-optimal performance caused by inter-dimensional noise coupling at the first stage of TNR, and resolve the issue via rank-constrained optimization for noise-target subspace separation at the second stage of TNR. Then, we develop an augmented tensorial detector based on cross-shaped spatial partitioning (CSP) to enhance detection performance, which jointly optimizes detection thresholds by adaptively refining noise estimation. Finally, field measurements confirm the TTD framework's operational validity, while in simulation the TNR method achieves 5 dB SNR improvement over 2D method, and the CSP-based detector delivers a 21% enhancement in detection probability over conventional approaches.

As a lightweight hardware security primitive, physical unclonable functions (PUFs) can provide reliable identity authentication for the Internet of Things (IoT) devices with limited resources. Arbiter PUF (APUF) is one of the most well-known PUF circuits. However, its hardware implementation has poor reliability on field programmable gate arrays (FPGAs). This paper proposed a highly reliable APUF that uses a delay difference quantization strategy (DDQ-APUF). By adding multiple configurable delay units to the two symmetrical paths of the conventional APUF, the delay difference between the two symmetrical paths of APUF can be obtained by collecting the output of APUF under different delay configurations. Compared to conventional APUFs, DDQ-APUF does not use the arbitration result of signal transmission in two symmetric paths as its response, but rather uses the quantified delay difference between the two paths as its response. A tolerance threshold is adopted in the authentication to accommodate the variations in delay differences due to environmental changes. Moreover, the modeling attack resistance of DDQ-APUF is evaluated, and a strategy for improving this resistance by incorporating pseudo-XOR technique is proposed. The circuit was implemented on Xilinx Artix-7 FPGAs and the experimental results show that the reliability achieves 99.95% with non-CRP-discard.

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

This paper investigates the hybrid beamforming design for the reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cell-free networks, where the access points (APs) connected to the central processing unit serve multiple users cooperatively. The weighted sum rate of uplink and downlink transmissions is maximized by jointly optimizing the digital and analog beamformers, the phase-shift coefficients of RISs and the uplink transmit power while satisfying the power budget constraints of the APs and users. For solving the problem, the objective function is reformulated using Lagrangian dual transform and fractional programming. Then, to tackle the variables coupling, the problem is divided into five subproblems that are solved iteratively via a proposed block coordinate descent (BCD)-based algorithm. To handle the unit-modulus constraint on analog beamformers and phase-shift coefficients, a monotonic fast proximal gradient (mFPG)-based method is proposed under the alternating direction method of multipliers (ADMM) framework. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm compared to three baseline algorithms. The impacts of the numbers of radio frequency chains and reflecting elements on the uplink and downlink transmissions are presented, respectively. The performance comparison between the FD and half-duplex schemes is provided.

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

In this paper, we study a robust beamforming design in a downlink reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication system with consideration of bounded channel uncertainty. The goal of the design is to minimize the transmit power by employing the symbol-level precoding (SLP) at the base station (BS), while satisfying the quality-of-service requirements of the primary users (PU) and secondary user (SU). Unlike most existing works where RIS is only a signal reflector, in this paper, RIS also operates as a transmitter and delivers the secondary information to SU by switching the reflecting beamformers. In single-PU scenario, the secondary information recovery (SIR) scheme is proposed and a power minimization problem is formulated. To tackle the non-convex problem, we decompose the problem into two sub-problems to alternately optimize the transmit and reflecting beamformers. Then, two algorithms namely the penalty-based algorithm and the monotone accelerated projected gradient-based algorithm are proposed to address the unit-modulus constraint on the reflecting beamformer. The problem is further extended to the multi-PU scenario and the extended SIR scheme is provided. Finally, the simulation results demonstrate the effectiveness of our proposed algorithms and exhibit the advantages of the SIR scheme.

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs.

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

This paper investigates beam design in a millimeter wave (mmWave) network, where some obstacles block the link between the base stations (BSs) and the user equipments (UEs). There is a single cell in an area where the UEs communicate with the single BS through a line of sight (LoS) link. To provide services in the network, synchronization signal blocks (SSBs) are transmitted by the BS in a fixed beam pattern. We aim at minimizing the transmit power by designing the beam while meeting the coverage requirement, which depends on the reference signal received power (RSRP) at the UEs. A non-convex power minimization problem is then formulated by jointly optimizing the beam directions, beam width and beam number subject to the coverage constraints. To resolve this problem, an alternating optimization (AO) based algorithm is proposed and the successive convex approximation (SCA) method is applied. The simulation results demonstrate that the proposed algorithm can significantly improve the performance of power consumption compared with the other two heuristic algorithms.

This paper considers a positioning problem of high-speed trains (HST) in railway wireless network by utilizing the 5G new radio (NR) positioning reference signals (PRS). It is assumed that the train runs along the railroad at a fixed velocity and receives PRS signals from the base stations (BSs) deployed on the side of the railroad. To deal with the positioning problem, an iterative two-phase weighted least squares (I2WLS) method based on range difference of arrival (RDOA) measurements is proposed, which linearizes the RDOA equations to pseudo-linear ones. The accuracy gap between RMSE of the proposed approach and Cramer-Rao lower bound (CRLB) is small when the RDOA measurements noise level is sufficiently small. Furthermore, the simulation results illustrate that a high, centimeter-level accuracy can be achieved for small PRS intervals, velocities and base station distances.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but with almost the same power to the SDR-based algorithm. Then, we apply the AO technique to the multiuser case and thereby develop an algorithm based on the proximal gradient descent (PGD) method. The algorithm can be generalized to the case of finite-resolution IRS and the scenario with direct links from the transmitter to the users. Numerical results show that the SLP can significantly improve the system performance. Meanwhile, 3-bit phase shifters can achieve near-optimal power performance.