This paper investigates a secure beam-scanning framework for near-field integrated sensing and communication (ISAC) systems, driven by location division multiple access (LDMA). Specifically, an ISAC base station performs full-map robust beam-scanning within each transmission cycle, aiming to simultaneously detect potential eavesdroppers (Eves) and ensure secure communication for legitimate users (Bobs). Based on the Bobs’ channel state information (CSI) obtained at the cycle’s start and the estimated CSI of Eves sensed in the previous cycle, we formulate a robust optimization problem. This problem jointly optimizes the hybrid analog-digital precoding and time allocation for beam-scanning, with the objective of maximizing the worst-case average sum secrecy rate. To simplify the solution process, we first eliminate or relax the semi-infinite constraints caused by uncertain multipath channels from two perspectives: convex hull and bounded uncertainty. Subsequently, we design a near-field LDMA codebook in both azimuth and distance domains to construct ideal radar beampatterns for covering and partitioning the spatial scanning region. We also develop efficient analog precoders to significantly reduce computational complexity. Based on the convex hull model, we develop a low-complexity alternating optimization (AO) algorithm. In addition, for the bounded uncertainty model, we propose a semidefinite relaxation-based AO algorithm without requiring a rank-one constraint. Simulation results demonstrate that the proposed framework enables effective full-map Eves sensing while guaranteeing secure communication for Bobs. Moreover, the convex hull-based algorithm exhibits superior robustness and scalability compared to conventional bounded uncertainty approaches.

This paper investigates a near-field covert communication system enhanced by an absorptive reconfigurable intelligent surface (ARIS) and a fluid antenna system (FAS), enabling covert transmission to arbitrary receiver locations. By generating near-field spherical waves via large-scale antenna arrays at Alice and ARIS, covert transmission to Bob is enabled while evading detection by Willie. We jointly optimize Alice’s hybrid precoding, ARIS reflection coefficients, and Bob’s active port selection to maximize the worst-case covert transmission rate. We begin by evaluating ARIS’s suitability versus conventional RIS. We demonstrate the asymptotic orthogonality of near-field beam-focusing vectors in the 3D domain for uniform planar arrays, and characterize the beam-focusing behavior in cascaded ARIS-enabled covert transmissions. Additionally, we reveal the channel gain improvement owing to FAS over traditional antenna systems. To solve the coupled non-convex problem, we propose a low-complexity block coordinate descent algorithm. It incorporates Fibonacci search for hybrid precoding, three complexity-performance trade-off strategies for reflection coefficients optimization, and both exhaustive search and linear conic relaxation for active port selection. Finally, we recover precoding via an alternating minimization scheme. Numerical results show that (i) significant improvement of covert transmission is achieved only with both ARIS and FAS, when Bob and Willie are co-located; (ii) the proposed algorithm outperforms near-field and far-field beam alignment schemes without ARIS, as well as beam focusing of full-map zeroing with ARIS, when Bob and Willie share the same reception direction.

In this paper, we investigate a near-field Internet of Vehicles scenario that utilizes a dual-array antenna architecture and incorporates a transmitting reconfigurable intelligent surface (T-RIS)-assisted integrated sensing and communication (ISAC) to assist driving. A high-frequency large antenna phased array constructed from low-cost energy-efficient T-RIS forms a near-field spherical wave electromagnetic environment. The vehicle communicates with a roadside small base station (SBS) while performing radar detection of the region of interest (RoI) to enhance driving safety. First, we use the kinematic behavior of the vehicle to predict the RoI. Then, we propose a unified optimization framework for all architectures aimed at maximizing the overall gains in both sensing and communication performance, investigating performance upper bounds and associated trade-offs, and proposing a fairness-enhanced scheme. Finally, we propose an integrated sensing, positioning, and communication scheme that derives the multi-objective Cramér-Rao Bound (CRB) for the joint estimation of azimuth, elevation, and range information, and we formulate and solve the problem of minimizing the CRB with guaranteed communication quality. Simulation results demonstrate that the proposed schemes effectively focus on both the RoI and SBS regions, contributing to enhanced driving safety. Additionally, our findings reveal the inherent performance trade-offs between sensing and communications in vehicular networks, and validate the energy efficiency superiority of the proposed T-RIS-aided architecture.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios.

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms.

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance.

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min security EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained concave-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves’ links, and investigate the robust security EE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the Bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP, and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

In this paper, we investigate the energy efficiency (EE) problem in downlink reconfigurable intelligent surface (RIS)-aided secure cell-free networks. First, we formulate a max-min secure EE (SEE) optimization problem via jointly optimizing the distributed beamforming at base stations and phase shifts at RISs under the constraint of each base station transmit power. To deal with it, we divide the original optimization problem into two sub-ones and propose an alternative scheme. Specifically, we develop an iterative optimization algorithm to solve each sub-one based on the fractional programming, constrained convex-convex procedure and semi-definite programming techniques. After that, these two sub-ones are alternatively solved until convergence, and then the final solutions are obtained. Finally, the simulation results show the effectiveness of the proposed algorithm.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS’ CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user’s secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Meanwhile, we also study the system performance under the imperfect CSI. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.