Yu Xu

The extremely large-scale reconfigurable intelligent surface (XL-RIS) is a highly promising technology for the sixth-generation (6G) wireless communications. However, the conventional near-field beam training (NFBT) for XL-RIS is intractable and incurs excessively high beam training overhead. To solve this problem, we propose a two-stage hierarchical NFBT scheme for XL-RIS. In the first stage, we generate a hierarchical codebook based on the proposed 3D binary-tree search strategy, which can iteratively and evenly divide the beam search region into sub-regions, wherein the sampling point pair formed by the center points of the BS sub-region and the user sub-region corresponds to a codeword in the codebook. Then, we perform NFBT on this hierarchical codebook to achieve coarse beam alignment in the downlink. In the second stage, we perform NFBT based on the globally optimal codeword of the first stage to achieve fine-grained beam alignment. Numerical results show that the proposed two-stage hierarchical NFBT scheme can significantly reduce the beam training overhead while achieving high rate.

Conventional simultaneous wireless information and power transfer (SWIPT) systems are inherently limited by the constrained spatial degrees of freedom (DoFs) associated with fixed-position antenna (FPA) arrays, thereby impeding the effective dynamic optimization of channel conditions. This paper proposes a near-field SWIPT framework based on movable antenna (MA) technology, with the goal of maximizing the total achievable rate for information decoding (ID) users while satisfying the power harvesting requirements of energy harvesting (EH) users, by jointly optimizing antenna positions and beamforming matrices. To address the intractable non-convex problem, we propose an efficient alternating optimization (AO) algorithm by invoking the semidefinite relaxation technique, the Dinkelbach method, and the successive convex approximation approach. Simulation results demonstrate that the proposed algorithm achieves significant improvement in both total achievable rate and energy harvesting power, while retaining its remarkable advantages in signal quality enhancement through MA.

We propose a unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and jamming (U-ISJC) framework, in which a multifunctional UAV first detects the sensing target to obtain sensing information, and subsequently transmits the information to communication users via a unified beam in the presence of multiple eavesdroppers. To avoid functional conflicts, a time slot frame structure is designed for the UAV’s multifunctional capabilities, enabling communication, sensing, and jamming tasks within each timeslot. The time slot allocation factor dynamically adjusts based on the UAV’s flight trajectory for efficient UAV resource utilization. Additionally, to prevent security rate leakage caused by eavesdroppers, a jamming beam is added to serve both jamming and sensing functions. Our objective is to maximize the the worst-case total secure data transmission rate by jointly optimizing sub-time slot allocation, beamforming, and UAV trajectory. To address this problem, we propose a joint optimization algorithm that adopts the concave–convex procedure (CCCP) technique and semi-definite relaxation (SDR), under the block coordinate descent (BCD) framework. The simulation results show that compared with the baseline scheme, the proposed algorithm substantially improves the communication security rate while ensuring the quality of communication and sensing.

To address the waste of spectrum resources caused by the separate operation of integrated sensing and communication (ISAC) and mobile edge computing (MEC), the integrated sensing, communication, and computing (ISCC) paradigm has been proposed. However, ISCC systems are still facing serious channel fading and obstacle occlusion problems, which are expected to be solved by the emerging simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In this article, we propose a STAR-RIS-assisted ISCC framework, where the computational tasks from users are offloaded to a base station (BS) for processing with the assistance of a STAR-RIS, and the results are subsequently downloaded back to the corresponding users. Notably, target sensing operation is executed concurrently throughout the process of result downloading. To enhance communication efficiency, the nonorthogonal multiple access (NOMA) scheme is incorporated. The number of offloading bits of users is maximized by optimizing the uplink–downlink resource of the network, including the received beamforming vector, active beamforming vector, time slot, phase shift of STAR-RIS, and computational resource. In order to solve the highly complex nonconvex problem, we employ the block coordinate descent (BCD) framework to decompose the proposed problem into three subproblems. The convex–concave procedure (CCCP) method is used to deal with the nonconvex constraints. For the rank-1 constraints, the semidefinite relaxation (SDR) approach and the penalty method are invoked. The numerical results show the following: 1) the application of STAR-RIS significantly improves the offloading capability of the network; 2) the NOMA scheme significantly outperforms the orthogonal multiple access (OMA) scheme; and 3) comparing with other benchmark schemes, the proposed algorithm significantly improves the overall network performance.

In this paper, we consider a UAV-assisted cargo delivery system with limited payload capacity. Due to the limited load capacity of the cargo UAV, it needs to make multiple trips to the warehouse to pick up the parcels. Meanwhile, due to the uneven distribution of cellular signal strength in the air, to send logistics information to ground users (GUs) in time, the cellular-connected UAV needs to bypass the weak signal area in the air. Therefore, these two factors lead to the increase of the total cargo delivery time. To reduce the total delivery time and ensure the communication quality of the UAV, we formulate an objective function to be optimized, which is the weighted sum of the delivery time and the communication outage time of the cargo UAV. We propose a limited payload UAV delivery (LP-UAV-D) framework to solve this problem. The framework consists of the particle swarm optimization (PSO) algorithm and the dueling double deep Q network (D3QN) algorithm. We used two classic algorithms as control groups. The numerical results show that regardless of the maximum payload or flight speed of the UAV, the objective function value obtained through our proposed LP-UAV-D framework and with the help of radio maps is always the smallest. Specifically, the performance of solving the trade-off problem between delivery time and communication quality is improved by about 10%-20% compared with the two comparison algorithms.

Recently, movable antenna (MA) has become a prominent research topic in wireless communications due to the flexible mechanical movement. In this letter, a MA-assisted unmanned aerial vehicle (UAV) relaying framework is proposed, where the UAV relay delivers the information from a source node (SN) to multiple destination nodes (DNs). The goal is to maximize the total throughput of DNs, by jointly optimizing beamforming, time slot allocation, antenna position, and UAV trajectory. To solve the highly complex non-convex problem, we propose an efficient penalty-based alternating optimization algorithm. Specifically, the successive convex approximation technique is adopted to deal with the non-convex constraints, and the penalized method is exploited to handle the rank-one constraints. Numerical results demonstrate that the MA outperforms the conventional fixed-position antenna (FPA), and the proposed algorithm significantly enhances network throughput compared to other benchmark schemes.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted hybrid far- and near-field (HFNF) simultaneous wireless information and power transfer (SWIPT) framework is proposed, where the energy users are located in the near-field region while the information users are located in the far-field region. The weighted harvested power of near-field energy users is maximized by jointly optimizing the active beamforming of base station (BS) and the passive beamforming of STAR-RIS, subject to the communication quality of far-field communication users. To solve this non-convex problem, a two-layer penalty-based algorithm is proposed. For the inner layer, the concave-convex procedure (CCCP) technique is invoked to address non-convex difference-of-convex (DC) expressions. For the outer layer, the penalized approach is used to guarantee the rank-1 solution. Numerical results show that: 1) the proposed algorithm achieves significant energy harvesting enhancement; 2) the STAR-RIS outperforms both reflecting-only and transmitting-only RIS; and 3) in contrast to far-field beamformers, near-field beamformers enable energy to be concentrated directly around energy users, thereby improving energy harvesting performance.