Reconfigurable intelligent surfaces (RISs), have become a revolutionary wireless channel control paradigm as a potential solution for future 6G networks. However, in many scenarios, due to the influence of “multiple-scatterer” environments, the existing RIS-assisted multi-user systems still have limitations in terms of capacity. Therefore, this paper proposes the novel concept of active RIS-assisted movable antenna systems (MAs), which can optimize channel conditions and improve communication performance by locally moving the antenna solely in the receiving area. Additionally, the optimization problem for RIS-assisted multi-user multiple-input single-output (MU-MISO) systems, which aims to maximize the sum-rate among multiple users, also entails beamforming optimization at the base station (BS), phase shift design at the RIS, and MAs positioning. To solve this challenging and nonconvex optimization problem, in the case of a fixed MA's position, the Lagrange dual transform is used to introduce auxiliary variables to decouple the original problem into three subproblems: the optimization of auxiliary variables, the optimization of beamforming transmission on the BS, and the phase shift design of the RIS. Furthermore, to identify the optimal positions of the MAs, an adaptive fractional programming quadratic transform algorithm based on a genetic algorithm (GA) (FPQT-GA) is proposed to investigate the proposed system. The simulation results show that compared with traditional fixed antenna systems (FAs) active RIS-assisted, the employment of MAs can significantly enhance the system's overall performance in typical application scenarios.

Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches.

In this paper, we propose a channel feature projection clustering (CFPC) based joint channel state information and direction of arrival (JCDoA) estimation scheme for integrated sensing and communication (ISAC) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Specifically, the spatial-delay domain coarse channel estimation scheme is introduced first, and then the angular-domain coarse direction of arrival (DoA) estimation is presented. In order to improve the performance of channel estimation, the angular subspace pruning (ASP) based channel state information (CSI) refinement scheme is proposed. Finally, the DoA refinement scheme based on angular-domain CFPC is proposed. The simulation results show that the proposed JCDoA scheme outperforms traditional least square (LS) and linear minimum mean square error (LMMSE) channel estimation schemes, multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) DoA estimation methods and their spatial smoothing (SS) enhancement schemes.

Due to the sharing of communication and sensing signals, integrated sensing and communication (ISAC) systems are vulnerable to potential eavesdropping attacks. This article investigates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled secure ISAC for Internet of Everything (IoE) networks. Two typical ISAC scenarios are explored: 1) communication enhancement (CE) and 2) communication-sensing fusion (CSF). In the CE scenario, the communication user (CU) is located in the Non-Line of Sight (NLoS) region of the ISAC base station (ISAC-BS), while the sensing target is located in the Line-of-Sight (LoS) region of the ISAC-BS. In the CSF scenario, both the CU and the sensing target are located in the NLoS region of the BS. The STAR-RIS is deployed to improve communication and sensing performance. The artificial noise (AN)-aided robust and secure beamforming problems are formulated for different scenarios based on imperfect channel knowledge. Several STAR-RIS protocols are also considered to provide a more complete system design and performance analysis. Efficient iteration-based algorithms are developed to solve these nonconvex and highly coupled problems. Based on simulation results, we observe that: 1) the proposed methods can effectively improve the system performance and 2) from the perspective of communication security, the dedicated AN signal is helpful for STAR-RIS-enabled ISAC systems.

In this article, we propose a novel localization and channel estimation integration framework for cell-free massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, in which position information supports accurate channel estimation and accurate channel information can, in turn, improve positioning accuracy. Under this integration framework, we propose a two-phase fingerprint-based localization method consisting of both initial and accurate localization phases and a coarse-location-based (CLB) pilot reassignment scheme. The coarse location information for pilot reassignment is obtained in the initial localization phase of the two-phase localization method, and the fingerprint information used in the accurate localization phase is extracted through channel estimation based on the CLB scheme. Furthermore, for localization, two different fingerprint similarity criteria are proposed to meet the requirements of the different localization phases. Simulation results demonstrate that our proposed two-phase fingerprint-based localization method achieves better positioning performance than existing methods, although there is a slight increase in computational complexity compared to the initial localization. Moreover, our proposed CLB pilot reassignment scheme outperforms the conventional pilot assignment schemes in the comprehensive performance considering both channel estimation performance and complexity.