To ensure the thriving development of low-altitude economy, countering unauthorized uncrewed aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming (JCJ). In the light of this, this article investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input-multiple-output (MIMO) cellular systems. We first formulate a JCJ problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Based on these insights, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas. Simulation codes are provided to reproduce the results in this article:https://github.com/LiZhuoRan0.

The Extremely-large Aperture Reconfigurable In-telligent Surface (RIS) stands out as a promising technology for future 6G communications. However, existing far-field or near-field channel models struggle to adapt effectively to channel estimation in the context of Extremely-large Aperture RIS-assisted wireless communication under a hybrid field. To address this challenge, this paper introduces an efficient hybrid-field channel estimation scheme tailored for Extremely-large Aperture RIS-assisted wireless communication. In this scheme, we initially extend the one-dimensional polar coordinate dictionary to a full-dimensional spherical coordinate dictionary to achieve a more uniform distribution of grid points in the spherical coordinate-domain. Subsequently, we propose a hybrid passive/active RIS architecture, utilizing a limited number of Radio Frequency (RF) chains to acquire channel observations. Finally, we introduce a hybrid-field channel estimation scheme designed to estimate both far-field and near-field components. Simulation results demonstrate that the proposed scheme outperforms purely far-field or near-field schemes.

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UM-MIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios.

Abstract In the internet of vehicles (IoV) networks, a direct connection from the source end to the destination end may not be established due to the fast vehicle speed, long distance between vehicles, variable vehicle density, serious channel fading etc. In this paper, a joint resource allocation (RA) in the relay‐aided IoV networks is modelled as a mixed binary integer non‐linear programming (MBINP), which maximises the throughput of cognitive IoV networks among different subcarriers and relays. To further reduce the computational complexity, a suboptimal scheme is presented. First, the appropriate relay and subcarrier pairs are obtained by averaging the power allocation among the cognitive sources and relays. Second, an alternative optimisation mechanism is proposed to the power allocation. Simulation results show that, different from the symmetric time‐slot relay transmission, the asymmetric one can significantly increase the degree of freedom for transmission. Therefore, it is more robust to the impact of the relay node location on the throughput. Moreover, the proposed suboptimal RA algorithm not only can obtain the system capacity close to the optimal one, but also can reduce the computational complexity. At the same time, unacceptable degradation caused by severe channel fading is avoided.

In this paper, the resource allocation for vehicle-to-everything (V2X) underlaying 5G cellular mobile communication networks is considered. The optimization problem is modeled as a mixed binary integer nonlinear programming (MBINP), which minimizes the interference to 5G cellular users (CUs) subject to the quality of service (QoS), the total available power, the interference threshold, and the minimal transmission rate. To achieve that, the original MBINP is decomposed into three steps: transmission power initialization, subchannel assignment, and power allocation. Firstly, the minimum transmission power required by the V2X users (VUs) is set as the initial power value. Secondly, the Hungarian algorithm is used to obtain the appropriate subchannel. Finally, an optimization mechanism is proposed to the power allocation. Simulation results show that the proposed algorithm can not only ensure the minimal transmission rate of VUs but also further improve the CUs’ channel capacity under the premise of guaranteeing the QoS of the CUs.