Xiaokai Song

The integration of fluid antenna systems (FAS) into integrated sensing and communication (ISAC) networks offers promising reconfigurability for air–ground scenarios in low-altitude economic (LAE) environments. This paper investigates a monostatic FAS-aided ISAC architecture comprising a two-dimensional (2D) FAS-based transmit array and a fixed-position receive array, aiming to enhance the joint performance across multiple ground user equipments (GUEs) and one aerial user equipment (AUE). A joint optimization problem is formulated to maximize a weighted utility metric integrating the communication signal-to-interference-plus-noise ratio (SINR) of the AUE and the sensing signal-to-clutter-plus-noise ratio (SCNR), subject to the transmit power constraint, inter-antenna spacing constraint, deployment geometry, and the minimum SINR requirement of GUEs. The resulting problem involves coupled spatial variables and fractional structures, which are addressed via an alternating optimization (AO) framework. Specifically, the receive beamformer is derived in closed form, the transmit beamformer is optimized via fractional programming (FP) and successive convex approximation (SCA), and the antenna positions are iteratively refined on a per-element basis through second-order Taylor expansion to construct convex surrogate upper bounds. Numerical results verify that the proposed FAS-enhanced ISAC system substantially outperforms benchmark schemes.

Integrated sensing and communication (ISAC) is regarded as a promising solution for the development of the emerging low-altitude economy (LAE). Given the necessity of accurate and realistic wireless channel models for ISAC evaluation and optimization, this paper proposes an LAE-oriented ISAC channel modeling framework. By incorporating the target unmanned aerial vehicle (UAV) scattering response, we decouple the ISAC channel into target and background channels. For the target channel, the model is formulated as a cascade of the transmitting base station (BS)-target link, the target-sensing BS link, and the scattering response from the target UAV. For the background channel, a novel parameter is introduced to separate the influence of the target UAV. Furthermore, key statistical properties, including the space-time-frequency correlation function, coherence distance, Doppler power spectral density, root mean square delay spread, and stationarity interval, are derived and analyzed. The accuracy of the proposed channel model is verified by the close agreement between simulated statistics properties and measured data, and the effectiveness of the cascaded method is validated by the close agreement between the concatenation output and simulation results.

Integrated sensing and communication (ISAC) is envisioned as a promising technology to empower the emerging low-altitude economy (LAE). However, current networks primarily serve terrestrial users, resulting in insufficient low-altitude coverage. Furthermore, standalone ISAC systems exhibit restricted sensing ranges, as fixed antenna orientations suffer from significant gain loss at large angles. In view of this, we propose cooperative ISAC enabled by rotatable antenna (RA) technology. RA aligns its boresight with target directions through antenna rotation, effectively mitigating gain loss at large angles. This compact and low-complexity design provides robust coverage and high-precision sensing, making it well-suited for deployment in the LAE. Specifically, multiple RA-equipped base stations (BSs) employ coordinated beamforming to provide robust connectivity for authorized unmanned aerial vehicles (UAVs), while concurrently monitoring distributed critical locations within the target airspace for potential intrusions. To implement this scheme, we maximize the system sum-rate subject to various practical constraints by jointly optimizing BS-UAV associations, transmit beamforming, RA orientation, and UAV trajectories. This nonconvex problem is addressed by an efficient alternating optimization (AO) algorithm that integrates semidefinite relaxation (SDR) and successive convex approximation (SCA). Finally, numerical results demonstrate that the proposed RA-enhanced ISAC system outperforms baselines in line-of-sight (LoS)-dominated, large-angle low-altitude scenarios.

Self-sustainable RIS can harvest energy to maintain its operation by adaptively adjusting its reflection coefficient matrix, which brings convenience in deployment and maintenance. In this letter, we consider a self-sustainable RIS-assisted multi-input multi-output simultaneous wireless information and power transfer (SWIPT) system. Our aim is to maximize the weighted sum power received by energy users while considering the minimum data rate requirements of information users and the minimum energy demand of RIS. To tackle the non-convex and highly coupled sum power maximization problem, we first employ minimum-mean-square error (MMSE) method to reformulate the original problem. Subsequently, we propose a block coordinate descent-based framework that decouples the reformulated problem into two subproblems which can be solved iteratively. In addition, successive convex approximation (SCA) method is adopted to approximate the non-convex objective function and constraint. Finally, simulation results confirm that the self-sustainable RIS can significantly improve the energy transmission performance.

Reconfigurable intelligent surfaces (RISs) have emerged as a crucial technology for making sustainable and green communication in future sixth-generation (6G) networks. By adaptively adjusting the wireless environment, the RIS facilitates flexible control over signal propagation. When an RIS is integrated into a simultaneous wireless information and power transfer (SWIPT) system, it can improve energy-efficiency (EE) and link reliability. This enables low-power Internet of Things (IoT) devices to achieve continuous energy acquisition while maintaining reliable data access. This capability aligns well with the increasing demands of future 6G networks for enhancing EE and achieving ubiquitous connectivity. Motivated by this, we investigate an RIS-assisted multiuser SWIPT system that accounts for the nonlinear characteristics of the high-power amplifier (HPA) at the transmitting end and the nonlinearity of the energy harvesting (EH) circuits at the receivers. These hardware imperfections pose major challenges for system modeling and optimization, particularly under stringent power limitations and quality-of-service (QoS) requirements, where improving overall EE is a key objective. Therefore, we proposed a joint optimization framework that simultaneously designs the access point (AP) beamforming, the RIS reflection coefficients, and the power splitting (PS) ratios at the receivers. The resulting problem is nonconvex, with strong coupling among these variables. To tackle this issue, we propose an efficient alternating optimization (AO) framework. The fractional EE objective is handled using the Dinkelbach method, while each subproblem is iteratively convexified via successive convex approximation (SCA) and semi-definite relaxation (SDR) algorithms. Simulation results reveal that the proposed AO approach achieves substantial EE gains and confirm that integrating RIS conspicuous boosts the overall system EE performance.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where IRS adopts power splitting (PS) protocol, time switching (TS) protocol or element splitting (ES) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while accounting for the constraints in terms of maximum transmit power, IRS reflection coefficients, the energy harvesting requirements of both users and the IRS. The WSR maximization problem is non-convex due to the coupling among variables. For the formulated problem, we propose an alternating optimization (AO) framework to transform it into several subproblems and solves them iteratively. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Finally, simulation results demonstrate that employing a self-sustainable IRS together with the proposed algorithm yields a WSR improvement of 32%–60%.

Reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) communication has emerged as a crucial solution for providing reliable data services to vehicles on the road. However, in delay-sensitive or high-mobility communications, the rapid movement of vehicles can lead to random scattering in the environment and time-selective fading in the channel. In view of this, we investigate in this paper an innovative linear model with low-complexity transmitter signal design and receiver detection methods, which boost stability in fast-fading environments and reduce channel training overhead. Specifically, considering the differences in hardware design and signal processing at the receiving end between uplink and downlink communication systems, distinct solutions are proposed. Accordingly, we first integrate the Rician channel introduced by the RIS with the corresponding signal processing algorithms to model the RIS-aided downlink communication system as a Doppler-robust linear model. Inspired by this property, we design a precoding scheme based on the linear model to reduce the complexity of precoding. Then, by leveraging the linear model and the large-scale antenna array at the base station (BS) side, we improve the linear model for the uplink communication system and derive its asymptotic performance in closed-form. Simulation results demonstrate the performance advantages of the proposed RIS-aided high-mobility communication system compared to other benchmark schemes.

Cell-free network and intelligent reflecting surface (IRS) are considered as two promising technologies for improving future network capacity and coverage. They offer advantages such as low cost, low energy consumption, and compliance with green communication requirements. However, the static deployment of IRS restricts the network's ability to adapt to emergency coverage requirements and dynamic environments. To address this issue, we propose a flexible IRS-aided cell-free network, where network capacity and signal coverage are substantially improved by utilizing reflected signals from an aerial IRS. Our objective is to maximize the weighted transmission rate for users by jointly optimizing the active beamforming of the base stations (BSs), passive beamforming of the IRS, and the location of the unmanned aerial vehicle (UAV). Due to the non-convex and intractable nature of this problem, we decompose it into three subproblems. For the optimization problems of BSs' active beamforming and IRS's passive beamforming, we transform the log-sum problem into a quadratically constrained quadratic programming (QCQP) problem by employing the lagrangian dual principle and multi-ratio fractional programming. Furthermore, for the more challenging location optimization, we further transform it into a convex problem using the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Simulation results demonstrate that the proposed scheme can significantly improve the weighted transmission rate and effectively enhance the network coverage as compared to other benchmarks.