Ying Zhang

This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect channel state information (CSI) for MA placement or adopting a decoupled CSI acquisition and MA placement design paradigm, we address the practical CSI acquisition issue through the design of pilot signals and quantized CSI feedback, and further incorporate the joint optimization of channel estimation, MA placement, and precoding design. The proposed mechanism enables the system to learn an optimized transmission strategy from imperfect channel data, overcoming the limitations of conventional methods that conduct channel estimation and antenna position optimization separately. To balance the performance and overhead, we further extend the proposed framework to optimize the antenna placement based on the statistical CSI. Simulation results demonstrate that the proposed approach consistently outperforms traditional benchmarks in terms of achievable sum-rate of users, especially under limited feedback and sparse channel environments. Notably, it achieves a performance comparable to the widely-adopted gradient-based methods with perfect CSI, while maintaining significantly lower CSI feedback overhead. These results highlight the effectiveness and adaptability of learning-based MA system design for future wireless systems.

This paper presents an end-to-end deep learning framework for electromagnetically reconfigurable antenna (ERA)-aided user localization with active sensing, where ERAs provide additional electromagnetic reconfigurability to diversify the received measurements and enhance localization informativeness. To balance sensing flexibility and overhead, we adopt a two-timescale design: the digital combiner is updated at each stage, while the ERA patterns are reconfigured at each substage via a spherical-harmonic representation. The proposed mechanism integrates attention-based feature extraction and LSTM-based temporal learning, enabling the system to learn an optimized sensing strategy and progressively refine the UE position estimate from sequential observations. Simulation results show that the proposed approach consistently outperforms conventional digital beamforming-only and single-stage sensing baselines in terms of localization accuracy. These results highlight the effectiveness of ERA-enabled active sensing for user localization in future wireless systems.

Multiband fusion (MF) technology can greatly enhance the range resolution of radar by generating ultra-wideband echo using several sub-bands echoes (SBEs). A critical issue of MF is how to accurately estimate and compensate for incoherence between SBEs. In this letter, a novel high-accuracy incoherence compensation method is proposed. Firstly, the phase-coherent coefficient ( PCC ) is introduced to quantify the phase correlation between SBEs, which is not exactly analyzed in the existing MF methods. Then, it is noticed that for PCC, the linear phase and the fixed phase are coupled and difficult to be estimated simultaneously. Accordingly, this letter proposes to estimate the linear phase and the fixed phase separately, where the linear phase is estimated and compensated by applying the information entropy minimization criterion, and the fixed phase is then compensated based on PCC . Finally, the validity and advantages of the proposed method are investigated by numerous simulation results.

Multiband fusion (MF) which generates ultra-wideband echo by interpolating and extrapolating using multiple sub-bands echoes can greatly enhance the range resolution of radar targets. Existing MF methods mainly focus on how to accurately fuse multiple-bands while not considering sub-bands parameter selection. However, the accuracy of MF is influenced by the choice of sub-bands parameters due to target electromagnetic scattering fluctuation. In this letter, taking target scattering fluctuation into account, a sub-bands parameter optimization method based on particle swarm optimization is proposed for MF, which simultaneously maximizes the correlation coefficient and minimizes the relative root-mean-square error between the fused full-band frequency response and the ideal full-band frequency response. The validity and advantages of the proposed method are verified by simulated electromagnetic scattering field data as well as static-range data.

The antenna near-field samples (NFSs) are obtained by a circular array of probes (CAP) in spherical array sampling systems. In order to reduce sampling time, this paper proposes a compressive spherical array sampling technique based on pseudo-skeleton tensor approximation (PSTA). Specifically, the Huygens’ principle and the method of moments are applied to construct a tensor-based radiation model. Then in order to realize the compression of NFSs and thus to reduce the sampling effort, the skeleton sampling positions of CAP are determined via PSTA of the tensor model, where NFSs in non-skeleton sampling positions can be computed from those in skeleton sampling positions. Moreover, a fast algorithm is developed to accelerate PSTA of the tensor model. The ring antenna array and the patch antenna arrays are used to validate that the proposed method is applicable to the antennas with different shapes or multi-beam techniques. Numerical experiments show that the proposed method can determine the skeleton sampling positions with high computation efficiency and reduce the number of samples by 50% compared with uniform sampling.

In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV) assisted integrated sensing and communication system (ISAC), where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users. In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all ground users while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer non-convex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among ground users and UAVs by leveraging matching theory and fractional programming theory. For the non-convex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a sub-optimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.

In this letter, a general coarse-to-fine failure diagnosis method is proposed to accelerate the planar array diagnosis using far-field samples. First, a specific azimuth angle is selected to obtain the far-field pattern in the elevation plane. In this case, an equivalent linear array is formed by projecting antenna elements onto the line of intersection of the elevation plane and the array plane. Then a rough position range of faulty elements is determined by diagnosing the equivalent linear array in the coarse stage. Finally in the fine stage, the faulty elements can be identified fast from a reduced position range. Numerical simulation is carried out to analyze the diagnosis error and diagnosis time of the proposed method, which verifies its superiority over the traditional compressed sensing-based diagnosis method.

In this paper, an integrated sensing and communication (ISAC) system is considered, with a dual function base station designated to serve communication users while sensing interested targets. To enhance throughput and spectrum efficiency, the sum weighted bit rate is maximized to guarantee the effective sensing power through beamforming design. To tackle the non-convexity of the original optimization problem, an alternating optimization algorithm which efficiently combinies fractional programming (FP) with successive convex approximation (SCA) is proposed. Specifically, when the non-convex constraint is approximated by SCA as the difference between an affine function and a constant, the remaining non-convex beamforming design problem is equivalently transformed and can be solved by FP. Simulations results validate the superiority of the proposed algorithm in improving the communication performance while ensuring sensing performance.

Positioning accuracy and efficiency are two important features for embedded positioning devices of Internet of Things (IoT) in smart cities. In modern urban canyon environments, non-line-of-sight (NLoS) satellite signals may degrade positioning accuracy. The mirror ray tracing algorithm can be used to reconstruct NLoS propagation path and enhance positioning accuracy. However, for the embedded positioning devices of IoT, real-time reconstruction of NLoS propagation path is difficult. In this paper, an inherited sampling algorithm ensuring positioning accuracy and efficiency is proposed. Experimental results show that the proposed inherited sampling algorithm saves computation time by 69.8% compared to the double sampling algorithm. Furthermore, a mirror ray tracing accelerator is designed and built in FPGA, which greatly reduces the NLoS path reconstruction time. Finally, a real-time positioning system with a CPU and FPGA heterogeneous architecture is built. For a single trajectory point, test results show that the correction time of the proposed inherited sampling algorithm with an FPGA accelerator takes only 0.45 seconds, which is 80 times faster than that with CPU calculation alone.