In vehicle-to-everything (V2X) scenarios, the high dynamic characteristics of V2X environments impose significant challenges on communication channel estimation, where the emerging integrated sensing and communication technology could serve as a vital tool for achieving accurate channel estimation. This paper leverages radar-sensed angle information to assist in communication channel estimation and proposes a deep unfolding-based radar-assisted channel estimation network (Radar-CEnet). Specifically, for the radar module, to address the challenges posed by insufficient data in imperfect arrays, we employ a model-agnostic meta-learning with a convolutional neural network (MAML-CNN) approach to achieve high-precision direction-of-arrival (DOA) estimation. Then, the angle information obtained by the radar module, as prior knowledge, is used for channel estimation. Building on this, we design a novel soft-thresholding shrinkage function and propose the Radar-CEnet algorithm to efficiently estimate the sparse channel. Finally, we rigorously prove the convergence of the Radar-CEnet algorithm and demonstrate that it achieves a lower estimation error. Experimental results show that the proposed Radar-CEnet outperforms existing traditional methods and deep learning-based approaches in channel estimation performance. At an SNR of 20dB, the proposed Radar-CEnet method reduces the NMSE from –23.75dB to –27.15dB compared to the learning-based iterative soft-thresholding method, achieving an estimation accuracy improvement of approximately 54%.

As wireless communication advances toward the 6G era, the demand for ultra-reliable, high-speed, and ubiquitous connectivity is driving the exploration of new degrees-of-freedom (DoFs) in communication systems. Among the key enabling technologies, Movable Antennas (MAs) integrated into Flexible Cylindrical Arrays (FCLA) have shown great potential in optimizing wireless communication by providing spatial flexibility. This paper proposes an innovative optimization framework that leverages the dynamic mobility of FCLAs to improve communication rates and overall system performance. By employing Fractional Programming (FP) for alternating optimization of beamforming and antenna positions, the system enhances throughput and resource utilization. Additionally, a novel Constrained Grid Search-Based Adaptive Moment Estimation Algorithm (CGS-Adam) is introduced to optimize antenna positions while adhering to antenna spacing constraints. Extensive simulations validate that the proposed system, utilizing movable antennas, significantly outperforms traditional fixed antenna optimization, achieving up to a 31% performance gain in general scenarios. The integration of FCLAs in wireless networks represents a promising solution for future 6G systems, offering improved coverage, energy efficiency, and flexibility.

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.