Rui Chen

Sixth-generation (6G) wireless communication systems are expected to embrace extremely large aperture arrays (ELAAs), novel antenna architectures, and operation in high-frequency bands to meet the rapidly growing demand for data transmission. By increasing the number of antenna elements, ELAAs enable finer spatial resolution and enhanced beamforming capabilities. At these high operating frequencies, an ELAA aperture may span tens or even hundreds of wavelengths, causing the propagation conditions to gradually depart from the conventional far-field assumption, and making spherical-wave effects increasingly prominent. More generally, near-field behavior is jointly determined by the array size relative to the wavelength and the link distance, and it may also arise in short-range deployments. Consequently, near-field propagation is expected to play a key role in 6G systems. In the near-field region, the electromagnetic field impinges on the array with a non-negligible wavefront curvature. Consequently, the channel is parameterized by both angular information and the propagation distance (range) between transmitter and receiver. This additional distance-dependent degree of freedom increases the dimensionality of the channel parameters and alters the structural properties exploited by far-field channel estimators. As a result, straightforward extensions of conventional far-field channel estimation techniques, typically designed to exploit only angular information, to near-field scenarios may lead to significant high computational complexity. These challenges motivate the development of estimation methods tailored to the distinctive characteristics of near-field propagation. This paper provides a comprehensive overview of recent advances in near-field channel estimation. From an electromagnetic-wave perspective, we first delineate the boundary between near- and far-field regions and highlight the fundamental differences in their propagation mechanisms. We then summarize representative ELAA architectures, spherical-wavefront control techniques, emerging near-field applications, and ongoing standardization efforts related to near-field communications. Next, we introduce widely used near-field channel models and contrast them with their far-field counterparts. Finally, we systematically review major estimation techniques under various system configurations, including single- and multiuser, as well as single- and multi-carrier settings, covering both direct channel estimation between the base station and user equipments, and cascaded channel estimation assisted by a reconfigurable intelligent surface. The surveyed techniques reflect different trade-offs among estimation accuracy, complexity, and robustness. Overall, this survey aims to provide technical insights and theoretical foundations for efficient and scalable near-field channel estimation in 6G systems, while also highlighting key challenges and promising future research directions.

With its wide coverage and uninterrupted service, satellite communication is a critical technology for next-generation 6G communications. High throughput satellite (HTS) systems, utilizing multipoint beam and frequency multiplexing techniques, enable satellite communication capacity of up to Tbps to meet the growing traffic demand. Therefore, it is imperative to review the-state-of-the-art of multibeam HTS systems and identify their associated challenges and perspectives. Firstly, we summarize the multibeam HTS hardware foundations, including ground station systems, on-board payloads, and user terminals. Subsequently, we review the flexible on-board radio resource allocation approaches of bandwidth, power, time slot, and joint allocation schemes of HTS systems to optimize resource utilization and cater to non-uniform service demand. Additionally, we survey multibeam precoding methods for the HTS system to achieve full-frequency reuse and interference cancellation, which are classified according to different deployments such as single gateway precoding, multiple gateway precoding, on-board precoding, and hybrid on-board/on-ground precoding. Finally, we discuss the challenges related to Q/V band link outage, time and frequency synchronization of gateways, the accuracy of channel state information (CSI), payload light-weight development, and the application of deep learning (DL). Research on these topics will contribute to enhancing the performance of HTS systems and finally delivering high-speed data to areas underserved by terrestrial networks.

Unlike traditional target tracking tasks, UAV tracking scenarios in consumer electronics applications present complex challenges such as background interference, target occlusion, and aerial perspective change, which directly affect tracking performance. To tackle these challenges, we propose FCADtrack, a Spatial Context Awareness-Based Adaptive Real-Time UAV Tracking method for consumer electronics. FCADtrack focuses on fine-tuning to meet UAV-specific requirements and introduces a spatial information interaction mechanism to enhance tracking robustness. First, it incorporates an Adapter Fine-Tuning Module into the ViT backbone, trained on UAV-specific datasets, enabling the model to capture distinctive UAV tracking features and improve adaptability in complex scenarios. Additionally, the Feature Enhanced Contextual Attention (FECA) module is introduced, comprising the Local Spatial Feature Enhanced Module (FEM) and the Global Spatial-Channel Attention Module (SCAM). The FEM extracts rich target features and suppresses background interference through diverse convolutional operations, while the SCAM enhances global feature representation by integrating spatial and channel information using Global Average Pooling (GAP) and Global Max Pooling (GMP). These components significantly boost target feature representation and reduce background interference. The proposed FCADtrack has achieved competitive tracking performance compared with existing methods. Furthermore, it runs a real-time processing speed of 30.3 FPS on the Jetson Orin NX16 platform, demonstrating its potential for real-time consumer electronics applications. Code is released at: https://github.com/qin490/FCADtracker.

Integrated sensing and communication (ISAC) is a promising paradigm for future wireless systems due to spectrum reuse, hardware sharing, and joint waveform design. In dynamic scenes, Doppler shifts degrade both sensing and communication, which is particularly critical for beam-sensitive orbital angular momentum (OAM) wavefronts. To address this, we propose a Doppler-robust ISAC framework, which first senses and then communicates. Specifically, in the sensing phase, multiple vortex modes are simultaneously transmitted via code-division mode-multiplexing (CDMM). To solve Doppler-induced inter-mode interference, we propose a velocity-consistency matching (VCM)-expectation maximization (EM) algorithm that jointly decodes the sensing matrix and estimates range, azimuth, elevation, and velocity for multiple moving targets. In the communication phase, the joint transmitter (Tx) beamforming and receiver (Rx) beam steering are configured from the estimated channel state information (CSI). We further quantify the sensing-communication allocation trade-off by evaluating how pilot length affects estimation accuracy, beam alignment, and spectral efficiency (SE). Simulation results show that the proposed VCM-EM and ISAC designs achieve higher sensing accuracy and communication SE than baseline schemes in dynamic scenarios.

Millimeter wave (mmWave) radar has become a widely adopted technology in vehicles and advanced driver assistance systems (ADAS). Meanwhile, as wireless communication progresses to higher frequencies, the role of the mmWave band has expanded, now supporting both sensing and 5 G communication, which has given rise to integrated sensing and communication (ISAC). In this paper, we propose a novel mmWave ISAC vehicle localization system, which innovatively integrates emerging mmWave identification (MMID) technology into conventional automotive mmWave radar, enabling automotive radar to interact with roadside MMID tags, thereby achieving lane-level vehicle localization independent of the global positioning system (GPS). Unlike existing RFID-based vehicle localization solutions, the proposed solution is more practical for real-world deployment, as it eliminates the need to install additional large RFID antennas on vehicles. To achieve this, we first analyze the backscatter modulation characteristics of MMID tags and propose a novel frequency modulation strategy that lays the foundation for distinguishing signals from different tags within radar echoes that contain various tags and other objects. Subsequently, based on the distance, relative velocity, and azimuth of the tags, we perform static parameter estimation of the vehicle using the least squares (LS) algorithm. Finally, we construct a vehicle motion model and introduce a novel mutation particle filter (MPF) algorithm to estimate the dynamic motion state of the vehicle, ultimately achieving precise vehicle position tracking. The proposed system offers a practical solution for GPS-denied vehicle localization, aligning with the future vision of 6G-enabled intelligent transportation systems (ITS) and IoT-driven smart cities.