ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias

This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations. ReFLEX is a length-generalizable Transformer whose frequency attention uses a relative-frequency position bias (RFPB) generated from subcarrier offsets. A single checkpoint handles unseen RB lengths and can be applied to sparse DM-RS observations in the tested RB5/RB10 PUSCH setup without retraining. In a 3GPP~TR~38.901 UMa NLOS channel, ReFLEX achieves about $-9.6$~dB NMSE on unseen RB lengths. In NR PUSCH/UL-SCH simulations, ReFLEX denoising followed by time-frequency interpolation reduces the 10\% BLER threshold by about 2--3~dB.

Tri-Domain Multiuser MIMO Precoding Optimization and Channel Estimation with Spatial-EM Reconfigurable Antenna

In this paper, we propose a tri-domain reconfigurable multiuser multiple-input multiple-output (MIMO) communication system that integrates the electromagnetic (EM) reconfigurable antenna (EMRA) with the spatially movable antenna (SMA), termed the spatial-EM reconfigurable antenna (SEMRA). The proposed system offers EM, spatial, and digital domain degrees of freedom (DoFs) for joint channel reconfiguration, yet introduces new challenges in channel estimation (CE) and precoding optimization. Specifically, for multiuser orthogonal frequency division multiplexing (OFDM) downlink, the precoding design is formulated as a tri-domain optimization problem over antenna positions, EM-domain radiation-pattern weights, and digital precoders. We first develop a zero-forcing (ZF)-based baseline algorithm to decouple the design of spatial reconfiguration, and then propose a weighted minimum mean square error (WMMSE)-based tri-domain joint optimization algorithm for further improving the spectral efficiency (SE). Furthermore, we propose a low-overhead movement-aided channel estimation scheme in which coordinated antenna repositioning across pilot slots synthesizes a denser virtual array, enabling more accurate angle-of-departure (AoD) estimation and EM-domain channel state information (eCSI) reconstruction under the same per-user pilot overhead as the EMRA baseline. The resulting parametric representation enables eCSI assembly at desired antenna positions without additional pilots. Simulation results show that the proposed CE scheme improves eCSI estimation accuracy and the proposed SEMRA achieves higher SE than the EMRA baseline under the same pilot overhead.

DJSCC-Enabled Multi-User Semantic CSI Feedback for Hybrid Beamforming in Dual-Polarized cmWave Massive MIMO

Driven by the ultra-high throughput requirements of 6G, wireless communications are migrating to centimeter wave (cmWave) bands to overcome the limitations of current spectral resources. Massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) systems aim to achieve high spectral efficiency in cmWave regimes but are often constrained by the heavy overhead of downlink channel state information (CSI) feedback. This paper proposes a deep learning scheme based on the multi-axis multi-layer perceptron for image processing (MAXIM) architecture for joint semantic CSI feedback and hybrid beamforming in multi-user cmWave MIMO-OFDM systems, which maximizes the downlink sum rate by end-to-end optimization. Specifically, distributed encoders at multiple user equipments (UEs) perform limited CSI feedback, while the decoder at the base station (BS) jointly designs the hybrid beamforming matrices without explicit CSI reconstruction. The uplink transmission is implemented via deep joint source-channel coding (DJSCC) to enhance CSI compression efficiency and noise robustness. Furthermore, considering the high correlation between vertical and horizontal polarization channels in dual-polarized massive MIMO systems, a cross-polarization interaction module is introduced at the UEs to exploit polarization correlations for joint CSI compression. Simulation results demonstrate that the proposed method improves the downlink sum rate under various signal-to-noise ratio (SNR) conditions with a limited number of feedback symbols, validating its robustness and superiority in multi-user dual-polarized cmWave MIMO-OFDM systems.

Transformer-based Hybrid Beamforming with Dynamic Subarray for Near-Space Airship-Borne Communications

This paper proposes a hybrid beamforming framework for massive multiple-input multiple-output (MIMO) in near-space airship-borne communications. To achieve high energy efficiency (EE) in energy-constraint airships, a dynamic subarray structure is introduced, where each radio frequency chain (RFC) is connected to a disjoint subset of the antennas according to channel state information (CSI). The proposed joint dynamic hybrid beamforming network (DyHBFNet) comprises three key components: 1) An analog beamforming network (ABFNet) that optimizes the analog beamforming matrices and provides auxiliary information for the antenna selection network (ASNet) design, 2) an ASNet that dynamically optimizes the connections between antennas and RFCs, and 3) a digital beamforming network (DBFNet) that optimizes digital beamforming matrices by employing a model-driven weighted minimum mean square error algorithm for improving beamforming performance and convergence speed. The proposed ABFNet, ASNet, and DBFNet are all designed based on advanced Transformer encoders. Simulation results demonstrate that the proposed framework significantly enhances spectral efficiency and EE compared to baseline schemes. Additionally, its robust performance under imperfect CSI makes it a scalable solution for practical implementations.

Movable Antenna for Integrating Near-field Channel Estimation and Localization

Movable antenna (MA) introduces a new degree of freedom for future wireless communication systems by enabling the adaptive adjustment of antenna positions. Its large-range movement renders wireless channels transmission into the near-field region, which brings new performance enhancement for integrated sensing and communication (ISAC). This paper proposes a novel multi-stage design framework for broadband near-field ISAC assisted by MA. The framework first divides the MA movement area into multiple subregions, and employs the Newtonized orthogonal matching pursuit algorithm (NOMP) to achieve high-precision angle estimation in each subregion. Subsequently, a method called near-field localization via subregion ray clustering (LSRC) is proposed for identifying the positions of scatterers. This method finds the coordinates of each scatterer by jointly processing the angle estimates across all subregions. Finally, according to the estimated locations of the scatterers, the near-field channel estimation (CE) is refined for improving communication performance. Simulation results demonstrate that the proposed scheme can significantly enhance MA sensing accuracy and CE, providing an efficient solution for MA-aided near-field ISAC.

Ultra-Massive MIMO with Orthogonal Chirp Division Multiplexing for Near-Field Sensing and Communication Integration

This paper integrates the emerging ultra-massive multiple-input multiple-output (UM-MIMO) technique with orthogonal chirp division multiplexing (OCDM) waveform to tackle the challenging near-field integrated sensing and communication (ISAC) problem. Specifically, we conceive a comprehensive ISAC architecture, where an UM-MIMO base station adopts OCDM waveform for communications and a co-located sensing receiver adopts the frequency-modulated continuous wave (FMCW) detection principle to simplify the associated hardware. For sensing tasks, several OCDM subcarriers, namely, dedicated sensing subcarriers (DSSs), are each transmitted through a dedicated sensing antenna (DSA) within the transmit antenna array. By judiciously designing the DSS selection scheme and optimizing receiver parameters, the FMCW-based sensing receiver can decouple the echo signals from different DSAs with significantly reduced hardware complexity. This setup enables the estimation of ranges and velocities of near-field targets in an antenna-pairwise manner. Moreover, by leveraging the spatial diversity of UM-MIMO, we introduce the concept of virtual bistatic sensing (VIBS), which incorporates the estimates from multiple antenna pairs to achieve high-accuracy target positioning and three-dimensional velocity measurement. The VIBS paradigm is immune to hostile channel environments characterized by spatial non-stationarity and uncorrelated multipath environment. Furthermore, the channel estimation of UM-MIMO OCDM systems enhanced by the sensing results is investigated. Simulation results demonstrate that the proposed ISAC scheme enhances sensing accuracy, and also benefits communication performance.

AI Signal Processing Paradigm for Movable Antenna: From Geometric Optimization to Electromagnetic Reconfigurability

As 6G wireless communication systems evolve toward intelligence and high reconfigurability, the limitations of traditional fixed antenna (TFA) has become increasingly prominent, with geometrically movable antenna (GMA) and electromagnetically reconfigurable antenna (ERA) emerging as key technologies to break through this bottleneck. GMA activates spatial degrees of freedom (DoF) by dynamically adjusting antenna positions, ERA regulates radiation characteristics using tunable metamaterials, thereby introducing DoF in the electromagnetic domain. However, the ``geometric-electromagnetic dual reconfiguration" paradigm formed by their integration poses severe challenges of high-dimensional hybrid optimization to signal processing. To address this issue, we integrate the geometric optimization of GMA and the electromagnetic reconfiguration of ERA for the first time, propose a unified modeling framework for movable and reconfigurable antenna (MARA), investigate the channel modeling and spectral efficiency (SE) optimization for GMA, ERA, and MARA. Besides, we systematically review artificial intelligence (AI)-based solutions, focusing on analyzing the advantages of AI over traditional algorithms in high-dimensional non-convex optimization computations. This paper fills the gap in existing literature regarding the lack of a comprehensive review on the AI-driven signal processing paradigm under geometric-electromagnetic dual reconfiguration and provides theoretical support for the design and optimization of 6G wireless systems with high SE and flexibility.

Toward Near-Space Communication Network in the 6G and Beyond Era

Near-space communication network (NS-ComNet), as an indispensable component of sixth-generation (6G) and beyond mobile communication systems and the space-air-ground-sea integrated network (SAGSIN), demonstrates unique advantages in wide-area coverage, long-endurance high-altitude operation, and highly flexible deployment. This paper presents a comprehensive review of NS-ComNet for 6G and beyond era. Specifically, by contrasting satellite, low-altitude unmanned-aerial-vehicle (UAV), and terrestrial communications, we first elucidate the background and motivation for integrating NS-ComNet into 6G network architectures. Subsequently, we review the developmental status of near-space platforms, including high-altitude balloons, solar-powered UAVs, and stratospheric airships, and analyze critical challenges faced by NS-ComNet. To address these challenges, the research focuses on key enabling technologies such as topology design, resource and handover management, multi-objective joint optimization, etc., with particular emphasis on artificial intelligence techniques for NS-ComNet. Finally, envisioning future intelligent collaborative networks that integrate NS-ComNet with satellite-UAV-terrestrial systems, we explore promising directions. This paper aims to provide technical insights and research foundations for the systematic construction of NS-ComNet and its deep deployment in the 6G and beyond era.

MmWave-LoRadar Empowered Vehicular Integrated Sensing and Communication Systems: LoRa Meets FMCW

The integrated sensing and communication (ISAC) technique is regarded as a key component in future vehicular applications. In this paper, we propose an ISAC solution that integrates Long Range (LoRa) modulation with frequency-modulated continuous wave (FMCW) radar in the millimeter-wave (mmWave) band, called mmWave-LoRadar. This design introduces the sensing capabilities to the LoRa communication with a simplified hardware architecture. Particularly, we uncover the dual discontinuity issues in time and phase of the mmWave-LoRadar received signals, rendering conventional signal processing techniques ineffective. As a remedy, we propose a corresponding hardware design and signal processing schemes under the compressed sampling framework. These techniques effectively cope with the dual discontinuity issues and mitigate the demands for high-sampling-rate analog-to-digital converters while achieving good performance. Simulation results demonstrate the superiority of the mmWave-LoRadar ISAC system in vehicular communication and sensing networks.

Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency.