arXiv subjects

Jianhua Mo

Publications and source records attributed to Jianhua Mo.

At least 19 recordsLinked to original sources

arXiv2026

AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G

The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical-layer design. However, existing models often operate on channel state information (CSI) in the spatial-temporal-frequency (STF) domain, where multipath components are superimposed and structurally entangled. This hinders the learning of a universal channel representation. Their reliance on global attention also incurs prohibitive overhead. In this paper, we propose AirFM-DDA, an Air-interface Foundation Model in the Delay-Doppler-Angle (DDA) domain. AirFM-DDA reparameterizes CSI into the DDA domain to resolve multipath components along physically meaningful axes and employs window-based attention with frame-structure-aware positional encoding. Extensive experiments demonstrate transferability across scenarios, tasks, datasets, and antenna configurations. For channel prediction and estimation, AirFM-DDA generalizes zero-shot to unseen cities, achieving average normalized mean-square error (NMSE) gains of 4.9-8.5 dB over the strongest baselines. With only 10% labeled data, it achieves average gains of 12.0 percentage points in Top-1 accuracy for beam prediction and 3.4 percentage points in F1 score for line-of-sight (LoS) identification. It further transfers across simulated datasets and adapts to measured data and different antenna arrays. Compared with global attention, window-based attention reduces training and inference costs by nearly an order of magnitude.

cs.LG↗

arXiv2026

Low-Altitude Channel Multipath Prediction via Panoramic Perception and Vision-Language Model

Unmanned aerial vehicle (UAV) communication is expected to support a wide range of low-altitude applications in 6G mobile networks. However, traditional statistical channel models provide limited accuracy in specific environments, while deterministic methods such as ray tracing usually rely on accurate three-dimensional environment models and involve high computational complexity. Existing multimodal channel prediction approaches mainly focus on large-scale metrics such as path loss, and remain insufficient for modeling small-scale parameters. To address these limitations, this paper proposes PanoLAMP, a Panoramic perception and vision-language model-based Low-Altitude Multipath Prediction framework. It adopts a pretrained vision-language model as the backbone and captures the propagation environment features through panoramic RGB-D observations collected at both the transmitter and receiver to predict the delay, power, azimuth angle, and zenith angle offset relative to the line-of-sight path. Experiments are conducted on a synthetic dataset containing 18,949 UAV-vehicle links across seven UAV altitudes. Experimental results show that the proposed method consistently outperforms representative baselines in both multipath parameters and statistical metrics, and demonstrates stronger generalization across different flight heights.

cs.CV↗

arXiv2026

M3F-UAV: A Missing-Modality Multimodal Foundation Model for Low-Altitude Wireless Sensing

Low-altitude unmanned aerial vehicles (UAVs) are emerging as key platforms for wireless intelligence tasks. However, practical low-altitude wireless systems usually operate in complex urban environments, where visual occlusion, sparse geometric observations, multipath propagation, and sensor failures may degrade the reliability of single-modality models. To address these challenges, this paper proposes M3F-UAV, a missing-modality multimodal foundation model for low-altitude wireless sensing. The proposed framework learns a unified multimodal representation from visual, geometric, and wireless observations. Specifically, modality-specific pretrained feature extractors are adopted for RGB/depth images, LiDAR point clouds, and CSI matrices, respectively. Through cross-modal fusion and missing-modality-aware pretraining with feature-level masked reconstruction and UAV localization objectives, M3F-UAV can extract fixed-size features from different modality combinations and adapt them to downstream low-altitude wireless tasks with lightweight task heads. Experiments on the LAMBDA dataset show that M3F-UAV outperforms single-modality baselines and maintains robust performance under missing-modality settings.

eess.SP↗

arXiv2026

LAMBDA: A Low-Altitude Multimodal Base Dataset for UAV Sensing and Communication

Research on low-altitude integrated sensing and communication (ISAC) requires aligned multimodal data that jointly describe wireless propagation, visual appearance, unmanned aerial vehicle (UAV) motion, light detection and ranging (LiDAR) perception, and radar sensing under common trajectories and timestamps. To address this need, a low-altitude multimodal base dataset, named LAMBDA, is introduced. LAMBDA is characterized by high fidelity, modality diversity, scenario richness, and configuration flexibility. It is generated through a high-fidelity digital-twin pipeline with detailed scene geometry, refined material assignment, and electromagnetic modeling of UAVs. LAMBDA provides synchronized RGB images, depth maps, LiDAR point clouds, inertial measurement unit states, UAV poses, channel state information (CSI), and radar-synthesis resources across matched low-altitude operating conditions, shared coordinate systems, and synchronized frame indices. The dataset covers urban, suburban, and campus scenes, multi-UAV/multi-base-station settings, nighttime conditions, and sunny, rainy, snowy, and foggy weather variations. Its CSI and radar resources support user-defined antenna-array sizes, bandwidths, subcarrier spacings, chirp parameters, and plane-wave or spherical-wavefront channel synthesis. The reliability and usability of LAMBDA are assessed through quality control, weather and multimodal visualization, and two UAV ISAC-related use cases: RGB-aided beam prediction and RGB-LiDAR-based UAV localization.

eess.SP↗

arXiv2026

BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping

Most existing semantic communication systems based on joint source-channel coding (JSCC) employ analog modulation and are thus inherently incompatible with modern digital communication systems and impose stringent hardware design challenges. Although several digital transmission approaches have been proposed to address this issue, they often suffer from high sensitivity to bit errors, limited adaptability to varying source distributions, or re-training overhead under different modulation schemes. This letter proposes BitSemCom, a novel end-to-end bit-level JSCC framework that is robust to channel noise and modulation-agnostic. The core component is a learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bit sequences. By leveraging a sampling-based bit generation method based on the Gumbel-Softmax trick, the framework enables differentiable bit-level optimization while maintaining robustness to channel errors. Simulation results on image transmission demonstrate that BitSemCom achieves consistent peak signal-to-noise ratio (PSNR) gains of 2-3 dB over codebook-based digital semantic transmission methods and competitive performance with stronger robustness compared to separate source-channel coding (SSCC) benchmarks. Ablation studies further validate the effectiveness of the learnable bit mapper.

eess.IV↗

arXiv2026

Phase-Time Array Enabled Multistatic Sensing with Multi-Level Fusion for UAV Localization

Multistatic collaborative sensing eliminates self-interference, achieves spatial diversity gains, and enables wide-range seamless integrated sensing and communication (ISAC). However, conventional data fusion methods suffer from severe error amplification in geometry-sensitive regions. In addition, the conventional analog phased array solution introduces large beam sweeping overhead, whereas the fully digital arrays request high hardware cost. We propose a multistatic sensing framework enabled by a phase-time array (PTA). The rainbow beamforming maps spatial directions to orthogonal frequency division multiplexing (OFDM) subcarriers, achieving wide-angle coverage with a single radio frequency (RF) chain. We develop two parameter-level schemes-a geometry-aware analytical estimator (GDOP-WLS) and a lightweight multilayer perceptron (PF-MLP)-to mitigate the effects of topological singularities. Additionally, an end-to-end signal-level convolutional neural network (SF-CNN) directly estimates target coordinates from raw signals, avoiding cascaded estimation errors. The results demonstrate that the parameter-level schemes ensure robust convergence under adverse geometric conditions with minimal computational latency. Conversely, the signal-level scheme achieves sub-meter precision but requires an increased computational load. Consequently, the proposed framework establishes a scalable solution for collaborative surveillance of unmanned aerial vehicles (UAVs), providing flexible trade-offs among hardware complexity, latency, and accuracy.

eess.SP↗

arXiv2026

SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming

Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This paper proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating the PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error.

cs.LG↗

arXiv2026

MARBLE-Net: Learning to Localize in Multipath Environment with Adaptive Rainbow Beams

Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a frequency-dependent rainbow beam and a neural localization network for high-accuracy position estimation. By treating the phase-shifter (PS) and true-time-delay (TTD) parameters as learnable weights, the system adaptively refines its sensing beam to exploit environment-specific multipath characteristics. A structured multi-stage training strategy is proposed to ensure stable convergence and effective end-to-end optimization. Simulation results show that MARBLE-Net outperforms both a fixed-beam deep learning baseline (RaiNet) and a traditional k-nearest neighbors (k-NN) method, reducing localization error by more than 50\% in a multipath-rich scene. Moreover, the results reveal a nuanced interaction with multipath propagation: while confined uni-directional multipath degrades accuracy, structured and directional multipath can be effectively exploited to achieve performance surpassing even line-of-sight (LoS) conditions.

eess.SP↗

arXiv2026

CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar

Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by phase-time arrays (PTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However, accurate detection of multiple closely spaced targets remains a key challenge for conventional signal processing pipelines, particularly those relying on constant false alarm rate (CFAR) detectors. This paper presents CFARNet, a learning-based processing framework that replaces CFAR with a convolutional neural network (CNN) for peak detection in the angle-Doppler domain. The network predicts target subcarrier indices, which guide angle estimation via a known frequency-angle mapping and enable high-resolution range and velocity estimation using the MUSIC algorithm. Extensive simulations demonstrate that CFARNet significantly outperforms a baseline combining CFAR and MUSIC, especially under low transmit power and dense multi-target conditions. The proposed method offers superior angular resolution, enhanced robustness in low-SNR scenarios, and improved computational efficiency, highlighting the potential of data-driven approaches for high-resolution mmWave radar sensing.

eess.SP↗

arXiv2025

Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder

This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIMO systems. To this end, we propose Fed-PELAD, a novel federated learning framework that incorporates personalized encoders and a LoRA-adapted shared decoder. Specifically, personalized encoders are trained locally on each user equipment (UE) to capture device-specific channel characteristics, while a shared decoder is updated globally via the coordination of the base station (BS) by using Low-Rank Adaptation (LoRA). This design ensures that only compact LoRA adapter parameters instead of full model updates are transmitted for aggregation. To further enhance convergence stability, we introduce an alternating freezing strategy with calibrated learning-rate ratio during LoRA aggregation. Extensive simulations on 3GPP-standard channel models demonstrate that Fed-PELAD requires only 42.97\% of the uplink communication cost compared to conventional methods while achieving a performance gain of 1.2 dB in CSI feedback accuracy under heterogeneous conditions.

cs.IT↗

arXiv2025

Deep Learning-Based Beamforming Design Using Target Beam Patterns

This paper proposes a deep learning-based beamforming design framework that directly maps a target beam pattern to optimal beamforming vectors across multiple antenna array architectures, including digital, analog, and hybrid beamforming. The proposed method employs a lightweight encoder-decoder network where the encoder compresses the complex beam pattern into a low-dimensional feature vector and the decoder reconstructs the beamforming vector while satisfying hardware constraints. To address training challenges under diverse and limited channel station information (CSI) conditions, a two-stage training process is introduced, which consists of an offline pre-training for robust feature extraction using an auxiliary module, followed by online training of the decoder with a composite loss function that ensures alignment between the synthesized and target beam patterns in terms of the main lobe shape and side lobe suppression. Simulation results based on NYUSIM-generated channels show that the proposed method can achieve spectral efficiency close to that of fully digital beamforming under limited CSI and outperforms representative existing methods.

eess.SP↗

arXiv2025

SemCSINet: A Semantic-Aware CSI Feedback Network in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) technology is a key enabler of modern wireless communication systems, which demand accurate downlink channel state information (CSI) for optimal performance. Although deep learning (DL) has shown great potential in improving CSI feedback, most existing approaches fail to exploit the semantic relationship between CSI and other related channel metrics. In this paper, we propose SemCSINet, a semantic-aware Transformer-based framework that incorporates Channel Quality Indicator (CQI) into the CSI feedback process. By embedding CQI information and leveraging a joint coding-modulation (JCM) scheme, SemCSINet enables efficient, digital-friendly CSI feedback under noisy feedback channels. Experimental results on DeepMIMO datasets show that SemCSINet significantly outperforms conventional methods, particularly in scenarios with low signal-to-noise ratio (SNR) and low compression ratios (CRs), highlighting the effectiveness of semantic embedding in enhancing CSI reconstruction accuracy and system robustness.

eess.SP↗

arXiv2025

Joint Phase Time Array: Opportunities, Challenges and System Design Considerations

This paper presents a novel approach to designing millimeter-wave (mmWave) cellular communication systems, based on joint phase time array (JPTA) radio frequency (RF) frontend architecture. JPTA architecture comprises time-delay components appended to conventional phase shifters, which offer extra degrees of freedom to be exploited for designing frequency-selective analog beams. Hence, a mmWave device equipped with JPTA can receive and transmit signals in multiple directions in a single time slot per RF chain, one direction per frequency subband, which alleviates the traditional constraint of one analog beam per transceiver chain per time slot. The utilization of subband-specific analog beams offers a new opportunity in designing mmWave systems, allowing for enhanced cell capacity and reduced pilot overhead. To understand the practical feasibility of JPTA, a few challenges and system design considerations are discussed in relation to the performance and complexity of the JPTA systems. For example, frequency-selective beam gain losses are present for the subband analog beams, e.g., up to 1 dB losses for 2 subband cases, even with the state-of-the-art JPTA delay and phase optimization methods. Despite these side effects, system-level analysis reveals that the JPTA system is capable of improving cell capacity: 5-percentile cell throughput by up to 65 per cent. To the best of the author's knowledge, this paper is the first paper explaining the system-level benefits and system-design challenges of JPTA, with an analysis of the performance tradeoff based on an intuitive metric of beam gain losses.

eess.SP↗

arXiv2025

Beamforming with Joint Phase and Time Array: System Design, Prototyping and Performance

Joint phase-time arrays (JPTA) is a new mmWave radio frequency front-end architecture constructed with appending time-delay elements to phase shifters for analog beamforming. JPTA allows the mmWave base station (BS) to form multiple frequency-dependent beams with a single RF chain, exploiting the extra degrees of freedom the time-delay elements offer. Without requiring extra power-hungry RF chains, a BS with JPTA can schedule multiple users in different directions in a frequency-division multiplexing (FDM) manner. A BS with JPTA achieves various advantages over the traditional analog beamforming system. Simulation results show that JPTA can bring significant system-level benefits, e.g., extending uplink throughput coverage by 100%. To realize these system benefits of JPTA, high-resolution delay elements with a wide delay dynamic range are essential. With newly developed delay elements, we demonstrate that a single TRX RF chain can serve four users in four different directions in the mmWave band.

cs.IT↗

arXiv2025

Hybrid Near/Far-Field Frequency-Dependent Beamforming via Joint Phase-Time Arrays

Joint phase-time arrays (JPTA) emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications by utilizing both true-time delay units and phase shifters. This paper exploits the potential of JPTA to simultaneously serve multiple users in both near- and far-field regions with a single radio frequency chain. The goal is to jointly optimize JPTA-based beamforming and subband allocation to maximize overall system performance. To this end, we formulate a system utility maximization problem, including sum-rate maximization and proportional fairness as special cases. We develop a 3-step alternating optimization (AO) algorithm and an efficient deep learning (DL) method for this problem. The DL approach includes a 2-layer convolutional neural network, a 3-layer graph attention network (GAT), and a normalization module for resource and beamforming optimization. The GAT efficiently captures the interactions between resource allocation and analog beamformers. Simulation results confirm that JPTA outperforms conventional phased arrays (PA) in enhancing user rate and strikes a good balance between PA and fully-digital approach in energy efficiency. Employing a logarithmic utility function for user rates ensures greater fairness than maximizing sum-rates. Furthermore, the DL network achieves comparable performance to the AO approach, while having orders of magnitude lower computational complexity.

cs.IT↗

arXiv2024

3D Beamforming Through Joint Phase-Time Arrays

High-frequency wideband cellular communications over mmWave and sub-THz offer the opportunity for high data rates. However, it also presents high path loss, resulting in limited coverage. High-gain beamforming from the antenna array is essential to mitigate the coverage limitations. The conventional phased antenna arrays (PAA) cause high scheduling latency owing to analog beam constraints, i.e., only one frequency-flat beam is generated. Recently introduced joint phase-time array (JPTA) architecture, which utilizes both true-time-delay (TTD) units and phase shifters (PSs), alleviates analog beam constraints by creating multiple frequency-dependent beams for scheduling multiple users at different directions in a frequency-division manner. One class of previous studies offered solutions with ``rainbow" beams, which tend to allocate a small bandwidth per beam direction. Another class focused on uniform linear array (ULA) antenna architecture, whose frequency-dependent beams were designed along a single axis of either azimuth or elevation direction. This paper presents a novel 3D beamforming design that maximizes beamforming gain toward desired azimuth and elevation directions and across sub-bands partitioned according to scheduled users' bandwidth requirements. We provide analytical solutions and iterative algorithms to design the PSs and TTD units for a desired subband beam pattern. Through simulations of the beamforming gain, we observe that our proposed solutions outperform the state-of-the-art solutions reported elsewhere.

cs.IT↗

arXiv2023

Joint Phase-Time Arrays: A Paradigm for Frequency-Dependent Analog Beamforming in 6G

Hybrid beamforming is an attractive solution to build cost-effective and energy-efficient transceivers for millimeter-wave and terahertz systems. However, conventional hybrid beamforming techniques rely on analog components that generate a frequency flat response such as phase-shifters and switches, which limits the flexibility of the achievable beam patterns. As a novel alternative, this paper proposes a new class of hybrid beamforming called Joint phase-time arrays (JPTA), that additionally use true-time delay elements in the analog beamforming to create frequency-dependent analog beams. Using as an example two important frequency-dependent beam behaviors, the numerous benefits of such flexibility are exemplified. Subsequently, the JPTA beamformer design problem to generate any desired beam behavior is formulated and near-optimal algorithms to the problem are proposed. Simulations show that the proposed algorithms can outperform heuristics solutions for JPTA beamformer update. Furthermore, it is shown that JPTA can achieve the two exemplified beam behaviors with one radio-frequency chain, while conventional hybrid beamforming requires the radio-frequency chains to scale with the number of antennas to achieve similar performance. Finally, a wide range of problems to further tap into the potential of JPTA are also listed as future directions.

cs.IT↗

arXiv2022

Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems

Beam management (BM), i.e., the process of finding and maintaining a suitable transmit and receive beam pair, can be challenging, particularly in highly dynamic scenarios. Side-information, e.g., orientation, from on-board sensors can assist the user equipment (UE) BM. In this work, we use the orientation information coming from the inertial measurement unit (IMU) for effective BM. We use a data-driven strategy that fuses the reference signal received power (RSRP) with orientation information using a recurrent neural network (RNN). Simulation results show that the proposed strategy performs much better than the conventional BM and an orientation-assisted BM strategy that utilizes particle filter in another study. Specifically, the proposed data-driven strategy improves the beam-prediction accuracy up to 34% and increases mean RSRP by up to 4.2 dB when the UE orientation changes quickly.

eess.SP↗