communication channel modeling

This work addresses the limitations of conventional channel knowledge graphs (CKGs), which capture only static environments and thus struggle to model time-varying channels induced by dynamic scatterers, terminal orientation changes, and radio-frequency impairments—leading to prohibitively high overhead in acquiring high-dimensional channel state information. To overcome this, the paper proposes a Dynamic Channel Knowledge Graph (Dynamic CKG), establishing for the first time a systematic theoretical framework that serves as an intermediate representation layer bridging static environmental priors and physical-layer signal processing. This framework enables joint pilot design, interference mitigation, and integrated sensing and communication. By integrating geospatial data, time-varying channel modeling, and machine learning–driven graph construction, the approach achieves co-design of CKG and signal processing, significantly reducing channel acquisition overhead while enhancing both communication efficiency and sensing performance, thereby offering a novel paradigm for 6G systems.

Large-scale channel modeling for the 7–15 GHz cmWave (FR3) band remains inadequate in urban (macro/microcell) and suburban scenarios, limiting accurate network planning and system simulation. Method: This study employs measurement-driven statistical modeling to systematically extract and analyze path loss, large-scale fading, and angular-domain statistics (AOA, AOD, DS). Contribution/Results: It reveals distinct propagation mechanisms: high obstacle density in urban areas causes strong path loss and large delay spread, whereas suburban environments—though sparsely obstructed—exhibit significant channel fluctuations due to large-volume scatterers. An enhanced scenario-aware channel prediction model is proposed, substantially improving accuracy in path loss and delay spread estimation. The resulting unified urban/suburban channel characterization model provides reusable, empirically grounded parameters critical for FR3 network planning, link adaptation, and end-to-end system simulation.

Existing UAV channel models neglect the spatial correlation of ground user mobility trajectories, leading to inaccurate characterization of dynamic LOS/NLOS transitions in urban environments and thus distorted modeling of path loss and shadow fading. To address this, we propose a spatially consistent air-to-ground channel model that jointly leverages azimuth and elevation angles to partition LOS/NLOS probabilities—enabling coupled deterministic path loss and stochastic shadow fading modeling. Crucially, it reproduces spatially correlated LOS/NLOS transitions without requiring full 3D environmental reconstruction—a first in the literature. The method integrates geometry-driven simulation, probabilistic modeling, and spatially consistent parameterization. Evaluated across diverse urban scenarios, the model significantly improves fidelity in path loss and shadow fading representation, thereby enabling high-accuracy link outage probability analysis.

This work characterizes the capacity region of the noiseless X channel under intermittent connectivity and delayed channel state information (CSIT), where each transmitter sends one common message (intended for both receivers) and two private messages (each intended for one receiver). Methodologically, we derive a novel outer bound that jointly incorporates interference alignment constraints and entropy limits on the common message; we further propose a dynamic channel-mode-adaptive achievability scheme that decomposes the X channel in parallel into interference-channel and broadcast-channel subproblems, leveraging hierarchical coding and multi-strategy interleaving. Our contributions include: (i) the first complete characterization of the exact capacity region for the homogeneous channel setting; and (ii) the extension of the outer bound to the heterogeneous case, accompanied by a matching achievability proof—thereby establishing theoretical optimality across both settings.

Existing vertical-dimension channel models for unmanned aerial vehicle (UAV) air-to-ground communications suffer from insufficient accuracy, particularly in characterizing height-dependent propagation effects. Method: This study conducts extensive field measurements at 1 GHz and 4 GHz across line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, systematically quantifying large-scale path loss, shadow fading (modeled via log-normal distribution), and small-scale fading (validated against Rayleigh and Rician distributions). Contribution/Results: We propose, for the first time, a height-dependent path loss model that explicitly incorporates UAV flight altitude as a key parameter, and jointly characterize vertical-direction propagation specificity and fading statistics. The resulting high-fidelity air-to-ground channel model significantly improves link budget prediction accuracy and coverage performance assessment reliability. It provides a reproducible, scalable empirical foundation and modeling paradigm for low-altitude communication network design and optimization.

This work addresses the lack of theoretical understanding regarding the degradation of rate-splitting multiple access (RSMA) to space-division multiple access (SDMA) in the presence of transceiver hardware impairments and imperfect successive interference cancellation (SIC). By constructing a unified system model that jointly accounts for hardware distortions and residual SIC interference, the study rigorously proves—using optimization theory—that as the residual interference coefficient approaches unity, the optimal beamformer for the common stream in RSMA converges to zero. Consequently, the RSMA transmission structure naturally collapses into SDMA. This result provides the first optimality-based explanation for the empirically observed performance convergence between RSMA and SDMA under severe SIC failure, offering crucial theoretical guidance for selecting appropriate multiple access schemes in SIC-constrained systems.

This study addresses the lack of accurate channel modeling for 3.4 GHz air-to-air (A2A) communications, which has hindered the design of unmanned aerial vehicle (UAV) communication systems. Leveraging an open-source, reconfigurable channel sounding platform built with USRP B210 and a GNSS-disciplined oscillator, the authors conducted spherical-trajectory flight experiments at the AERPAW Lake Wheeler testbed to systematically collect A2A channel measurements across varying altitudes, elevation angles, and relative headings. This work presents the first comprehensive characterization of sub-6 GHz A2A channel properties at 3.4 GHz and introduces a geometry-aware fading model that explicitly incorporates real flight trajectories. The study quantifies the relationship between RMS delay spread and link geometry and publicly releases both the lightweight sounding platform and the measured dataset, providing a reliable foundation for simulation, protocol design, and performance evaluation of UAV communication systems.

This work addresses the fundamental trade-off between sensing and communication performance in integrated sensing and communication (ISAC) systems under non-coherent conditions where channel state information is unavailable at both transmitter and receiver. By jointly designing sensing-aware beamforming and communication modulation through optimized spatial power allocation, the study conducts asymptotic performance analysis across high and low signal-to-noise ratio (SNR) regimes. Leveraging a lower bound on non-coherent mutual information, a sensing-induced rate loss metric, and a projected gradient algorithm, the paper quantifies the communication rate penalty due to sensing at high SNR and optimizes beamforming accordingly. Notably, it proves that at low SNR, sensing and communication objectives can be perfectly aligned, achieving zero first-order communication performance loss—thereby revealing a fundamental dichotomy and synergistic potential between the two functions across different SNR regimes.

This work addresses the scarcity of MIMO channel measurement data under extreme weather conditions, which hinders reliable coverage assessment for 5G/6G networks. To overcome this limitation, the authors propose a conditional diffusion model that, for the first time, incorporates both weather type and intensity as conditioning inputs. Leveraging only pilot-based channel state information (CSI) estimates collected under mild weather, the model generates realistic MIMO channels across three distinct weather types and multiple intensity levels. The synthesized channels demonstrate strong performance in key metrics such as downlink bit error rate and outage probability, confirming the model’s generalization capability and scalability in harsh environments. This approach offers an effective solution for channel modeling in scenarios where empirical measurements under adverse weather are unavailable.

This work addresses the challenge of efficiently constructing and dynamically updating context-aware, location-dependent channel gain maps (CGMs) in large-scale wireless networks. It proposes a physics-informed approach based on 3D Gaussian Splatting (3DGS), introducing 3DGS for the first time into CGM modeling. The method represents the propagation environment using Gaussian primitives and integrates physical radio propagation mechanisms—including path loss, transmission, and scattering—to generate grid-level channel gains via differentiable rendering. To accommodate dynamic environmental changes, an incremental learning strategy is devised, combining frozen reference primitives with adaptable incremental ones. Experimental results demonstrate that the proposed method achieves high accuracy while significantly reducing computational overhead, enabling real-time updates and fine-grained representation of CGMs.