standardized channel parameterization (3gpp)

This work addresses the absence of a 3GPP-adopted standardized channel model and corresponding measurement data for the 7–24 GHz frequency band. To bridge this gap, the study systematically conducts millimeter-wave channel measurements and constructs a large-scale, wideband measurement dataset spanning the entire 7–24 GHz range. Channel parameters are extracted and modeled in strict compliance with the 3GPP TR 38.901 framework, followed by rigorous curve fitting. The resulting dataset and modeling methodology constitute the foundational channel model officially adopted by 3GPP Release 19, marking the first public release of such standardized data and procedures for this frequency range. This contribution provides authoritative support for the design of 5G-Advanced and 6G high-frequency communication systems and advances the standardization and unification of millimeter-wave channel modeling.

This work addresses the limitations of the 3GPP-standardized tapped delay line (TDL) channel model in accurately capturing the spatial propagation characteristics of MIMO systems, which can lead to biased performance evaluations. To overcome this, the authors propose and validate a reduced cluster delay line (rCDL) model. Through comparative analysis against real-world channel measurements in representative commercial scenarios, the study evaluates the spatial modeling accuracy of rCDL relative to TDL and further assesses their discriminative capability via CSI reporting performance simulations. Results demonstrate that rCDL significantly improves the fidelity of spatial channel characterization while maintaining reasonable computational complexity. It outperforms TDL in both measurement-to-model alignment and evaluation of CSI feedback schemes, thereby offering strong support for future 3GPP standardization efforts.

To address the demand for real-time, high-fidelity wireless channel modeling in network digital twins, this paper proposes an efficient ray-tracing (RT)-oriented channel generation method to reconcile the high computational cost of RT in 3D environments with stringent real-time requirements. The method comprises: (1) a channel-preserving geometric scene pre-simplification algorithm that reduces ray emission and intersection complexity; and (2) a temporal-consistency-aware post-processing enhancement mechanism for RT outputs, enabling sub-millisecond dynamic channel parameter interpolation and improved temporal resolution. Evaluated across multiple photorealistic 3D scenarios, the approach achieves over 50× overall speedup, with mean errors in key metrics—such as delay spread and angular spread—below 8%. It satisfies real-time constraints (<100 ms per frame) while maintaining modeling fidelity, marking the first demonstration of online, dynamic RT-based channel generation at fine-grained spatial resolution.

The absence of a standardized system-level simulation framework hinders rigorous performance evaluation of Reconfigurable Intelligent Surfaces (RIS) in 6G multi-RIS, multi-base-station networks. Method: This work establishes the first 3GPP-compliant system-level simulator for RIS-aided networks. It integrates geometrically random RIS deployment, configurable panel density and size, and—novelly within a standardized framework—jointly models key non-ideal hardware effects: near-field propagation, inter-panel interference, phase quantization error, and unit failure. These factors are rigorously incorporated into path loss and large-scale fading modeling for both RIS-reflected and direct links. Results: Simulation results demonstrate that strategic RIS deployment significantly enhances Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), spectral efficiency, and cell coverage. The platform provides a reproducible, quantitative performance benchmark and design guidelines to support standardization of RIS technology in 6G systems.

To address weak downstream-task generalization, poor performance under few-shot conditions, and high computational complexity in wireless communication-and-sensing systems, this paper introduces LWM—the first foundational model for wireless channels. Built upon the Transformer architecture, LWM employs self-supervised pretraining on large-scale channel data to learn task-agnostic, context-aware universal channel embeddings. Its core contribution is establishing the foundational model paradigm for wireless channels, enabling data-efficient and transferable channel representation learning. Experiments demonstrate that LWM significantly outperforms raw channel representations across diverse communication and sensing downstream tasks—particularly under limited training data or high model complexity—thereby providing a scalable, reusable representational foundation for intelligent wireless systems.

This study addresses a critical gap in communication-aware robotic planning, where existing approaches commonly rely on channel-level metrics to predict end-to-end 5G throughput—a practice lacking empirical validation in private 5G deployments. Conducted in a shielded underground industrial environment, the work integrates commercial ray-tracing simulations, Gaussian process regression with a rational quadratic kernel, a mobile robotic platform, and off-the-shelf 5G user equipment to perform real-world measurements. It reveals for the first time that dynamic adaptation of MIMO spatial layers is the primary cause of systematic overestimation of throughput by conventional channel models, with ray tracing significantly overpredicting performance even in line-of-sight conditions. In contrast, a data-driven approach that directly learns end-to-end throughput reduces prediction error by approximately two-thirds and exhibits near-zero bias, demonstrating clear superiority over traditional channel-centric modeling.

This study addresses the optimization of LoRaWAN gateway deployment, a problem whose efficacy critically hinges on the accuracy of the underlying channel model. To this end, the authors propose an integrated framework combining ray tracing with discrete-event network simulation, leveraging stochastic, empirical, and ray-tracing-based channel models to generate wireless performance metrics. A combinatorial optimization model respecting power constraints is then formulated to determine the optimal gateway placement. The work presents the first systematic evaluation of how different channel modeling approaches influence deployment outcomes, revealing a fundamental trade-off between model fidelity and computational overhead. Experimental results further demonstrate that even within the same environment, distinct ray-tracing tools yield significantly divergent predictions, underscoring the pivotal role of high-fidelity channel modeling in enabling precise and reliable network planning.

Existing foundational channel models lack a unified and fair evaluation benchmark, making cross-model comparisons difficult due to inconsistent assessment protocols. To address this gap, this work proposes CFM-Bench, the first standardized evaluation framework tailored for foundational channel models. CFM-Bench integrates six channel configurations and six task groups, spanning three key application domains: physical layer communications, radio access networks, and integrated sensing and communication. The benchmark combines 3GPP statistical simulations, ray-tracing data, real-world measurements, and multimodal vehicular datasets, employing trajectory-, session-, and link-level isolation to rigorously prevent test leakage and contamination from pretraining data. It supports diverse tasks including CSI feedback, channel extrapolation, beam prediction, and localization. CFM-Bench thus provides a reproducible and comparable platform that systematically advances research into the cross-domain generalization and practical deployment of foundational channel models.

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.

Current cellular network coverage assessment relies solely on minimum bandwidth thresholds (e.g., 35/3 Mbps for 5G), neglecting spatiotemporal variations in availability and stability, and thus failing to capture dynamic performance fluctuations induced by radio propagation characteristics, traffic load dynamics, and capacity constraints. To address this, we propose a novel paradigm—Quality of Coverage (QoC)—which establishes a multi-dimensional KPI framework integrating availability, stability, and empirically measured performance, enabling fine-grained quantification of coverage across both spatial and temporal dimensions. Leveraging three independent large-scale measurement datasets, and jointly incorporating radio propagation modeling with load-capacity relationship analysis, we rigorously validate QoC’s superiority over conventional metrics. Results demonstrate that QoC more accurately characterizes network dynamism, offering an interpretable, scalable theoretical foundation and practical toolkit for coverage planning, optimization, and SLA evaluation.