3GPP TR 38.901 Channel Model

Zhu, Qiuming; Wang, Cheng-Xiang; Hua, Boyu; Masuda, Kai; Jiang, Shan; Yao, Mengtian

doi:10.1002/9781119471509.w5gref048

Cited by 50 publications

(13 citation statements)

References 60 publications

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“…To highlight the impact of posture instead of the multipath propagation, the birth-death process of clusters in NLoS paths and the delay variations caused by moving of clusters are omitted. The simulated U2V communication system operates at 2.4 GHz and the path and sub-path angles are assumed to follow the 3GPP 38.901 standard definition [39,35]. The mean value of the Ricean K-factor is set as 7 and the variation is set as 4, which means the LoS component is also timevariant.…”

Section: Numerical Results and Discus-sionsmentioning

confidence: 99%

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

Hua¹,

Zhou²,

Zhu³

et al. 2022

Preprint

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Considering the unmanned aerial vehicle (UAV) three-dimensional (3D) posture, a novel 3D non-stationary geometry-based stochastic model (GBSM) is proposed for multiple-input multipleoutput (MIMO) UAV-to-vehicle (U2V) channels. It consists of a line-of-sight (LoS) and non-line-of-sight (NLoS) components. The factor of fuselage posture is considered by introducing a time-variant 3D posture matrix. Some important statistical properties, i.e. the temporal autocorrelation function (ACF) and spatial cross correlation function (CCF), are derived and investigated. Simulation results show that the fuselage posture has significant impact on the U2V channel characteristic and aggravate the non-stationarity. The agreements between analytical, simulated, and measured results verify the correctness of proposed model and derivations. Moreover, it is demonstrated that the proposed model is also compatible to the existing GBSM without considering fuselage posture.

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Section: Numerical Results and Discus-sionsmentioning

confidence: 99%

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

Hua¹,

Zhou²,

Zhu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Even amongst different deterministic or stochastic approaches, there are variations in their benefits and limitations. To address such challenges and provide an optimum solution different hybrid approaches combining different deterministic approaches [166], [240], [241] or deterministic and stochastic approaches [96], [242]- [247] are proposed. The details of such trade-offs are further elaborated in subsubsections IV-C1 and IV-C2.…”

Section: Hybrid Modelingmentioning

confidence: 99%

THz Channel Sounding and Modeling Techniques: An Overview

Ghosh

Kim

2023

IEEE Access

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As the world warms up to the idea of millimeter wave (mmWave) communication and fifth generation (5G) mobile networks, realization slowly dawns that the data rate, latency, throughput, and other performance metrics that are used to assess a new wireless communication technology will not be enough to support the demands of envisioned futuristic applications. Thus there is an eagerness to further climb up the frequency ladder to use the large swathes of available spectrum in the 0.1 − 10 THz band which is expected to act as the key technology enabler to fulfill the requirements of the sixth generation (6G) wireless communication and possibly even beyond. Channel measurement and modeling are crucial to the design and deployment of future wireless communication systems and researchers across the world are putting their best foot forward to accelerate the process. The present article presents comprehensive assimilation of research efforts in the context of THz channel sounding. A detailed overview of the current channel sounding techniques is first introduced followed by their relevance to THz band channel measurement. An in-house novel channel sounder developed for THz band measurement is also briefly introduced in this context. The paper next provides elaborate dissemination of various measurement campaigns in the band of interest followed by the modeling techniques that are available in the literature and are being adopted for the THz band. Post the description of different challenges and future research directions in the context of sounding, measurement, and modeling the article is concluded.

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“…The size of the cells and channel model change according to different scenarios [ 39 ], which are referenced from the test scenior in the 3GPP protocol, such as UMa, RMa, RMi. By default, the outsider scenario of the non-line-of-sight of the RMa was selected to evaluate the proposed method.…”

Section: Simulationsmentioning

confidence: 99%

“…In the traditional MADRL, since the complex communication environments usually contain a large amount of information, uniform experience replay leads to poor stability and the slow convergence of neural networks; (2) It weakens the interconnectivity between agents, especially in the system where the agent plays a direct role with the other agents (for example, an agent promotes individually and hinders others). Therefore, the traditional MADRL, which uses a distributed training process to explore solutions, is unsuitable for finding the action characteristics of each agent; and (3) It is not realistic to simplify the channel with a free-space propagation model, since some test scenarios are neglected in different channel models [39], including the urban macro-cell (UMa), rural macro0cell (RMa), and rural micro-cell (RMi) in IMT-2020.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning

Zhang,

Lv,

Huang

et al. 2023

Sensors

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Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4–15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.

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3GPP TR 38.901 Channel Model

Cited by 50 publications

References 60 publications

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

THz Channel Sounding and Modeling Techniques: An Overview

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning

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