Environment Features-Based Model for Path Loss Prediction

Sun, Yutong; Zhang, Jianhua; Zhang, Yuxiang; Yu, Li; Yuan, Zhiqiang; Wang, Xinbo

doi:10.1109/lwc.2022.3192516

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…Radio waves encounter different objects, giving rise to diverse paths. These paths are formed by various propagation mechanisms, including line-of-sight (LOS) transmission, reflection, and diffraction [23,24]. As a result, the position, size, and material of scatterers directly impact the characteristics of the channel.…”

Section: Problem Formulation and Architecturementioning

confidence: 99%

PC-SC: A Predictive Channel-Based Semantic Communication System for Sensing Scenarios

et al. 2023

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Due to its significant efficiency, semantic communication emerges as a promising technique for sixth-generation (6G) networks. The wireless propagation channel plays a crucial role in system design, as it directly impacts transmission performance and capability. Given the increasingly complex communication scenarios, the channel exhibits high dynamism and poses challenges in acquisition. In such cases, sensing-based methods have drawn significant attention. To enhance system robustness, we propose a predictive channel-based semantic communication (PC-SC) system tailored for sensing scenarios. The PC-SC system is designed with an orientation toward applications by directly taking semantic targets into account. It comprises three modules: transmitter, predictive channel, and receiver. Firstly, at the transmitter, instead of employing global semantic coding, the scheme emphasizes preserving semantic information through target-based semantic extraction. Secondly, the channel prediction module predicts the dynamic wireless channel by utilizing the extracted target-based semantic information. Finally, at the receiver, the target-based semantic information can be utilized to meet specific application requirements. Alternatively, pre-captured background and semantic targets can be composited to fulfill complete image reconstruction needs. We evaluate the proposed approach by using a sensing image transmission scenario as a case study. Experimental results demonstrate the superiority of the PC-SC system in terms of image reconstruction performance and cost savings of bit. We employ beam prediction as a channel prediction task and find that the targets-based method outperforms the complete image-based approach in terms of efficiency and robustness, which can provide 32% time-saving.

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Section: Problem Formulation and Architecturementioning

confidence: 99%

PC-SC: A Predictive Channel-Based Semantic Communication System for Sensing Scenarios

et al. 2023

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“…The LSP can reflect channel quality evaluation related to the performance [10], where the parameter types should not be considered in detail for coarse semantic mapping. In addition to the LSP, the blockage characteristic of LOS also has a significant impact, where the blockage would attenuate the performance.…”

Section: A Task-oriented Channel Propertiesmentioning

confidence: 99%

“…In practice, diverse methods can be utilized for PESS feature calculation according to the geometric relationship between the Tx, Rx, the scatterer. There, we use the method mentioned in [10], which obtains the blockage feature for each scatterer by describing the extent to which LOS and scatterers intersect. Specifically, the distance d i between the center point of i-th scatterer and the LOS is calculated according to the position coordinates.…”

Section: B Task-oriented Pes Constructionmentioning

confidence: 99%

“…The environment samples with random scatterer changing are considered. The 3D modeling software Blender and the ray-tracing tool WirelessInSite are utilized for the traceable environment, and channel generation [10] as shown in Fig. 4.…”

Section: A Simulation Settingsmentioning

confidence: 99%

“…In [8] [9], the cluster nuclei is proposed by directly mapping the physical environment to the channel. For representing the propagation environment from different perspectives, the environment features and graph representations are proposed in [10] and [12], which can assist the efficient channel prediction in dynamic environments. Hence, we believe that defining the propagation environment semantics (PES) by considering the different environment representations, i.e., propagation environment semantic symbols (PESS), is crucial for highly efficient prediction by leveraging the environment information directly.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

Sun¹,

Zhang²,

Liu³

et al. 2022

Preprint

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In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm

Yuliana,

Iskandar,

Hendrawan

et al. 2024

2024 10th International Conference on Wireless and Telematics (ICWT)

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Environment Features-Based Model for Path Loss Prediction

Cited by 14 publications

References 14 publications

PC-SC: A Predictive Channel-Based Semantic Communication System for Sensing Scenarios

PC-SC: A Predictive Channel-Based Semantic Communication System for Sensing Scenarios

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm

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