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

Sun, Yutong; Zhang, Jianhua; Yu, Li; Zhang, Zhen; Zhang, Ping

doi:10.1109/lwc.2023.3237827

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…The large-scale level includes layout and global environment representations for large-scale parameters (LSP), while the smallscale level comprises local and target representations for small-scale parameters (SSP), and line-of-sight (LOS) blockage. In a recent study [36], the concept of propagation environment semantics (PES) was proposed, which is a set of propagation environment semantic symbols (PESS). The PESS are deconstructed environment representations at a semantic level and can be utilized for the prediction of wireless…”

Section: Semantic Communication Architecturementioning

confidence: 99%

Advances and Challenges in Semantic Communications: A Systematic Review

Zhang,

Liu,

Song

et al. 2023

NSO

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Inspired by the recent success of Machine Learning (ML), the concept of semantic communication introduced by Weaver in 1949 has gained significant attention and has become a promising research direction. Unlike conventional communication systems, semantic communication emphasizes the precise retrieval of conveyed meaning from the source to the receiver, rather than focusing on the accurate transmission of symbols. Thus, semantic communication can achieve a significant gain in source data compression, alleviate communication bandwidth pressure, and support new intelligent services, which is envisioned as a crucial enabler of future sixth-generation (6G) networks. In this review, we critically summarize the advances made in semantic information and semantic communications, including theory, architecture, and potential applications. Moreover, we deeply explore the major challenges in developing semantic communications and present the development prospects, aiming to prompt further scientific and industrial advances in semantic communications.

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Section: Semantic Communication Architecturementioning

confidence: 99%

Advances and Challenges in Semantic Communications: A Systematic Review

Zhang,

Liu,

Song

et al. 2023

NSO

View full text Add to dashboard Cite

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“…For evaluating the performance of the target image-based method and complete imagebased method and analysis of the robustness of different network configurations, the max pooling and main layers of ResNet-18 are set with different parameters. Because the max pooling and main layers can affect the expressive capacity and ability to capture detailed information about the network, the max pooling layer is configured with parameters of (4, 1) and (10,8), where (4, 1) indicates a kernel size of 4 and a stride of 1 and (10, 8) represents a kernel size of 10 and a stride of 8. Additionally, the basic blocks in ResNet-18 consist of 4 layers each, with plane sizes of (64, 128, 256, 256) and (32, 64, 128, 128), respectively.…”

Section: Performance Evaluation Of Beam Predictionmentioning

confidence: 99%

“…In other words, the channel becomes more sensitive to the ever-changing propagation environment and introduces more significant uncertainty in transmission. Channel-related propagation environment semantics (PES) [10] can be used to establish a direct mapping between the propagation environment and the channel, enabling the development of an online predictive network.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can be combined with machine learning (ML) techniques, enhancing efficiency and adaptability to environmental changes. In [10], PES is defined by representing the environment using features and graphs, which improves the efficiency of beam prediction tasks. Images are commonly used to address the prediction challenge due to their ease of collection.…”

Section: Introductionmentioning

confidence: 99%

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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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