| 1. |
- Gan, Mingming, et al.
(author)
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Modeling time-variant fast fading statistics of mobile peer-to-peer radio channels
- 2011
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In: [Host publication title missing]. - 1550-2252. - 9781424483327
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Conference paper (peer-reviewed)abstract
- Abstract in UndeterminedThe radio channels between nodes of an indoor peer-to-peer network show specific fast fading characteristics. Depending on the mobility and on the scattering properties of the environment, different kinds of fading distributions can occur: Ricean fading between static nodes, but also Rayleigh or even double-Rayleigh fading between mobile nodes. We investigate fast fading in indoor peer-to-peer networks based on radio channel measurements. It turns out that the fading statistics change over time. While the predominant fading mechanism is a combination of Rayleigh and double-Rayleigh fading, Ricean fading also occasionally occurs. On top of that, indoors, the statistics of the fast fading change over time even for small-motions of the nodes, since the propagation environment is inhomogeneous. We comprehensively model these effects using a hidden Markov model, parameterized from our measurements. The model is validated, revealing a convincing fit between the model and the measurements.
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| 2. |
- Grebien, Stefan, et al.
(author)
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Localization and tracking
- 2021
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In: Inclusive Radio Communications for 5G and Beyond. - 9780128205815 ; , s. 253-293
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Book chapter (other academic/artistic)abstract
- The use of radio signal for position tracking (positioning) and navigation has a long tradition. Most notably, global navigation satellite systems (GNSS) are now widely applied for civilian applications, ranging all the way from tracking of cargo containers to gaming. However, GNSS suffers from limited coverage in dense urban areas and indoors and also from a limited position accuracy, which rules out most location-based applications that are concerned with the (natural) interaction of humans with their immediate surroundings, the physical environment in which we live, work, and spend our free time.This chapter highlights the expected benefits of the new wireless technologies proposed for the fifth generation (5G) of mobile communication systems and for the Internet of Things (IoT) for the purpose of improved radiopositioning. It starts with a summary of the most promising future application scenarios for high-accuracy positioning, followed by a discussion on the technical challenges arising from those applications, and on the expected features and limitations of 5G and IoT wireless systems with respect to positioning. These initial considerations are followed by four technical sections, focusing on measurement acquisition and modeling, position estimation, multipath-assisted positioning, and system-level studies. Finally, a range of experimental facilities are described, which have been used to validate the theoretical contributions.
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| 3. |
- Haneda, Katsuyuki, et al.
(author)
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Comparison of angular and delay spreads between channel measurements and the COST channel model
- 2010
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In: ; , s. 477-480
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Conference paper (peer-reviewed)abstract
- The COST2100 channel model is a reference channel model which provides dynamic multiple-input multiple-output channel responses for radio system simulations. In this paper, channels created by the COST2100 model were compared to channel measurements in order to understand behaviours of the model. Model parameters of the COST2100 model were derived by dynamic double-directional channel measurements with which the channel realizations from the COST2100 model were compared. Delay and angular spreads were used as a simple but important metric for the comparison. Despite a fundamental difference that the COST2100 model is a geometry- based stochastic model while the channel measurements are ob- tained in deterministic environments, the comparison revealed an acceptable level of agreement for practical channel simulations.
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| 4. |
- Haneda, Katsuyuki, et al.
(author)
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Comparison of delay and angular spreads between channel measurements and the COST2100 channel model
- 2010
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In: 2010 Loughborough Antennas and Propagation Conference, LAPC 2010. - 9781424473052 ; , s. 477-480
-
Conference paper (peer-reviewed)abstract
- The COST2100 channel model is a reference channel model which provides dynamic multiple-input multiple-output channel responses for radio system simulations. In this paper, channels created by the COST2100 model were compared to channel measurements in order to understand behaviours of the model. Model parameters of the COST2100 model were derived by dynamic double-directional channel measurements with which the channel realizations from the COST2100 model were compared. Delay and angular spreads were used as a simple but important metric for the comparison. Despite a fundamental difference that the COST2100 model is a geometry-based stochastic model while the channel measurements are obtained in deterministic environments, the comparison revealed an acceptable level of agreement for practical channel simulations
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| 5. |
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| 6. |
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| 7. |
- Huang, Chen, et al.
(author)
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Artificial intelligence enabled radio propagation for communications – Part I: Channel characterization and antenna-channel optimization
- 2022
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In: IEEE Transactions on Antennas and Propagation. - 0018-926X. ; 70:6, s. 3939-3954
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Research review (peer-reviewed)abstract
- To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigatethe application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper.
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| 8. |
- Huang, Chen, et al.
(author)
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Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling
- 2022
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In: IEEE Transactions on Antennas and Propagation. - 0018-926X. ; 70:6, s. 3955-3969
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Research review (peer-reviewed)abstract
- This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well.
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| 9. |
- Lau, Buon Kiong, et al.
(author)
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Multiple antenna terminals
- 2011
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In: MIMO: From Theory to Implementation. ; , s. 267-298
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Book chapter (other academic/artistic)
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| 10. |
- Liu, Lingfeng, et al.
(author)
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The COST 2100 MIMO Channel Model
- 2012
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In: IEEE Wireless Communications. - 1536-1284. ; 19:6, s. 92-99
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Journal article (peer-reviewed)abstract
- The COST 2100 channel model is a geometry-based stochastic channel model (GSCM) that can reproduce the stochastic properties of multi-link Multiple-Input Mulitple-Output (MIMO) channels over time, frequency and space. By contrast to other popular GSCMs, the COST 2100 approach is generic and flexible, making it suitable to model multi-user or distributed MIMO scenarios. In this paper a concise overview of the COST 2100 channel model is presented. Main concepts are described, together with useful implementation guidelines. Recent developments, including dense multipath components, polarization and multi-link aspects are also discussed.
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