| 1. |
- Huang, Chen, et al.
(författare)
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Artificial intelligence enabled radio propagation for communications – Part I: Channel characterization and antenna-channel optimization
- 2022
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Ingår i: IEEE Transactions on Antennas and Propagation. - 0018-926X. ; 70:6, s. 3939-3954
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Forskningsöversikt (refereegranskat)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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| 2. |
- Huang, Chen, et al.
(författare)
-
Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling
- 2022
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Ingår i: IEEE Transactions on Antennas and Propagation. - 0018-926X. ; 70:6, s. 3955-3969
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Forskningsöversikt (refereegranskat)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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| 3. |
- Pradhan, Anish, et al.
(författare)
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Stochastic Geometry Analysis of a New GSCM with Dual Visibility Regions
- 2023
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Ingår i: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications : 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023 - 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023. - 9781665464833
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Konferensbidrag (refereegranskat)abstract
- The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.
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