Joonas Kokkoniemi and Markku Juntti. 2021. Channel modeling for reflective phased array type RISs in mmWave networks. In Proceedings of the 5th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems (mmNets '21). Association for Computing Machinery, New York, NY, USA, 31–36. DOI:https://doi.org/10.1145/3477081.3481675
Channel modeling for reflective phased array type RISs in mmWave networks
|Author:||Kokkoniemi, Joonas1; Juntti, Markku1|
1Centre for Wireless Communications, University of Oulu P.O. Box 4500, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022030221484
Association for Computing Machinery,
|Publish Date:|| 2022-03-02
This paper presents and overview, challenges and some recent results on channel modeling of the phased array type reconfigurable intelligent surfaces (RISs) in the millimeter Wave band (mmWave, 30‐300 GHz) networks. The RISs have been under intense investigation lately for their ability to provide control on the mmWave sparse channels. This has been shown to improve the coverage and signal power levels in environments where line-of-sight (LoS) paths cannot be guaranteed. The gain properties of RISs in LoS channels are analyzed with respect to carrier frequency and the size of the RIS. It is shown that the RISs provide the best gain compared to LoS links at lower frequencies due to the larger RIS sizes for a fixed number of antenna elements. On the other hand, high gain RISs tend to push the near field far away from the array in the low frequencies. These observations mean that different parts of the mmWave band have advantages and disadvantages when utilizing RIS assisted channels.
|Pages:||31 - 36|
5th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems, mmNets 2021, Part of ACM MobiCom 202
ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the Horizon 2020, European Union’s Framework Programme for Research and Innovation, under grant agreement no. 871464 (ARIADNE). It was also supported in part by the Academy of Finland 6Genesis Flagship under grant no. 318927.
|EU Grant Number:||
(871464) ARIADNE - Artificial Intelligence Aided D-band Network for 5G Long Term Evolution
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2022 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 5th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems (mmNets '21), http://dx.doi.org/10.1145/3477081.3481675.