Energy detection for M-QAM signals
|Author:||Ishihara, Shun1; Umebayashi, Kenta2; Lehtomäki, Janne J.3|
1Faculty of Engineering, Tokyo University of Agriculture and Technology, Koganei, Japan
2Graduate School of Engineering, Tokyo University of Agriculture and Technology, Koganei, Japan
3Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023071390588
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-07-13
Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for \(M\)-ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume \(M\)=∞ to avoid the brute-force search. Due to the assumption of \(M\), the proposed models are independent of \(M\) and can handle adaptive modulation where \(M\) can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model.
|Pages:||6305 - 6319|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work of Kenta Umebayashi was supported in part by the European Commission in the Framework of the Project 5GEnhance under Grant H2020-EUJ-02-2018 and Grant 815056, in part by the ‘‘Strategic Information and Communications Research and Development Promotion Programme (SCOPE)’’ of Ministry of Internal Affairs and Communications (MIC) of Japan under Grant JPJ000595, and in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP18KK0109. The work of Janne J. Lehtomäki was supported by the Academy of Finland 6Genesis Flagship under Grant 318927.
|EU Grant Number:||
(815056) 5G-Enhance - 5G Enhanced Mobile Broadband Access Networks in Crowded Environments
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.