scholarly journals System Level Integration of Irregular Repetition Slotted ALOHA for Industrial IoT in 5G New Radio

Author(s):  
H. Murat Gursu ◽  
M. Cagatay Moroglu ◽  
Mikhail Vilgelm ◽  
Federico Clazzer ◽  
Wolfgang Kellerer
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rony Kumer Saha

In this paper, we first give an overview of the coexistence of cellular with IEEE 802.11 technologies in the unlicensed bands. We then present a coexistence mechanism for Fifth-Generation (5G) New Radio on Unlicensed (NR-U) small cells located within buildings to coexist with the IEEE 802.11ad/ay, also termed as Wireless Gigabit (WiGig). Small cells are dual-band enabled operating in the 60 GHz unlicensed and 28 GHz licensed millimeter-wave (mmW) bands. We develop an interference avoidance scheme in the time domain to avoid cochannel interference (CCI) between in-building NR-U small cells and WiGig access points (APs). We then derive average capacity, spectral efficiency (SE), and energy efficiency (EE) performance metrics of in-building small cells. Extensive system-level numerical and simulation results and analyses are carried out for a number of variants of NR-U, including NR standalone, NR-U standalone, and NR-U anchored. We also analyze the impact of the spatial reuse of both mmW spectra of multiple NR-U anchored operators with a WiGig operator. It is shown that NR-U anchored provides the best average capacity and EE performances, whereas NR-U standalone provides the best SE performance. Moreover, both vertical spatial reuse intrabuilding level and horizontal spatial reuse interbuilding level of mmW spectra in small cells of an NR-U anchored can improve its SE and EE performances. Finally, we show that by choosing appropriate values of vertical and horizontal spatial reuse factors, the proposed coexistence mechanism can achieve the expected SE and EE requirements for the future Sixth-Generation (6G) mobile networks.


Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1196 ◽  
Author(s):  
Eunmi Chu ◽  
Janghyuk Yoon ◽  
Bang Jung

In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.


Author(s):  
Sandra Lagen ◽  
Kevin Wanuga ◽  
Hussain Elkotby ◽  
Sanjay Goyal ◽  
Natale Patriciello ◽  
...  

1998 ◽  
Author(s):  
Martin P. Charns ◽  
Victoria A. Parker ◽  
William H. Wubbenhorst
Keyword(s):  

2018 ◽  
Vol 4 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Ivan J. Raymond ◽  
Matthew Iasiello ◽  
Aaron Jarden ◽  
David Michael Kelly
Keyword(s):  

2007 ◽  
Vol 51 (1-2) ◽  
pp. 43
Author(s):  
Balázs Polgár ◽  
Endre Selényi
Keyword(s):  

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