scholarly journals Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

2020 ◽  
Author(s):  
Vincent Savaux ◽  
Matthieu Kanj
Keyword(s):  
Maximum Likelihood ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Cross Correlation ◽  
Low Complexity ◽  
Optimal Estimator ◽  
Synchronization Signal ◽  
Ml Estimator ◽  
Hard Decision

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by<br>the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto-<br>correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator<br>based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions<br>of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS,<br>and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a<br>performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach<br>the performance of ML after several repetitions for a lower overall complexity.

scholarly journals Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

2020 ◽  
Author(s):  
Vincent Savaux ◽  
Matthieu Kanj
Keyword(s):  
Maximum Likelihood ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Cross Correlation ◽  
Low Complexity ◽  
Optimal Estimator ◽  
Synchronization Signal ◽  
Ml Estimator ◽  
Hard Decision

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by<br>the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto-<br>correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator<br>based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions<br>of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS,<br>and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a<br>performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach<br>the performance of ML after several repetitions for a lower overall complexity.

scholarly journals Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model

2018 ◽  
Vol 7 (3) ◽  
pp. 38 ◽  
Author(s):  
Xiao-Li Hu ◽  
Pin-Han Ho ◽  
Limei Peng
Keyword(s):  
Maximum Likelihood ◽  
Performance Analysis ◽  
Theoretical Analysis ◽  
Numerical Experiments ◽  
Likelihood Estimation ◽  
Signal Strength ◽  
Strength Model ◽  
Transmit Power ◽  
Ml Estimation ◽  
Theoretical Performance

We study theoretical performance of Maximum Likelihood (ML) estimation for transmit power of a primary node in a wireless network with cooperative receiver nodes. The condition that the consistence of an ML estimation via cooperative sensing can be guaranteed is firstly defined. Theoretical analysis is conducted on the feasibility of the consistence condition regarding an ML function generated by independent yet not identically distributed random variables. Numerical experiments justify our theoretical discoveries.

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