Low complexity frequency offset estimator for OFDM with time-frequency training sequence

2005 ◽  
Author(s):  
Yanxiang Jiang ◽  
Dongming Wang ◽  
Xiqi Gao ◽  
Xiaohu You
Keyword(s):  
Low Complexity ◽  
Frequency Offset ◽  
Training Sequence ◽  
Time Frequency

scholarly journals On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

2021 ◽  
Author(s):  
Vincent Savaux ◽  
Christophe Delacourt ◽  
Patrick Savelli
Keyword(s):  
Cost Function ◽  
Optimal Algorithm ◽  
A Priori ◽  
Low Complexity ◽  
Frequency Offset ◽  
Maximization Problem ◽  
Frequency Synchronization ◽  
Time Frequency ◽  
Time Offset ◽  
Ml Estimator

This paper deals with time and frequency synchronization in LoRa system based on the preamble symbols. A thorough analysis of the maximum likelihood (ML) estimator of the delay (time offset) and the frequency offset shows that the resulting cost function is not concave. As a consequence the a priori solution to the maximization problem consists in exhaustively searching over all the possible values of both the delay and the frequency offset. Furthermore, it is shown that these parameters are intertwined and therefore they must be jointly estimated, leading to an extremely complex solution. Alternatively, we show that it is possible to recover the concavity of the cost function, from which we suggest a low-complexity synchronization algorithm, whose steps are described in detail. Simulations results show that the suggested method reaches the same performance as the ML exhaustive search, while the complexity is drastically reduced, allowing for a real-time implementation of a LoRa receiver. <br>

scholarly journals On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

2021 ◽  
Author(s):  
Vincent Savaux ◽  
Christophe Delacourt ◽  
Patrick Savelli
Keyword(s):  
Cost Function ◽  
Optimal Algorithm ◽  
A Priori ◽  
Low Complexity ◽  
Frequency Offset ◽  
Maximization Problem ◽  
Frequency Synchronization ◽  
Time Frequency ◽  
Time Offset ◽  
Ml Estimator

This paper deals with time and frequency synchronization in LoRa system based on the preamble symbols. A thorough analysis of the maximum likelihood (ML) estimator of the delay (time offset) and the frequency offset shows that the resulting cost function is not concave. As a consequence the a priori solution to the maximization problem consists in exhaustively searching over all the possible values of both the delay and the frequency offset. Furthermore, it is shown that these parameters are intertwined and therefore they must be jointly estimated, leading to an extremely complex solution. Alternatively, we show that it is possible to recover the concavity of the cost function, from which we suggest a low-complexity synchronization algorithm, whose steps are described in detail. Simulations results show that the suggested method reaches the same performance as the ML exhaustive search, while the complexity is drastically reduced, allowing for a real-time implementation of a LoRa receiver. <br>

scholarly journals Multilayer Learning Network for Modulation Classification Assisted with Frequency Offset Cancellation in Satellite to Ground Link

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Guan Qing Yang
Keyword(s):  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Frequency Offset ◽  
Training Sequence ◽  
Classification Performance ◽  
Modulation Classification ◽  
Correct Classification ◽  
Offset Cancellation ◽  
Learning Network ◽  
Hidden Layer

A multilayer learning network assisted with frequency offset cancellation is proposed for modulation classification in satellite to ground link. Carrier frequency offset greatly reduces modulation classification performance. It is necessary to cancel frequency offset before modulation classification. Frequency offset cancellation weights are established through multilayer learning network based on MSE criterion. Then the weight and hidden layer of multilayer learning network are also established for modulation classification. The hidden layers and weight are trained and tuned to combat the interference introduced by frequency offset. Compared with current modulation classification algorithm, the proposed multilayer learning network greatly improves the Probability of Correct Classification (PCC). It has been proven that the proposed multilayer learning network assisted with frequency offset has higher performance for modulation classification within the same training sequence.

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