Low-complexity frequency-offset insensitive detection for orthogonal modulation

2005 ◽  
Vol 41 (22) ◽  
pp. 1226 ◽  
Author(s):  
S. Park ◽  
D. Park ◽  
H. Park ◽  
K. Lee
Keyword(s):  
Low Complexity ◽  
Frequency Offset ◽  
Orthogonal Modulation

Low-complexity soft-input soft-output demodulation of orthogonal modulation

2007 ◽  
Vol 11 (12) ◽  
pp. 919-921 ◽  
Author(s):  
Shuling Che ◽  
Peng Wang ◽  
Li Ping ◽  
Xinmei Wang
Keyword(s):  
Low Complexity ◽  
Orthogonal Modulation

Robust MVDR Estimation via Polynomial Rooting for MC-CDMA Carrier Frequency Offset

2013 ◽  
Vol 284-287 ◽  
pp. 2687-2693
Author(s):  
Ing Jiunn Su ◽  
Chiao Chan Huang
Keyword(s):  
Carrier Frequency ◽  
Code Division Multiple Access ◽  
Multiple Access ◽  
Carrier Frequency Offset ◽  
Low Complexity ◽  
Frequency Offset ◽  
High Accuracy ◽  
Cdma Systems ◽  
Code Division Multiple ◽  
Polynomial Rooting

In this letter, we present a blind carrier frequency offset (CFO) estimator by exploiting the polynomial rooting technique for multicarrier-code division multiple access (MC-CDMA) systems. Relative high accuracy and low-complexity to the CFO estimation can be achieved by rooting a polynomial. Simulation results are provided for illustrating the effectiveness of the proposed blind polynomial rooting estimator.

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>

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