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2013 International Conference on Information Networking (ICOIN)
A study on detection method of same spreading code signals by multimodulus algorithm
Bangkok Thailand
January 28-January 30
ISBN: 978-1-4673-5740-1
Genichiro Murata, Tokyo University of Agriculture and Technology 2-24-16, Nakacho, Koganei-shi, Tokyo, 184-8588
Kenta Umebayashi, Tokyo University of Agriculture and Technology 2-24-16, Nakacho, Koganei-shi, Tokyo, 184-8588
Yasuo Suzuki, Tokyo University of Agriculture and Technology 2-24-16, Nakacho, Koganei-shi, Tokyo, 184-8588
This paper investigates a signal detection method with a RAKE combiner when the receiving signals are spreaded by a same spreading code. In this case, a blind channel estimation is difficult and this issue has not been investigated deeply. We propose two blind channel estimation methods based on the multimodulus algorithm (MMA), i.e., MMA-IQ and MMA-I channel estimation methods, for the issue. When one dimensional modulation scheme, such as differential binary phase-shift keying (DBPSK) is used, a channel estimation by the MMA-IQ channel estimation method may provides two states since the MMA uses the constant modulus algorithm (CMA) in real and imaginary components (I- and Q-axis in I-Q plane). Specifically, the first state is that the channel estimation corresponds to one signal in the multiple receiving signals, and the second state is that the channel estimation consist of two signals. In the later state, it is possible to detect the both signals at once. However, the receiver has to recognize the state before the signal detection, thus we also propose a state recognition method. On the other hand, in the MMA-I channel estimation method, only I-axis is used thus the channel estimation is always for one signal. Numerical results showed that bit error rate performances of the MMA-IQ and MMA-I.
Citation:
Genichiro Murata, Kenta Umebayashi, Yasuo Suzuki, "A study on detection method of same spreading code signals by multimodulus algorithm," icoin, pp.268-273, 2013 International Conference on Information Networking (ICOIN), 2013
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