• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1631-1637
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• 2019

In this paper, we propose a low-complexity robust maximum likelihood (ML) receiver for generalized spatial modulation. The proposed receiver performs the transmit antenna partition to lower the computational loads. After we divide the transmit antenna combinations into two parts, one of which is "the likely TAC part," and the other of which is "the unlikely TAC part", based on the minimum mean square error (MMSE) filtering output. We first perform the maximum likelihood detection only in the likely TAC part. Then we evaluate the reliability of the solution found in the first search, and based its reliability we decide whether we continue the search in the unlikely TAC part. This partitioned search strategy maintains the performance of the conventional robust maximum likelihood receiver and simultaneously lowers computational loads. Through simulation, we found that our newly-proposed receiver achieves considerable gains over the conventional robust ML detector in terms of the computational loads while providing almost the same performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.3 s.11
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• pp.29-39
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• 2006

In this paper, we propose an efficient method to detect the signal and evaluate performance of the system in frequency selective fading channel. We combine proposed system with OFDM (Orthogonal Frequency Division Multiplexing) to improve performance of the system. First, we study the SOSTTC-OFDM system using two transmit antenna and one receive antenna, and compare performance of the proposed space-time coded OFDM with that of previous system. We expand this system to the system using four transmit antennas with the proposed decoding method. Simulation results show that the proposed decoding method can detect the signal efficiently, and we identify that the performance of the proposed system is shown with varying doppler frequency in frequency selective fading channel.