• ETRI Journal
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• v.39 no.5
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• pp.707-717
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• 2017

This paper presents a novel generalized quadrature spatial modulation (GQSM) transmission scheme using antenna grouping. The proposed GQSM scheme combines QSM and conventional spatial multiplexing (SMux) techniques in order to improve the spectral efficiency (SE) of the system. Analytical and simulation results show that the proposed transmission scheme has minimal losses in terms of the average bit error probability along with the advantage of an increased SE compared with previous SM and QSM schemes. For the case studies, this advantage represents a reduction of up to 81% in terms of the number of required transmit antennas compared with QSM. In addition, a detection architecture based on the ordered successive interference cancellation scheme and the QR decomposition is presented. The proposed QRD-M adaptive algorithm showed a near-maximum-likelihood performance with a complexity reduction of approximately 90%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.731-738
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• 2017

In this paper, we proposed a modified ML detector for generalized spatial modulation which is a method among Multiple-input Multiple-output. This proposed method detects signal applying modified channel statement information based on priority. Complexity in conventional methods increases as increasing the number of active antennas. To solve this problem, we proposed a new ML method using static channel information decided by the number of transmit antennas and the number of receive antennas. This method detects active antennas one by one through priority. The proposed method has proved benefit on complexity compared with conventional method through simulations. When the number of transmit antennas is equal to 10, there is approximately 45% complexity reduction.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.523-529
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• 2017

In this paper, we propose an efficient sphere decoding scheme that reduces computational complexity by combining receive and transmit ordering techniques in generalized spatial modulation systems, where the indexes of activated transmit antennas as well as the transmit symbols are exploited to transfer information to the receiver. In this scheme, the receive signals are optimally ordered so that the calculation for a candidate solution outside the sphere is terminated early to lower the computational complexity. In addition, the transmit ordering technique is applied to first search for candidate symbols and activated antennas having higher probabilities to further reduce the computational complexity. Simulation results show that the proposed doubly ordered sphere decoding scheme provides the same bit error rate performance with the conventional sphere decoding method and the sphere decoder employing only the receive ordering technique while it requires lower computational complexity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.43-46
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• 2015

Recently, full-duplex communications has been considered as one of the most promising techniques for net-generation mobile communication system. In this paper, we propose a compressed sensing based signal detection technique for full-duplex generalized spatial modulation (FD-GSM) systems. In FD-GSM systems, some antennas are used for signal transmission according to input data and the otehrs are used for detecting signals received over the same frequency band. The self-interference (SI) is assumed to be completely removed by help for the recently proposed SI cancellation techniques. The proposed signal detection technique significantly outperforms the conventional ones in terms of symbol error rate (SER). We will investigate the optimal number of used antennas in FD-GSM systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1557-1564
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• 2014

In this paper, we propose a signal detection technique based on the parallel orthogonal matching pursuit (POMP) is proposed for generalized shift space keying (GSSK) systems, which is a modified version of the orthogonal matching pursuit (OMP) that is widely used as a greedy algorithm for sparse signal recovery. The signal recovery problem in the GSSK systems is similar to that in the compressed sensing (CS). In the proposed POMP technique, multiple indexes which have the maximum correlation between the received signal and the channel matrix are selected at the first iteration, while a single index is selected in the OMP algorithm. Finally, the index yielding the minimum residual between the received signal and the M recovered signals is selected as an estimate of the original transmitted signal. POMP with Quantization (POMP-Q) is also proposed, which combines the POMP technique with the signal quantization at each iteration. The proposed POMP technique induces the computational complexity M times, compared with the OMP, but the performance of the signal recovery significantly outperform the conventional OMP algorithm.